Method and system for predicting time consumption of peak pushing link in disassembly operation

Abstract

The invention relates to the technical field of railway marshalling station hump operation, in particular to a method and a system for predicting time consumption of a hump pushing link in disintegration operation, and the method comprises the steps: obtaining the position information of a dispatching machine in the hump pushing link of a marshalling station; and based on the position information, in combination with the train set composition information, the team personnel composition information, the operation time period and the environmental factors at the current moment in the marshalling station, obtaining the predicted time consumption of the marshalling station peak pushing link. Through Beidou positioning, a GIS model and multi-source data fusion, accurate prediction of time consumption of a peak pushing link is realized, so that the working efficiency of a marshalling station is improved, manual operation and errors are reduced, the labor cost is also reduced, and the operation time of machine debugging can be shortened while the prediction accuracy is improved.

Description

Background Art

[0002] In the daily operation of railway marshaling yards, hump operations, as a key link in disassembly and marshaling, directly affect the overall operational efficiency of marshaling yards. The traditional method for determining the time consumption of the push-up link mainly relies on manual operation, including manual tracking of trains to record time and subsequent calculation. This method not only requires a large amount of human resources, but also has a long cycle from tracking of trains to data processing to time-consuming calculation, low efficiency and high operating costs. In addition, manual operation is prone to errors, data accuracy is difficult to guarantee, and due to poor timeliness, it is impossible to provide an effective basis for operation adjustment in a timely manner, limiting the flexibility and response speed of marshaling yard operations. On the other hand, although the existing electrical system can make time-consuming inferences through parameters such as light bands, locomotive speeds, and axle recording, its calculation accuracy is low and can only provide rough estimates. This method cannot be dynamically adjusted according to the actual train composition and complex marshaling plans, and it is difficult to meet the needs of modern railway marshaling yards for efficient and accurate scheduling of hump operations, which not only restricts the improvement of the overall efficiency of marshaling yards, but also affects the overall efficiency of the railway transportation system.

[0003] Therefore, it is urgent to provide a technical solution to solve the above problems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for predicting the time consumption of the peak pushing link in the dismantling operation.

[0005] In the first aspect, the present invention provides a method for predicting the time consumption of the peak pushing link in the dismantling operation, and the technical scheme of the method is as follows:

[0006] Obtain the location information of the shunting machine in the peak pushing link of the marshaling yard;

[0007] Based on the location information, combined with the train composition information, team composition information, operation time period and environmental factors in the marshalling yard at the current moment, the predicted time consumption of the peak pushing link in the marshalling yard is obtained.

[0008] The beneficial effects of the method for predicting the time consumption of the peak pushing link in the dismantling operation of the present invention are as follows:

[0009] The method of the present invention realizes accurate prediction of the time consumption of the peak pushing link through Beidou positioning, GIS model and multi-source data fusion, thereby improving the operating efficiency of the marshalling yard, reducing manual operations and errors, and reducing labor costs. It can improve the prediction accuracy while shortening the operation time of the machine adjustment.

[0010] On the basis of the above scheme, the method for predicting the time consumption of the peak pushing link in the dismantling operation of the present invention can also be improved as follows.

[0011] In an optional manner, the step of obtaining the position information of the shunting locomotive in the peak pushing link of the marshaling yard includes:

[0012] The location information is obtained using Beidou positioning technology and GIS model.

[0013] In the above optional methods, by using Beidou positioning technology and GIS model to obtain the location information of the dispatching machine, high-precision positioning can be achieved, thereby improving the accuracy of the time consumption prediction of the peak-pushing link.

[0014] In an optional manner, the location information includes: an initial location, a current location, and an end location.

[0015] In the above optional manner, by clarifying the composition of the position information, the entire operation trajectory of the machine adjustment can be fully considered when making a time consumption prediction, thereby improving the comprehensiveness and reliability of the time consumption prediction.

[0016] In an optional manner, the method further includes:

[0017] Determining the train composition information by at least one automatic recognition technology of RFID recognition and image recognition;

[0018] Determine the composition information of the team members by calling the length of service information, age information, position information, current task allocation information, health status information and training record information of the personnel management terminal and using the Beidou positioning technology;

[0019] Determine the operation period by calling the shift time information, working day information and peak period information of the time management terminal in the marshalling yard;

[0020] The environmental factors are determined by calling the weather condition information, temperature and humidity information, signal status information and track occupancy information of the environment monitoring terminal, signal control terminal and track occupancy detection terminal in the marshalling yard.

[0021] In the above optional methods, through RFID identification, image recognition, personnel management terminals, Beidou positioning technology and other multi-source data acquisition technologies, the train composition information, team composition information, operation time periods and environmental factors can be automatically and accurately obtained, thereby reducing manual operations and errors and improving the real-time and accuracy of the data.

[0022] In an optional manner, based on the location information, combined with the train composition information, crew composition information, operation time period and environmental factors in the marshalling yard at the current moment, the step of obtaining the predicted time consumption of the peak pushing link of the marshalling yard includes:

[0023] According to the location information, the train composition information, the team composition information, the operation period and the environmental factors, an initial feature vector is constructed by using data cleaning technology, normalization technology and principal component analysis technology, and the initial feature vector is encoded and converted to obtain a target feature vector;

[0024] Calculating the relative position relationship between the location information and a preset marker using the GIS model according to the target feature vector;

[0025] Extract the characteristic parameters used for time consumption calculation in the target characteristic vector and the relative position relationship, calculate the time consumption prediction value at the current moment according to the characteristic parameters, and determine the time consumption prediction value as the predicted time consumption.

[0026] In the above optional method, by using the GIS model to calculate the relative position relationship between the position information and the preset markers, the running path and environment of the train can be accurately simulated and analyzed, thereby improving the accuracy and real-time performance of the time consumption prediction. By extracting the characteristic parameters used for time consumption calculation in the target feature vector and the relative position relationship, and calculating the time consumption prediction value at the current moment based on the characteristic parameters, accurate time consumption prediction can be achieved, thereby optimizing the train operation time and improving the operating efficiency of the marshaling yard.

[0027] In a second aspect, the present invention provides a time-consuming prediction system for the peak-pushing link in the dismantling operation, and the technical solution of the system is as follows:

[0028] The time-consuming prediction system for the peak-pushing link in the disassembly operation includes: an acquisition module and a prediction module;

[0029] The acquisition module is used to: acquire the position information of the shunting machine in the peak pushing link of the marshalling station;

[0030] The prediction module is used to obtain the predicted time consumption of the peak pushing link of the marshalling yard based on the position information, combined with the train composition information, team composition information, operation time period and environmental factors in the marshalling yard at the current moment.

[0031] The beneficial effects of the time-consuming prediction system for the peak-pushing link in the dismantling operation of the present invention are as follows:

[0032] The system of the present invention realizes accurate prediction of the time consumption of the peak-pushing link through Beidou positioning, GIS model and multi-source data fusion, thereby improving the operating efficiency of the marshalling yard, reducing manual operations and errors, and reducing labor costs. It can improve the prediction accuracy while shortening the operation time of locomotive adjustment.

[0033] On the basis of the above scheme, the time consumption prediction system for the peak pushing link in the dismantling operation of the present invention can also be improved as follows.

[0034] In an optional manner, the acquisition module is specifically used to:

[0035] The location information is obtained using Beidou positioning technology and GIS model.

[0036] In the above optional methods, by using Beidou positioning technology and GIS model to obtain the location information of the dispatching machine, high-precision positioning can be achieved, thereby improving the accuracy of the time consumption prediction of the peak-pushing link.

[0037] In an optional manner, the location information includes: an initial location, a current location, and an end location.

[0038] In the above optional manner, by clarifying the composition of the position information, the entire operation trajectory of the machine adjustment can be fully considered when making a time consumption prediction, thereby improving the comprehensiveness and reliability of the time consumption prediction.

[0039] In a third aspect, a technical solution of an electronic device of the present invention is as follows:

[0040] It includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the method for predicting the time consumption of the peak pushing link in the disassembly operation of the present invention are implemented.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0042] Instructions are stored in the computer-readable storage medium. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the method for predicting the time consumption of the peak pushing link in the disassembly operation of the present invention.

[0043] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0045] Figure 1 It is a flow chart of an embodiment of a method for predicting the time consumption of a peak pushing link in a dismantling operation of the present invention;

[0046] Figure 2 It is a structural schematic diagram of an embodiment of a system for predicting the time consumption of a peak-pushing link in a dismantling operation of the present invention;

[0047] Figure 3 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0048] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0049] Figure 1 The flowchart of an embodiment of a method for predicting the time consumption of the peak pushing link in a disassembly operation provided by the present invention is shown, and the method is executed by a controller. Figure 1 As shown, the following steps are included:

[0050] S1. Obtain the position information of the shunting machine in the peak pushing link of the marshaling yard. In S1:

[0051] 1) The marshaling yard is a place in the railway transportation system for re-marshaling trains. Trains are disassembled and reassembled at the marshaling yard to optimize transportation efficiency. The marshaling yard is also the place where the disassembly and peak pushing operations occur, providing the environment and infrastructure for the disassembly operations.

[0052] 2) The peak pushing link is the initial part of the marshaling yard dismantling operation. The peak pushing link refers to the shunting locomotive (shunting locomotive) pushing the train or train to the peak in the marshaling yard (usually a high point in the station) to prepare for the subsequent shunting operation. The peak pushing link is a key step in the dismantling operation of the marshaling yard and affects the operating efficiency of the entire marshaling yard.

[0053] 3) The shunting locomotive is used to move trains or trains within the marshaling yard. The task of the shunting locomotive is to push the train to the peak. The location and movement path of the shunting locomotive are important factors in predicting the time consumption of the peak pushing link.

[0054] 4) The location information includes the spatial location data of the shunting locomotive in the marshalling yard. The location information is the basic data for time consumption prediction, which is obtained through Beidou positioning technology and GIS model, including the initial location, current location and end location.

[0055] Specifically, the controller obtains the position information of the shunting locomotive in the peak-pushing link of the marshalling yard.

[0056] S2. Based on the position information, combined with the train composition information, team composition information, operation time period and environmental factors in the marshalling yard at the current moment, the predicted time consumption of the peak pushing link of the marshalling yard is obtained. In S2:

[0057] 1) Train composition information describes the arrangement and composition of each carriage in the train. Train composition information is obtained through RFID identification, image recognition technology or docking with other system data, which affects the operational complexity and time consumption of the peak-pushing link.

[0058] 2) Team composition information includes length of service, age, position, task assignment, health status and training records of marshaling yard operators. Personnel composition information affects operating efficiency and safety and is determined through the personnel management terminal. At the same time, Beidou positioning technology can also determine the current location of operators in the marshaling yard.

[0059] 3) Operation period is the working time arrangement of the marshalling yard, including shift time and peak period. The operation period affects the time consumption prediction of the peak period, which is obtained through the time management terminal.

[0060] 4) Environmental factors include weather conditions, temperature and humidity, signal status, and track occupancy. Environmental factors can affect the safety and efficiency of the peak-pushing process and are obtained through environmental monitoring terminals, signal control terminals, and track occupancy detection terminals.

[0061] 5) The predicted time consumption is the estimated time consumption of the peak-pushing link calculated based on a variety of data and information. The predicted time consumption is used to optimize the operation plan of the marshalling yard and improve efficiency. It is calculated through data analysis and GIS model.

[0062] Specifically, the controller further obtains the predicted time consumption of the peak pushing link of the marshalling yard based on the position information, combined with the train composition information, team composition information, operation time period and environmental factors in the marshalling yard at the current moment.

[0063] The technical solution of this embodiment achieves accurate prediction of the time consumption of the peak-pushing link through Beidou positioning, GIS model and multi-source data fusion, thereby improving the operating efficiency of the marshalling yard, reducing manual operations and errors, and reducing labor costs. It can improve the accuracy of predictions while shortening the operation time of locomotive adjustment.

[0064] In an optional manner, the step of obtaining the position information of the shunting locomotive in the peak pushing link of the marshaling yard includes:

[0065] The location information is obtained using Beidou positioning technology and GIS model.

[0066] It should be noted that through the high-precision Beidou positioning technology, the position dynamics of the dispatching machine in the operating area can be tracked in real time, and the positioning accuracy can reach the centimeter level standard, ensuring the accuracy and reliability of the data, and providing key position basis for subsequent accurate time-consuming predictions, so as to overcome the inaccuracy and lag of traditional positioning methods, and meet the requirements of high-precision and real-time location information in the peak-pushing link, thereby effectively improving the accuracy and timeliness of the pre-built peak-pushing prediction model.

[0067] The GIS model can have a real-time update function to cope with changes or new facilities in the working area of ​​the peak-pushing link, ensure the accuracy and timeliness of the machine position calculation, provide accurate and stable location data support for the peak-pushing time consumption prediction model, and optimize the model's analysis and time consumption estimation capabilities for the machine operation status. The GIS model contains one or more reference points, and the position of the reference points is accurate and fixed. In the process of using the GIS model for position calculation to determine the location information of the machine and other related calculations, coordinate calibration and auxiliary calculations are performed with reference to the reference points. Through accurate reference points, the position calculation deviation caused by measurement errors, equipment deviations and other factors can be effectively reduced, and the accuracy of the position calculation can be significantly improved, thereby improving the accuracy of the time consumption prediction results of the entire peak-pushing link. It can more accurately reflect the actual situation of the peak-pushing link and provide more reliable data support for efficient and safe job scheduling.

[0068] In this embodiment, artificial intelligence technology is used to build a peak prediction model. First, the information such as the location information of the dispatching machine, the train composition information, the team composition information, the operation period and environmental factors obtained by Beidou positioning technology is input into the peak prediction model as basic data. Then, the GIS model is used to calculate the position, determine the precise location information of the dispatching machine in the working area of ​​the peak link, and input the precise location information into the peak prediction model.

[0069] Specifically, the controller further utilizes Beidou positioning technology and GIS model to obtain the location information.

[0070] In an optional manner, the method further includes:

[0071] Determining the train composition information by at least one automatic recognition technology of RFID recognition and image recognition;

[0072] Determine the composition information of the team members by calling the length of service information, age information, position information, current task allocation information, health status information and training record information of the personnel management terminal and using the Beidou positioning technology;

[0073] Determine the operation time period by calling the shift time information and peak time period information of the time management terminal in the marshalling yard;

[0074] The environmental factors are determined by calling the weather condition information, temperature and humidity information, signal status information and track occupancy information of the environment monitoring terminal, signal control terminal and track occupancy detection terminal in the marshalling yard.

[0075] It should be noted that the train composition information includes: no-peak trains, no-slip trains, dangerous goods, empty trains, heavy trains and other different types of trains, and the distribution and quantity of different types of trains are input into the peak-pushing prediction model. In the process of building the peak-pushing prediction model, a database is established to associate the train composition with the difficulty and time consumption of the peak-pushing operation. According to the influence of the composition characteristics of different trains on the pushing speed, disassembly sequence, and operation safety requirements in the peak-pushing link, the comprehensive effect of the composition characteristics of different trains on the peak-pushing time consumption is analyzed, so that the peak-pushing prediction model can dynamically adjust the time consumption prediction value according to the actual composition of the train, improve the adaptability and accuracy of the prediction model to different train formations, and provide more targeted decision-making basis for the dispatching of the peak-pushing link.

[0076] The team member composition information, such as length of service information, age information, job information, current task assignment information, health status information and training record information, is input into the peak pushing prediction model. By establishing the correlation between personnel composition and work efficiency, degree of operational standardization, etc., the advantages and disadvantages of different personnel compositions in the peak pushing operation are analyzed, and the impact of different personnel compositions on the speed, accuracy and safety of the peak pushing link is considered. By converting the team member composition information into the influencing parameters in the model, the peak pushing prediction model can estimate the time consumption of the peak pushing operation based on the actual situation of the team members, provide reference for personnel scheduling and work arrangement, optimize personnel allocation, improve the overall efficiency of the peak pushing link, and also make the model prediction results more in line with the actual operation scenario.

[0077] The operating time periods and operating environment factors, such as shift time information and peak time information in different time periods, traffic density information, weather conditions information, temperature and humidity information, signal status information and track occupancy information, are input into the peak pushing prediction model. An influence coefficient database is established for the influence of operating time periods and environmental factors on peak pushing operations. For example, the waiting time in the peak pushing link increases due to heavy traffic during peak hours, and the track conditions are affected by bad weather, thereby affecting the pushing speed. The peak pushing prediction model is based on the operating time period and environmental data, and comprehensively calculates the time consumption of the peak pushing link, so that the prediction result can fully consider the external influencing factors in the actual operation, plan in advance and adjust the scheduling plan in time, improve the adaptability and efficiency of the peak pushing link under different environmental conditions, and ensure the stability and efficiency of the overall operation of the marshalling yard.

[0078] In this embodiment, RFID tags can be installed at key locations of each carriage, and the RFID tags store unique identification information of the corresponding carriage (such as carriage number, type, load capacity, etc.). RFID readers are deployed at preset locations of the marshalling yard to ensure that all passing carriages can be covered. When the carriage passes through the RFID reader, the reader automatically reads the RFID tag information of each carriage, and transmits the read data to the central control terminal to parse and verify the data and generate the train composition information. In addition, the carriage identification information can also be read through the inherent tags of the carriage itself (such as bar codes, QR codes, NFC tags or other electronic tags), and then the train composition information is generated. In addition to obtaining information through physical tags, relevant data can also be obtained from other external terminals through API interfaces to further supplement or verify the train composition information, and finally generate the train composition information.

[0079] Through the API interface or direct access to the database of the personnel management terminal, the length of service information, age information, position information, current task assignment information, health status information and training record information of the personnel are obtained, and the obtained data are integrated. At the same time, each team member is equipped with a Beidou positioning device to ensure that the Beidou positioning device works normally and can transmit location information in real time. By collecting the personnel location of the team members in real time, it is ensured that the personnel location within the marshalling yard can be tracked, and the obtained personnel location is combined with the information of the personnel management terminal to analyze the distribution of personnel at the work site and the execution of tasks, thereby forming a complete team member composition information.

[0080] Obtain the marshalling yard's shift time information and peak period information through the API interface or by directly accessing the database of the time management terminal. By parsing the acquired data, match the current time, determine the applicable operating time period, and notify the relevant teams and personnel to carry out operation scheduling based on the determined operating time period.

[0081] Obtain weather information, temperature and humidity information, signal status information, and track occupancy information through API or direct access to the database of the environmental monitoring terminal, signal control terminal, and track occupancy detection terminal. Integrate and analyze the acquired data to determine the current environmental factors.

[0082] Specifically, the controller further determines the train composition information through RFID identification, image recognition technology or data obtained through other systems; determines the team composition information by calling the length of service, age, position, current task allocation, health status and training record information from the personnel management terminal and using Beidou positioning technology; determines the working period by calling the shift time, working day and peak period information from the time management terminal; determines the environmental factors by calling the weather conditions, temperature and humidity, signal status and track occupancy information from the environmental monitoring terminal, signal control terminal and track occupancy detection terminal.

[0083] In an optional manner, based on the location information, combined with the train composition information, team member composition information, operation time period and environmental factors in the marshalling yard at the current moment, the step of obtaining the predicted time consumption of the peak pushing link of the marshalling yard includes:

[0084] According to the location information, the train composition information, the team composition information, the operation period and the environmental factors, an initial feature vector is constructed by using data cleaning technology, normalization technology and principal component analysis technology, and the initial feature vector is encoded and converted to obtain a target feature vector;

[0085] Calculating the relative position relationship between the location information and a preset marker using the GIS model according to the target feature vector;

[0086] Extract the characteristic parameters used for time consumption calculation in the target characteristic vector and the relative position relationship, calculate the time consumption prediction value at the current moment according to the characteristic parameters, and determine the time consumption prediction value as the predicted time consumption.

[0087] It should be noted that based on the detailed geographic information data of the working area of ​​the peak-pushing link in the marshalling yard, a geographic information model is constructed, and the Beidou positioning data of the shunting locomotive is accurately matched and calculated with the geographic information data to accurately determine the positional relationship of the shunting locomotive relative to each track and fixed production factors (such as signal machines and switches, etc.). Among them, fixed production factors such as signal machines and switches in the marshalling yard are used as key markers, and the key markers are uniquely coded and identified, and the key marker information and the associated data between the key markers and the peak-pushing operation process are input into the peak-pushing prediction model. When key markers are involved in the peak-pushing operation process, the peak-pushing prediction model can quickly identify and accurately determine the operation stage and position node based on the coding identification of the key markers. By combining the relative position relationship between the key markers and the shunting locomotive and the train and the pre-established associated database, the time consumption of the peak-pushing operation can be accurately estimated, the prediction model's control ability and time consumption prediction accuracy for the key links of the peak-pushing operation can be improved, and the adaptability and analysis ability of the peak-pushing prediction model for complex peak-pushing operation processes can be enhanced. In the calculation of the GIS model, the position of the key marker can be used as a reference point for the position calculation of the GIS model.

[0088] Based on the division of operating areas within the station, the track positioning and high-precision dynamic and precise positioning data of the shunting machine and personnel are integrated into the peak-pushing prediction model. The operating area is divided according to the function of the hump operating area, the track layout and other factors, and accurate reference coordinates and accuracy standards are formulated for track positioning. For the dynamic positioning of the shunting machine and personnel, the movement speed, direction and acceleration of the shunting machine and personnel are comprehensively considered. The multi-sensor fusion technology is used to optimize the positioning data, and the optimized positioning data is input into the peak-pushing prediction model, so that the peak-pushing prediction model can adjust the peak-pushing time consumption prediction results in real time according to the precise position relationship and dynamic changes of fixed production factors such as signals and switches in the operating area, as well as variable production factors such as shunting machines and personnel, so as to improve the responsiveness and prediction accuracy of the peak-pushing prediction model to the actual situation of the operating area.

[0089] The positional relationship and relative position of all production factors in the marshaling yard are calculated in real time, and the calculation results are input into the peak-pushing prediction model as important parameters. At the same time, the data processing algorithm and computing architecture are used to quickly process the position data of multiple production factors in the operating area, and accurately calculate the distance, angle, topological relationship and other information between each production factor. Combined with historical operation data and preset model rules, the time consumption of the peak-pushing link under different train marshaling and operating conditions is predicted.

[0090] In this embodiment, all data including location information, train composition information, team composition information, operation time period and environmental factors are cleaned to remove noise data and outliers to ensure data accuracy and consistency, and the cleaned data is normalized to convert data of different scales and dimensions to the same range for subsequent analysis and calculation. Then, the principal component analysis technology is used to reduce the dimension of the normalized data, extract the main features, construct the initial feature vector, and encode and transform the initial feature vector to obtain the target feature vector, ensuring that the feature vector can effectively represent each production factor and influencing factor.

[0091] According to the target feature vector, the GIS model is used to calculate the relative position relationship between the location information of each production factor and the preset marker, including distance, angle, topological relationship, etc. The key feature parameters for time consumption calculation are extracted from the target feature vector and the relative position relationship, including train composition, team personnel configuration, operation period, environmental factors, position relationship, etc. According to the extracted feature parameters, the time consumption prediction value at the current moment is calculated using mathematical models and algorithms (such as machine learning models or statistical analysis methods), and the calculated time consumption prediction value is determined as the final predicted time consumption to guide actual operations and scheduling decisions.

[0092] It should be noted that in the process of data processing and calculation, fault tolerance and error correction mechanisms are introduced to ensure that stable and accurate computing capabilities can be maintained when facing data anomalies or equipment failures, so as to continuously optimize data processing algorithms, feature extraction methods and prediction models based on actual operating data and feedback, and improve prediction accuracy and responsiveness.

[0093] Specifically, the controller further constructs the target feature vector based on the location information, train composition information, team member information, operation period and environmental factors using data cleaning, normalization and principal component analysis techniques, and calculates the relative position relationship through the GIS model. Subsequently, the relevant feature parameters are extracted, the current time prediction value is calculated, and it is determined as the final predicted time.

[0094] Figure 2 FIG. 2 is a schematic diagram showing a structural embodiment of a system 200 for predicting the time consumption of a peak-pushing link in a disassembly operation provided by the present invention. Figure 2 As shown, the system 200 includes: an acquisition module 210 and a prediction module 220;

[0095] The acquisition module 210 is used to: acquire the position information of the shunting machine in the peak pushing link of the marshalling yard;

[0096] The prediction module 220 is used to obtain the predicted time consumption of the peak pushing link of the marshalling yard based on the position information, combined with the train composition information, team composition information, operation time period and environmental factors in the marshalling yard at the current moment.

[0097] In an optional manner, the acquisition module is specifically used to:

[0098] The location information is obtained using Beidou positioning technology and GIS model.

[0099] In an optional manner, the location information includes: an initial location, a current location, and an end location.

[0100] The technical solution of this embodiment achieves accurate prediction of the time consumption of the peak-pushing link through Beidou positioning, GIS model and multi-source data fusion, thereby improving the operating efficiency of the marshalling yard, reducing manual operations and errors, and reducing labor costs. It can improve the accuracy of predictions while shortening the operation time of locomotive adjustment.

[0101] The above-mentioned parameters and steps for each module to implement corresponding functions in the system 200 for predicting the time consumption of the peak-pushing link in the dismantling operation of this embodiment can refer to the parameters and steps in the embodiment of the method for predicting the time consumption of the peak-pushing link in the dismantling operation mentioned above, and will not be repeated here.

[0102] like Figure 3As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above-mentioned methods for predicting the time consumption of the peak pushing link in the disassembly operation, specifically:

[0103] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements any of the methods for predicting the time consumption of the peak-pushing link in the disassembly operation provided in the above-mentioned embodiments. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for realizing the functions of the device, which will not be described in detail here.

[0104] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above-mentioned methods for predicting the time consumption of the peak pushing link in the dismantling operation.

[0105] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0106] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes any of the above-mentioned methods for predicting the time consumption of the peak-pushing link in the disassembly operation.

[0107] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0108] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0109] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0110] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for predicting the time consumption of the peak pushing link in the dismantling operation, characterized in that: include: Obtain the location information of the shunting machine in the peak pushing link of the marshaling yard; Based on the location information, combined with the train composition information, team composition information, operation time period and environmental factors in the marshalling yard at the current moment, the predicted time consumption of the peak pushing link in the marshalling yard is obtained.

2. The method for predicting the time consumption of the peak pushing link in the dismantling operation according to claim 1 is characterized in that: The steps of obtaining the position information of the shunting machine in the peak pushing link of the marshaling yard include: The location information is obtained using Beidou positioning technology and GIS model.

3. The method for predicting the time consumption of the peak pushing link in the dismantling operation according to claim 1 is characterized in that: The location information includes: initial location, current location and end location.

4. The method for predicting the time consumption of the peak pushing link in the dismantling operation according to claim 1 is characterized in that: Also includes: Determining the train composition information by at least one automatic recognition technology of RFID recognition and image recognition; Determine the composition information of the team members by calling the length of service information, age information, position information, current task allocation information, health status information and training record information of the personnel management terminal and using the Beidou positioning technology; Determine the operation period by calling the shift time information, working day information and peak period information of the time management terminal in the marshalling yard; The environmental factors are determined by calling the weather condition information, temperature and humidity information, signal status information and track occupancy information of the environment monitoring terminal, signal control terminal and track occupancy detection terminal in the marshalling yard.

5. The method for predicting the time consumption of the peak pushing link in the dismantling operation according to claim 1 is characterized in that: The step of obtaining the predicted time consumption of the peak pushing link of the marshalling yard based on the position information, combined with the train composition information, team composition information, operation time period and environmental factors at the current moment in the marshalling yard includes: According to the location information, the train composition information, the team composition information, the operation period and the environmental factors, an initial feature vector is constructed by using data cleaning technology, normalization technology and principal component analysis technology, and the initial feature vector is encoded and converted to obtain a target feature vector; Calculating the relative position relationship between the location information and a preset marker using the GIS model according to the target feature vector; Extract the characteristic parameters used for time consumption calculation in the target characteristic vector and the relative position relationship, calculate the time consumption prediction value at the current moment according to the characteristic parameters, and determine the time consumption prediction value as the predicted time consumption.

6. A time-consuming prediction system for the peak-pushing link in the dismantling operation, characterized in that: include: Acquisition module and prediction module; The acquisition module is used to: acquire the position information of the shunting machine in the peak pushing link of the marshalling station; The prediction module is used to obtain the predicted time consumption of the peak pushing link of the marshalling yard based on the position information, combined with the train composition information, team composition information, operation time period and environmental factors in the marshalling yard at the current moment.

7. The time consumption prediction system for the peak pushing link in the dismantling operation according to claim 6 is characterized in that: The acquisition module is specifically used for: The location information is obtained using Beidou positioning technology and GIS model.

8. The time consumption prediction system for the peak pushing link in the dismantling operation according to claim 6 is characterized in that: The location information includes: initial location, current location and end location.

9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device can implement the method for predicting the time consumption of the peak pushing link in the dismantling operation as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor so that the computer-readable storage medium implements the method for predicting the time consumption of the peak pushing link in the dismantling operation as described in any one of claims 1 to 5.

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