A rail transit operation and maintenance method, system, electronic device and storage medium based on BIM

Abstract

The present invention discloses a rail transit operation and maintenance method, system, electronic device and storage medium based on BIM, which belongs to the field of rail transit operation and maintenance. The rail transit operation and maintenance method based on BIM analyzes historical rail transit operation and maintenance data, comprehensively considers the impact of rainfall factors on weekday passenger flow, adopts an improved LSTM method, fuses multiple weak learning predictors to determine a strong learning predictor, corrects the daily flow through a dynamic reduction coefficient, improves the accuracy of the daily flow prediction value, and performs visual flow warning by establishing a BIM operation and maintenance model.

Description

Technical Field

[0001] The present invention relates to the field of rail transit operation and maintenance, and in particular to a BIM-based rail transit operation and maintenance method, system, electronic equipment and storage medium. Background Art

[0002] Rail transit is an important mode of transportation in my country and is of great significance to the sustainable development of cities. Due to commuting reasons, the passenger flow on weekdays is generally higher than that on non-working days. For weekdays with extremely high passenger flow, it is generally necessary to initiate passenger flow control measures or add temporary trains in a timely manner.

[0003] Therefore, how to accurately predict short-term passenger flow through rail transit operation and maintenance related data has become a technical problem that needs to be urgently solved in subway travel. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies in the prior art and provide a BIM-based rail transit operation and maintenance method, system, electronic device and storage medium, by improving the LSTM method, fusing multiple weak learning predictors to determine a strong learning predictor, and determining the daily flow prediction value.

[0005] To this end, the present application provides a rail transit operation and maintenance method based on BIM, comprising:

[0006] Obtain rail transit operation and maintenance data as sample data;

[0007] Assuming that the sample data have the same weight, an improved LSTM method is used to fuse multiple weak learning predictors to determine a strong learning predictor;

[0008] Determine the dynamic reduction factor based on the duration of rainfall in rainy weather;

[0009] Determine the daily flow forecast value of each rail transit station based on the strong learning predictor and dynamic reduction coefficient;

[0010] A BIM operation and maintenance model is established based on the distribution of rail transit stations and the daily traffic forecast values ​​of each rail transit station to provide visual traffic warning.

[0011] In some specific implementations, assuming that the sample data have the same weight, an improved LSTM method is used to fuse multiple weak learning predictors to determine a strong learning predictor, specifically including

[0012] The maximum training error of the nth weak learning predictor is Where: Train predictions for the weak learner predictor, is the observed value for training;

[0013] The relative error of the sample data is Where: For the The weakly learned predictor The relative error of the sample data; The error rate of the LSTM weak learning predictor is

[0014] Where: For the The error rate of the weak predictor, For the The weak predictor The weight of the sample data; The weight coefficient of the LSTM weak learning predictor is For Weak learning predictors are updated with weights; The strong learning predictor fused from multiple weak learning predictors is Where: for of the median.

[0015] In some specific implementations, the dynamic reduction coefficient is determined based on the rainfall duration of the rainy weather, specifically:

[0016] If there is no rainfall, the dynamic reduction factor is 1;

[0017] If there is rainfall, the dynamic reduction factor is: Where: i is the sample number of the training set; n is the total number of samples in the training set; is a dynamic reduction coefficient, ranging from 0 to 1, t is the duration of rainfall in the forecast day, and T is the total operation time of the rail transit station on the forecast day.

[0018] In some specific implementations, the daily flow forecast value of each rail transit station is determined based on the strong learning predictor and the dynamic reduction coefficient, specifically including:

[0019] Obtain the date to be predicted, and use the strong learning predictor to predict and obtain the expected value of the date to be predicted;

[0020] The expected value of daily flow is corrected according to the reduction factor, and the product of the two is used as the predicted value of the date to be predicted.

[0021] This application also provides a BIM-based rail transit operation and maintenance system, including

[0022] An acquisition unit, used for acquiring rail transit operation and maintenance data as sample data;

[0023] The fusion unit is used to assume that the sample data have the same weight, adopt the improved LSTM method, and fuse multiple weak learning predictors to determine the strong learning predictor;

[0024] A reduction unit, used for determining a dynamic reduction coefficient based on the rainfall duration of rainy weather;

[0025] A prediction unit, used to determine the daily flow prediction value of each rail transit station based on a strong learning predictor and a dynamic reduction coefficient;

[0026] The early warning unit is used to establish a BIM operation and maintenance model based on the distribution of rail transit stations and the daily traffic forecast values ​​of each rail transit station, and to perform visual traffic early warning.

[0027] In some specific implementations, assuming that the sample data have the same weight, an improved LSTM method is used to fuse multiple weak learning predictors to determine a strong learning predictor, specifically including

[0028] The maximum training error of the nth weak learning predictor is Where: Train predictions for the weak learner predictor, is the observed value for training;

[0029] The relative error of the sample data is Where: For the The weakly learned predictor The relative error of the sample data; The error rate of the LSTM weak learning predictor is Where: For the The error rate of the weak predictor, For the The weak predictor The weight of the sample data; The weight coefficient of the LSTM weak learning predictor is For Weak learning predictors are updated with weights; The strong learning predictor fused from multiple weak learning predictors is Where: for In some specific implementations, the dynamic reduction coefficient is determined based on the duration of rainfall in rainy weather, specifically: if there is no rainfall, the dynamic reduction coefficient is 1; if there is rainfall, the dynamic reduction coefficient is: Where: i is the sample number of the training set; n is the total number of samples in the training set; is a dynamic reduction coefficient, ranging from 0 to 1, t is the duration of rainfall in the forecast day, and T is the total operation time of the rail transit station on the forecast day.

[0030] In some specific implementations, the daily flow forecast value of each rail transit station is determined based on the strong learning predictor and the dynamic reduction coefficient, specifically including:

[0031] Obtain the date to be predicted, and use the strong learning predictor to predict and obtain the expected value of the date to be predicted;

[0032] The expected value of daily flow is corrected according to the reduction factor, and the product of the two is used as the predicted value of the date to be predicted.

[0033] The present application further provides an electronic device, comprising: one or more processors; a storage system for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0034] The present application further provides a storage medium on which a computer program is stored, wherein the program implements the above method when executed by a processor.

[0035] The BIM-based rail transit operation and maintenance method provided in this application obtains rail transit operation and maintenance data as sample data; adopts an improved LSTM method, fuses multiple weak learning predictors to determine a strong learning predictor, determines a dynamic reduction coefficient based on the duration of rainfall in rainy weather, determines the daily flow forecast value of each rail transit station based on the strong learning predictor and the dynamic reduction coefficient, establishes a BIM operation and maintenance model based on the distribution of rail transit stations and the daily flow forecast value of each rail transit station, and performs visual flow warning. This method analyzes historical rail transit operation and maintenance data, comprehensively considers the impact of rainfall factors on weekday passenger flow, corrects daily flow through dynamic reduction coefficients, improves the accuracy of daily flow forecast values, and performs visual flow warning by establishing a BIM operation and maintenance model. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flow chart of a BIM-based rail transit operation and maintenance method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0039] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0040] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0041] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0042] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0043] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0044] Please refer to Figure 1 , which shows the process of an embodiment of a rail transit operation and maintenance method based on BIM according to the present disclosure. The rail transit operation and maintenance method based on BIM is mainly used for station flow prediction on weekdays.

[0045] like Figure 1 As shown in FIG. 1 , a rail transit operation and maintenance method based on BIM includes:

[0046] Step 100, obtaining rail transit operation and maintenance data as sample data.

[0047] Step 200, assuming that the sample data have the same weight, adopt an improved LSTM method to fuse multiple weak learning predictors to determine a strong learning predictor.

[0048] Here, specifically include:

[0049] The maximum training error of the nth weak learning predictor is / Where: Train predictions for the weak learner predictor, is the observed value for training;

[0050] The relative error of the sample data is Where: For the The weakly learned predictor The relative error of sample data;

[0051] No. The error rate of the LSTM weak learning predictor is Where: For the The error rate of the weak predictor, For the The weak predictor The weight of sample data;

[0052] No. The weight coefficient of the LSTM weak learning predictor is For Weak learning predictors are updated with weights; The strong learning predictor fused from multiple weak learning predictors is Where: for of the median.

[0053] Step 300: determining a dynamic reduction coefficient based on the duration of rainfall in rainy weather.

[0054] Here, specifically: if there is no rainfall, the dynamic reduction factor is 1;

[0055] If there is rainfall, the dynamic reduction factor is: Where: i is the sample number of the training set; n is the total number of samples in the training set; is a dynamic reduction coefficient, ranging from 0 to 1, t is the duration of rainfall in the forecast day, and T is the total operation time of the rail transit station on the forecast day.

[0056] Step 400, determining the daily flow forecast value of each rail transit station based on the strong learning predictor and the dynamic reduction coefficient.

[0057] Here, specifically include:

[0058] Obtain the date to be predicted, and use the strong learning predictor to predict and obtain the expected value of the date to be predicted;

[0059] The expected value of daily flow is corrected according to the reduction factor, and the product of the two is used as the predicted value of the date to be predicted.

[0060] Step 500: Establish a BIM operation and maintenance model based on the distribution of rail transit stations and the daily traffic forecast value of each rail transit station to perform visual traffic warning.

[0061] The BIM-based rail transit operation and maintenance method obtains rail transit operation and maintenance data as sample data; adopts an improved LSTM method, fuses multiple weak learning predictors to determine a strong learning predictor, determines a dynamic reduction coefficient based on the duration of rainfall in rainy weather, determines the daily flow forecast value of each rail transit station based on the strong learning predictor and the dynamic reduction coefficient, and establishes a BIM operation and maintenance model based on the distribution of rail transit stations and the daily flow forecast value of each rail transit station to perform visual flow warning. This method analyzes historical rail transit operation and maintenance data, comprehensively considers the impact of rainfall factors on weekday passenger flow, corrects daily flow through dynamic reduction coefficients, improves the accuracy of daily flow forecast values, and performs visual flow warnings by establishing a BIM operation and maintenance model.

[0062] This application also provides a BIM-based rail transit operation and maintenance system, including

[0063] An acquisition unit, used for acquiring rail transit operation and maintenance data as sample data;

[0064] The fusion unit is used to assume that the sample data have the same weight, adopt the improved LSTM method, and fuse multiple weak learning predictors to determine the strong learning predictor;

[0065] A reduction unit, used for determining a dynamic reduction coefficient based on the rainfall duration of rainy weather;

[0066] A prediction unit, used to determine the daily flow prediction value of each rail transit station based on a strong learning predictor and a dynamic reduction coefficient;

[0067] The early warning unit is used to establish a BIM operation and maintenance model based on the distribution of rail transit stations and the daily traffic forecast values ​​of each rail transit station, and to perform visual traffic early warning.

[0068] The present application further provides an electronic device, which may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0069] An electronic device may include a processing system (e.g., a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory or a program loaded from a storage system into a random access memory. In the memory, various programs and data required for the operation of the electronic device are also stored. The processing system, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0070] Typically, the following systems can be connected to the I / O interface: input systems including, for example, touch screens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output systems including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage systems including, for example, magnetic tapes, hard disks, etc.; and communication systems. The communication system can allow an electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0071] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication system, or installed from a storage system, or installed from a ROM. When the computer program is executed by a processing system, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0072] It should be noted that the storage medium of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: 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 of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. Computer readable signal media may also be any storage medium other than computer readable storage media that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, system or device. The program code contained on the storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0073] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0074] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0075] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0076] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0077] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A rail transit operation and maintenance method based on BIM, characterized in that: The method comprises: Obtain rail transit operation and maintenance data as sample data; Assuming that the sample data have the same weight, the improved LSTM method is used to fuse multiple weak learning predictors to determine the strong learning predictor, including: The maximum training error of the nth weak learning predictor is , where: Train predictions for the weak learner predictor, Observations for training the weakly learned predictor; is the sample number of the training set, which is represented by Sample data; The relative error of the sample data is , where: For the The weakly learned predictor The relative error of the sample data; The error rate of the LSTM weak learning predictor is , where: For the The error rate of the weakly learned predictor, For the The weakly learned predictor The weight of sample data Heavy; nth The weight coefficient of the LSTM weak learning predictor is , for Weak learning predictors are weighted new , the strong learning predictor model of multiple weak learning predictors is: , where: is the variable value of the ith sample data of the nth weak learning predictor, is the observation value of the weak learning predictor training of the ith sample data of the nth weak learning predictor, for The dynamic reduction factor is determined based on the rainfall duration of rainy weather, specifically: If there is no rainfall, the dynamic reduction factor is 1; If there is rainfall, the dynamic reduction factor is: Where: i is the sample sequence of the training set k is the dynamic reduction coefficient, ranging from 0 to 1, t is the duration of rainfall in the forecast day, and T is the rail transit time in the forecast day. Total site operation time, Train prediction values ​​for weak learner predictors; Determine the daily flow forecast value of each rail transit station based on the strong learning predictor and dynamic reduction coefficient; A BIM operation and maintenance model is established based on the distribution of rail transit stations and the daily traffic forecast of each rail transit station. Visual traffic warning.

2. The BIM-based rail transit operation and maintenance method according to claim 1 is characterized in that: Based on strong learning predictor The daily flow forecast value of each rail transit station is determined by the dynamic reduction coefficient, including: Obtain the date to be predicted, and use the strong learning predictor to predict the expected value of daily traffic on the date to be predicted; The expected value of daily flow is corrected according to the dynamic reduction coefficient, and the product of the two is used as the predicted value of the date to be predicted.

3. The BIM-based rail transit operation and maintenance system is characterized by: include An acquisition unit, used for acquiring rail transit operation and maintenance data as sample data; Fusion unit, used to assume that sample data has the same weight, using the improved LSTM method to fuse multiple weak learning predictions The strong learning predictor is determined by The maximum training error of the nth weak learning predictor is , where: Train predictions for the weak learner predictor, Observations for training the weakly learned predictor; is the sample number of the training set, which is represented by Sample data; The relative error of the sample data is , where: For the The weakly learned predictor The relative error of the sample data; The error rate of the LSTM weak learning predictor is , where: For the The error rate of the weakly learned predictor, For the The weakly learned predictor The weight of sample data Heavy; nth The weight coefficient of the LSTM weak learning predictor is , for Weak learning predictors are weighted new , the strong learning predictor model of multiple weak learning predictors is: ， Where: is the variable value of the ith sample data of the nth weak learning predictor, Prediction for the nth weak learner The observation value of the weak learning predictor training of the i-th sample data of the machine, for Median; based on rainfall The duration of rainfall in the weather determines the dynamic reduction coefficient, which is: If there is no rainfall, the dynamic reduction factor is 1; If there is rainfall, the dynamic reduction factor is: , where i is the sample sequence of the training set k is the dynamic reduction coefficient, ranging from 0 to 1, t is the duration of rainfall in the forecast day, and T is the rail transit time in the forecast day. Total site operation time, Train prediction values ​​for weak learner predictors; Prediction unit, used to determine the daily flow forecast of each rail transit station based on strong learning predictor and dynamic reduction coefficient value; The early warning unit is used to establish BIM operation based on the distribution of rail transit stations and the daily flow forecast values ​​of each rail transit station. dimensional model for visual traffic warning.

4. The BIM-based rail transit operation and maintenance system according to claim 3 is characterized in that: Based on strong learning predictor The daily flow forecast value of each rail transit station is determined by the dynamic reduction coefficient, including: Obtain the date to be predicted, and use the strong learning predictor to predict the expected value of daily traffic on the date to be predicted; The expected value of daily flow is corrected according to the dynamic reduction coefficient, and the product of the two is used as the predicted value of the date to be predicted.

5. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement A method as claimed in any one of claims 1 to 2.

6. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it is implemented as follows The method according to any one of claims 1 to 2.

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