Time synchronization method, rail transit management method and system based on digital twin

Abstract

The present invention discloses a time synchronization method based on digital twins, a rail transit management method and a system, and relates to the field of information control. The present invention includes receiving an information data packet; parsing out the timestamp, device number and information content included in the information data packet; constructing a digital twin model according to the timestamp, device number and information content parsed out in the information data packet, wherein the information content contained in one or more information data packets constructs an event unit in the digital twin model; screening out the same event unit in the digital twin model constructed by the information content contained in multiple information data packets as a multimodal cross event unit; obtaining multiple timestamps corresponding to the multimodal cross event unit; and obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to the multiple timestamps corresponding to the multimodal cross event unit. The present invention realizes accurate time synchronization of multiple devices.

Description

Technical Field

[0001] The present invention belongs to the field of information control technology, and in particular relates to a time synchronization method based on digital twins, and a rail transit management method and system. Background Art

[0002] With the rapid development of modern information technology, digital twin technology has gradually been widely used in various fields. Digital twin refers to the establishment of a digital model corresponding to the entity in the real world in the virtual world to achieve real-time monitoring, optimization and simulation of the entity, thereby improving the management efficiency and operational safety of the system. In the field of rail transit management, digital twin technology has important application value. It can help realize the real-time monitoring, fault diagnosis and prediction, and intelligent scheduling of rail transit equipment and systems.

[0003] However, the various devices and components in the rail transit management system usually have complex timing relationships and high requirements for time synchronization. How to achieve accurate time synchronization to ensure the efficient and coordinated operation of various parts of the system has become an urgent problem to be solved. Traditional time synchronization methods usually rely on hardware clocks and network communications, which are easily affected by factors such as hardware failures and network delays, and cannot meet the high requirements of the rail transit management system for time synchronization accuracy.

[0004] A time synchronization system and method based on rail transit is disclosed in the patent with publication number CN105812118A. The system includes an integrated monitoring system ISCS and an industrial computer. The integrated monitoring system is connected to the industrial computer via Ethernet. The industrial computer is provided with an HMI module, a system clock and a time server. The HMI module is used to read the data of the PLC module. The PLC module is used to collect the data of the IO module to the HMI module. The HMI module is used to obtain the time information of the system clock and save it in the data, and send the data to the integrated monitoring system. The integrated monitoring system is used to send a time synchronization command to the industrial computer. The time server is used to obtain the time synchronization command sent by the integrated monitoring system, perform data analysis, obtain time information, and control the system clock modification time, and synchronize the time information of the system clock with the time information of the integrated monitoring system. This solution relies on hardware network timing to achieve time synchronization. It is still difficult to maintain high-precision time synchronization for rail transit with high dispersion and complex environment. Summary of the invention

[0005] The purpose of the present invention is to provide a time synchronization method, a rail transit management method and a system based on digital twins, which can mine events occurring at the same time by parsing data packets uploaded by decentralized devices, thereby achieving accurate time synchronization of multiple devices.

[0006] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] The present invention provides a time synchronization method based on digital twins, comprising:

[0008] receiving information data packets;

[0009] Parsing the timestamp, device number and information content included in the information data packet;

[0010] Constructing a digital twin model based on the timestamp, device number and information content parsed from the information data packet, wherein the information content contained in one or more information data packets constructs an event unit in the digital twin model;

[0011] Screening out the same event unit in the digital twin model constructed by the information contents contained in the multiple information data packets as a multimodal cross-event unit;

[0012] Obtaining multiple timestamps corresponding to the multimodal cross event unit;

[0013] Obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to multiple timestamps corresponding to the multimodal cross event unit;

[0014] Obtain multiple device numbers corresponding to the multimodal cross event unit;

[0015] Time synchronization calibration is performed on multiple devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit.

[0016] In one embodiment of the present invention, the step of obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to the multiple timestamps corresponding to the multimodal cross event unit includes:

[0017] Obtaining the distribution state of multiple timestamps corresponding to the multimodal cross event unit on the time axis;

[0018] According to the distribution status of multiple timestamps on the time axis, abnormal timestamps are filtered out to obtain the distribution status of normal timestamps on the time axis;

[0019] Get the interval time between each normal timestamp and the adjacent normal timestamp on the time axis;

[0020] Obtain the average of the interval time periods between each normal timestamp and the adjacent normal timestamps as the normal timestamp interval average time period;

[0021] Obtain the number of other normal timestamps within the mean time period of each normal timestamp before and after each normal timestamp on the time axis as the time period density of the normal timestamp;

[0022] Calculate the mean value of the density of the time period where the normal timestamp is located;

[0023] The actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit is obtained by screening according to the time period density of the normal timestamp and the mean value of the time period density of the normal timestamp.

[0024] In one embodiment of the present invention, the step of obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit by screening according to the time period density of the normal timestamp and the mean value of the time period density of the normal timestamp includes:

[0025] Arrange the density of each normal timestamp from high to low to obtain a normal timestamp density sequence;

[0026] According to the mean value of the density of the time period in which the normal timestamps are located, a normal timestamp density sequence is selected to obtain multiple high-density normal timestamps;

[0027] Determine whether the distribution span of multiple high-density normal timestamps on the time axis exceeds the mean time period of the interval between two normal timestamps;

[0028] If it does not exceed, the normal timestamp with the highest density in the time period in the normal timestamp density sequence is taken as the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit;

[0029] If it exceeds, the device corresponding to the multimodal cross-event unit is marked as a time-unreliable device.

[0030] In one embodiment of the present invention, the method further comprises:

[0031] Determine whether the number or proportion of time-unreliable devices exceeds the set value;

[0032] If not, no operation is performed;

[0033] If so, a synchronization calibration instruction including a standard timestamp is broadcast to all devices, and the synchronization calibration instruction requires the receiving device to package the receiving time as the timestamp, the receiving device number, and the received standard timestamp as the information content into an information data packet for transmission.

[0034] In one embodiment of the present invention, the method further comprises:

[0035] According to the corresponding relationship between the information data packet and the multimodal cross event unit, the correlation relationship between the information data packet and the actual occurrence time of the multimodal cross event is obtained;

[0036] The actual occurrence times of multiple multimodal cross events corresponding to the same information data packet are screened out according to the correlation relationship between the information data packet and the actual occurrence time of the multimodal cross event;

[0037] Determine whether the actual occurrence times of multiple multi-modal cross events corresponding to the same information data packet are the same;

[0038] If they are the same, no operation will be performed;

[0039] If they are not the same, the actual occurrence time of multiple multi-modal cross events corresponding to the same information data packet is used as the time to be corrected;

[0040] Get multiple timestamps corresponding to each moment to be corrected;

[0041] Correct the time to be corrected according to multiple timestamps corresponding to each time to be corrected, and obtain the corrected actual time of occurrence of the multi-modal cross event;

[0042] The time synchronization of the corresponding multiple devices is calibrated according to the actual occurrence time of the corrected multi-modal cross event.

[0043] In one embodiment of the present invention, the step of correcting the time to be corrected according to the multiple timestamps corresponding to each time to be corrected to obtain the corrected actual time of occurrence of the multimodal cross event includes:

[0044] Get the distribution status of the normal timestamp corresponding to each moment to be corrected on the time axis;

[0045] The accuracy of each time to be corrected is obtained according to the distribution state of the normal timestamp corresponding to the time to be corrected on the time axis;

[0046] The accuracy of the time to be corrected is used as the weight of the corresponding time to be corrected, and the weighted average of all the times to be corrected is obtained as the actual occurrence time of the corrected multimodal cross-event.

[0047] In one embodiment of the present invention, the step of obtaining the accuracy of each time to be corrected according to the distribution state of the normal timestamp corresponding to the time to be corrected on the time axis includes:

[0048] For each time to be corrected,

[0049] Obtain the number of all normal timestamps corresponding to the time to be corrected according to the distribution state of the normal timestamps corresponding to the time to be corrected on the time axis;

[0050] According to the distribution state of the normal timestamps corresponding to the time to be corrected on the time axis, the normal timestamps with the highest density in the time period corresponding to the time to be corrected and within the mean time period of the normal timestamps before and after the time axis are obtained as the possessing normal timestamps of the time to be corrected;

[0051] Get the number of normal timestamps corresponding to the time to be corrected;

[0052] The ratio of the number of normal timestamps corresponding to the time to be corrected to the number of all corresponding normal timestamps is taken as the accuracy of the time to be corrected.

[0053] The present invention also discloses a time synchronization method based on digital twins, comprising:

[0054] Sending the information data packet;

[0055] Accept the time synchronization calibration as described.

[0056] The present invention also discloses a rail transit management method based on digital twins.

[0057] receiving information data packets;

[0058] Parsing the timestamp, device number and information content included in the information data packet, wherein the device includes a train, and the information content includes the running status of the train;

[0059] A digital twin model is constructed according to the timestamp, device number and information content parsed from the information data packet, wherein the information content contained in one or more information data packets is used to construct an event unit in the digital twin model, and the event unit includes the arrival information of the train;

[0060] Screening out the same event unit in the digital twin model constructed by the information contents contained in the multiple information data packets as a multimodal cross-event unit;

[0061] Obtaining multiple timestamps corresponding to the multimodal cross event unit;

[0062] Obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to multiple timestamps corresponding to the multimodal cross event unit;

[0063] Obtain multiple device numbers corresponding to the multimodal cross event unit;

[0064] Time synchronization calibration is performed on multiple devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit.

[0065] The present invention also discloses a rail transit management system based on digital twin, comprising:

[0066] Device for transmitting information data packets, wherein the device comprises a train;

[0067] A main control terminal, used for receiving the information data packet;

[0068] Parsing the timestamp, device number and information content included in the information data packet, wherein the information content includes the train running status;

[0069] A digital twin model is constructed according to the timestamp, device number and information content parsed from the information data packet, wherein the information content contained in one or more information data packets is used to construct an event unit in the digital twin model, and the event unit includes the arrival information of the train;

[0070] Screening out the same event unit in the digital twin model constructed by the information contents contained in the multiple information data packets as a multimodal cross-event unit;

[0071] Obtaining multiple timestamps corresponding to the multimodal cross event unit;

[0072] Obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to multiple timestamps corresponding to the multimodal cross event unit;

[0073] Obtain multiple device numbers corresponding to the multimodal cross event unit;

[0074] Time synchronization calibration is performed on multiple devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit.

[0075] The present invention collects information packets uploaded by different devices, and then constructs a digital twin model containing different events and their corresponding timestamps. By analyzing the digital twin model, the time difference between the time when different devices record the same event can be obtained. Based on this time difference, the accurate actual time of the event can be obtained, and the time of multiple devices involved can be synchronized accordingly.

[0076] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0078] Figure 1 The process steps of a time synchronization method based on digital twins described in the present invention are shown in FIG. Figure 1 ;

[0079] Figure 2 This is a schematic diagram of the process steps of step S6 of the present invention;

[0080] Figure 3 The process steps of step S67 of the present invention are shown as follows: Figure 1 ;

[0081] Figure 4 The process steps of step S67 of the present invention are shown as follows: Figure 2 ;

[0082] Figure 5 The process steps of a time synchronization method based on digital twins described in the present invention are shown in FIG. Figure 2 ;

[0083] Figure 6 This is a schematic diagram of the process steps of step S15 of the present invention;

[0084] Figure 7 This is a schematic diagram of the process steps of step S152 of the present invention;

[0085] Figure 8 This is a schematic diagram of the functional modules and information flow of a rail transit management system based on digital twins described in the present invention.

[0086] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0087] 1-master terminal, 2-device, 21-train. DETAILED DESCRIPTION

[0088] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] Trains in rail transit systems, especially underground rail transit systems, travel underground for a long time where wireless signals are difficult to penetrate, which makes it difficult to accurately control the running status of trains. However, rail transit is an important guarantee for social operation, and trains running in tunnels may cause rear-end collisions and other serious faults due to untimely control under high-load operation. In view of this, it is necessary to keep accurate event synchronization between trains and other equipment and the main control end.

[0090] In order to accurately control rail transit equipment including trains, it is necessary to collect information packets uploaded by various devices in real time, parse the information content therein, and build a digital twin system that reflects the overall operating status of the rail transit system. However, due to the untimely synchronization of information between the device and the main control end, the digital twin system may be updated in a chaotic manner and lose its practical application value. In order to solve the above problems, the present invention provides the following solution.

[0091] See also Figure 1 As shown, the present invention provides a time synchronization method based on digital twins, which can generally include two major parts: establishing a digital twin model and analyzing the actual occurrence time of multimodal cross-events based on the digital twin model. First, step S1 can be executed to receive an information data packet. This data packet can be sent directly through the intranet by a vehicle or other equipment, or it can be sent through a public network. Next, step S2 can be executed to parse out the timestamp, device number, and information content included in the information data packet. Since the data packet is packaged in a fixed format, it can be obtained by reverse compilation. Next, step S3 can be executed to construct a digital twin model based on the timestamp, device number, and information content parsed from the information data packet. The digital twin model in this solution is a virtual digital model to reflect the operating status of various equipment in the rail transit system, and mainly records the operating events of various equipment. Therefore, in the digital twin model, the information content contained in one or more information data packets constructs an event unit in the digital twin model.

[0092] Next, step S4 can be executed to filter out the same event unit in the digital twin model constructed by the information content contained in multiple information data packets as a multimodal cross event unit, that is, the same event in which multiple devices participate. Next, step S5 can be executed to obtain multiple timestamps corresponding to the multimodal cross event unit. This is because for the same event, due to the local time difference of different devices, the recording time of different devices is different. Next, step S6 can be executed to obtain the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to the multiple timestamps corresponding to the multimodal cross event unit. By back-calculating the different recording times of the same event, the real occurrence time of the event can be analyzed. This is because the local time of a single device will have errors due to the influence of system hardware, but the overall expected value of the local event error of multiple devices during operation will be consistent with the actual event. Next, step S7 can be executed to obtain multiple device numbers corresponding to the multimodal cross event unit. Finally, step S8 can be executed to perform time synchronization calibration on multiple devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit, that is, to analyze the actual time through the twin digital model, and then perform time synchronization calibration on the device.

[0093] See also Figure 2 As shown, after a long period of operation, the local time of the device will have an error with the actual time due to hardware processing errors and other reasons. These errors can be described as probabilistic events of normal distribution in statistics. In view of this, the actual time can be calculated by integrating the local time of multiple devices. In the specific implementation process, step S61 can be first executed to obtain the distribution state of multiple timestamps corresponding to the multimodal cross event unit on the time axis. Next, step S62 can be executed to filter out abnormal timestamps according to the distribution state of multiple timestamps on the time axis to obtain the distribution state of normal timestamps on the time axis. Next, step S63 can be executed to obtain the interval time period between each normal timestamp and the adjacent normal timestamp on the time axis. Next, step S64 can be executed to obtain the mean of the interval time period between each normal timestamp and the adjacent normal timestamp as the normal timestamp interval mean time period. Next, step S65 can be executed to obtain the number of other normal timestamps in the mean time period of each normal timestamp before and after each normal timestamp on the time axis as the time period density of the normal timestamp. Next, step S66 can be executed to calculate the mean of the time period density of the normal timestamp. Finally, step S67 may be executed to filter out the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to the time period density of the normal timestamp and the mean value of the time period density of the normal timestamp.

[0094] This solution can be implemented in practice through the following program code:

[0095]

[0096]

[0097]

[0098] This code defines a series of functions to implement the task according to the given steps. In the main function, we call the corresponding functions according to these steps to process multiple timestamps corresponding to multimodal cross event units and filter out the actual occurrence time.

[0099] See also Figure 3 As shown, since the timing module of the device appears abnormal during the manufacturing process or during the operation process, it may cause a large deviation in the local time of the device. In order to avoid adverse effects on the subsequent calculation of the actual time, these abnormal events need to be discovered and eliminated. Therefore, during the execution of the above step S67, step S671 can be first executed to arrange the time period density of each normal timestamp from high to low to obtain a normal timestamp density sequence. Next, step S672 can be executed to select a normal timestamp density sequence according to the mean value of the time period density of the normal timestamp to obtain a plurality of high-density normal timestamps. Next, step S673 can be executed to determine whether the distribution span of the plurality of high-density normal timestamps on the time axis exceeds the mean time period of the interval between the two normal timestamps. If not, then step S674 can be executed to use the normal timestamp with the highest time period density in the normal timestamp density sequence as the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit. If it exceeds, then step S675 can be executed to mark the device corresponding to the multimodal cross event unit as a time unreliable device.

[0100] This solution can be implemented in practice through the following program code:

[0101]

[0102]

[0103]

[0104] This code example builds on the previous implementation and adds new functions to satisfy the given steps. Note that this code relies on functions from previous examples, such as remove_abnormal_timestamps, calculate_intervals, calculate_mean, and calculate_density. Be sure to use these functions together in your actual application.

[0105] See also Figure 4 As shown, if there are too many unreliable time devices in the system, the system operation may fail, and the actual time of occurrence generated by step S6 may also be seriously inaccurate. In order to avoid this situation, it is necessary to determine whether the number or proportion of unreliable time devices exceeds the set value. The set value here can be calculated according to the specific situation of the system, or it can be set by the management personnel. Generally speaking, it should not exceed 5% of the total number of devices. If not, then step S667 can be executed next without operation. If so, then step S678 can be executed next to broadcast a synchronization calibration instruction containing a standard timestamp to all devices. The synchronization calibration instruction requires the receiving device to package the received time as a timestamp, the receiving device number, and the received standard timestamp as information content into an information data packet for transmission. In this step, the event of the device responding to the synchronization calibration instruction is used as a multimodal cross event in the multimodal cross event unit in the digital twin model, and it is also a multimodal cross event in which all devices participate, and all devices are synchronized in time through the standard timestamp.

[0106] See also Figure 5 As shown, the same device may participate in generating multiple multimodal cross-events, that is, the actual occurrence times of multiple multimodal cross-events corresponding to the same information data packet. If the actual occurrence times of the corresponding multiple multimodal cross-events are inconsistent, they need to be corrected, that is, after executing step S8, it is necessary to continue to execute step S9 to obtain the association relationship between the information data packet and the actual occurrence time of the multimodal cross-event based on the correspondence between the information data packet and the multimodal cross-event unit.

[0107] Next, step S10 can be executed to filter out the actual occurrence time of multiple multimodal cross events corresponding to the same information data packet according to the correlation between the information data packet and the actual occurrence time of the multimodal cross event. Next, step S11 can be executed to determine whether the actual occurrence time of multiple multimodal cross events corresponding to the same information data packet is the same. If they are the same, then step S12 can be executed without any operation. If they are not the same, then step S13 can be executed to use the actual occurrence time of multiple multimodal cross events corresponding to the same information data packet as the time to be corrected. Next, step S14 can be executed to obtain multiple timestamps corresponding to each time to be corrected. Next, step S15 can be executed to correct the time to be corrected according to the multiple timestamps corresponding to each time to be corrected, and obtain the corrected actual occurrence time of the multimodal cross event. Finally, step S16 can be executed to perform time synchronization calibration on the corresponding multiple devices according to the corrected actual occurrence time of the multimodal cross event.

[0108] This solution can be implemented in practice through the following program code:

[0109]

[0110]

[0111]

[0112] This code example defines a series of functions to implement the task according to the given steps. In the main function, we call the corresponding functions according to these steps to process the correspondence between information packets and multimodal cross event units and perform time synchronization calibration.

[0113] See also Figure 6 and 7 As shown, in order to correct the actual occurrence time of the multimodal cross event, it is necessary to refer to the multiple normal timestamps of the actual occurrence time of the multimodal cross event, so it is necessary to first perform step S151 to obtain the distribution state of the normal timestamps corresponding to each time to be corrected on the time axis. Next, step S152 can be performed to obtain the accuracy of each time to be corrected according to the distribution state of the normal timestamps corresponding to the time to be corrected on the time axis. Finally, step S153 can be performed to use the accuracy of the time to be corrected as the weight of the corresponding time to be corrected, and the weighted average of all the times to be corrected is obtained as the actual occurrence time of the corrected multimodal cross event. However, in step S1521, since there are various statistical analysis methods for generating multiple normal timestamps of the actual occurrence time of the multimodal cross event, in order to realize the correction effect on the actual occurrence time of the multimodal cross event, it is necessary to refer to the effective normal timestamps therein, that is, for each time to be corrected, step S1521 can be first performed to obtain the number of all normal timestamps corresponding to the time to be corrected according to the distribution state of the normal timestamps corresponding to the time to be corrected on the time axis. Next, step S1522 can be executed to obtain the normal timestamp with the highest density in the time period corresponding to the time to be corrected according to the distribution state of the normal timestamps corresponding to the time to be corrected on the time axis, and the normal timestamps within the mean time period of the normal timestamps before and after on the time axis as the possessing normal timestamp of the time to be corrected. Next, step S1523 can be executed to obtain the number of possessing normal timestamps corresponding to the time to be corrected. Finally, step S1524 can be executed to take the ratio of the number of possessing normal timestamps corresponding to the time to be corrected to the number of all corresponding normal timestamps as the accuracy of the time to be corrected.

[0114] This solution can be implemented in practice through the following program code:

[0115]

[0116]

[0117] This code example defines a series of functions that implement the tasks given in the given steps. In the main function, we follow these steps and call the corresponding functions to handle the accuracy of the time to be corrected. Note that this code relies on some variables and functions from previous examples, such as adjusted_timestamps, normal_timestamps, and mean_interval. Make sure to use these variables and functions together in your actual application.

[0118] The above steps are the workflow of the entire system from the perspective of the control end. From the perspective of the device, it mainly includes two steps. The first is to send information data packets, and the second is to receive time synchronization calibration. Other implementation details will not be repeated.

[0119] Specifically in the rail transit system, the present solution also discloses a rail transit management method based on digital twins, and in its implementation process, step S1 can be first executed to receive an information data packet. Next, step S2 can be executed to parse the timestamp, device number and information content included in the information data packet, wherein the device includes a train, and the information content includes the train running status. Next, step S3 can be executed to build a digital twin model according to the timestamp, device number and information content parsed from the information data packet, wherein the information content contained in one or more information data packets constructs an event unit in the digital twin model, and the event unit includes the arrival information of the train. Next, step S4 can be executed to filter out the same event unit in the digital twin model constructed by the information content contained in multiple information data packets as a multimodal cross event unit. Next, step S5 can be executed to obtain multiple timestamps corresponding to the multimodal cross event unit. Next, step S6 can be executed to obtain the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to the multiple timestamps corresponding to the multimodal cross event unit. Next, step S7 can be executed to obtain multiple device numbers corresponding to the multimodal cross event unit. Finally, step S8 can be executed to perform time synchronization calibration on multiple devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit. A digital twin model is obtained by virtually modeling the events in which trains and other equipment in the rail transit system participate, and the model is analyzed to obtain the local time of the equipment such as trains participating in the same event. The actual time is obtained after analysis, and the time synchronization calibration of trains and other equipment is performed accordingly to avoid the loss of control of the rail transit system due to time asynchrony during operation.

[0120] Next, this solution is specifically analyzed in combination with the main control terminal 1 and the device 2.

[0121] See also Figure 8 As shown, the present solution also discloses a rail transit management system based on digital twins. From a functional perspective, the present system may include a master control terminal 1 and a device 2. The master control terminal 1 may be a master control server of a rail transit system, which is used to receive and send various data and commands, and may also store various data. The device 2 may be a train 21, or may be a gate, a screen door, a broadcasting device, a lighting control device, and the like. The device 2 is used to send an information data packet. During operation, the master control terminal 1 may first execute step S1 to receive an information data packet. Next, step S2 may be executed to parse out the timestamp, device number, and information content included in the information data packet, wherein the information content includes the running status of the train. Next, step S3 may be executed to construct a digital twin model according to the timestamp, device number, and information content parsed from the information data packet, wherein the information content contained in one or more information data packets constructs an event unit in the digital twin model, and the event unit includes the arrival information of the train. Next, step S4 may be executed to filter out the same event unit in the digital twin model constructed by the information content contained in multiple information data packets as a multimodal cross event unit. Next, step S5 may be executed to obtain multiple timestamps corresponding to the multimodal cross event unit. Next, step S6 can be executed to obtain the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to the multiple timestamps corresponding to the multimodal cross event unit. Next, step S7 can be executed to obtain the multiple device numbers corresponding to the multimodal cross event unit. Finally, step S8 can be executed to perform time synchronization calibration on the multiple devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit. The specific operating principle can be found in the above part. In general, through the construction and analysis of the digital twin model, the time synchronization of the equipment is realized, and the operation failure of the rail transit system caused by time error is effectively avoided.

[0122] In summary, during the implementation of this solution, the digital twin model is constructed by receiving information data packets, parsing the contents of the data packets, and using the multimodal cross-event unit in the model to obtain the timestamp and device number, and perform time synchronization calibration on the equipment. By constructing and analyzing the digital twin model, the time synchronization of multiple remote devices in the system is achieved, which improves the reliability of the rail transit system.

[0123] The above description of the illustrated embodiments of the present invention (including the contents in the abstract of the specification) is not intended to be an exhaustive list or to limit the present invention to the precise form disclosed herein. Although specific embodiments of the present invention and examples of the present invention are described herein for illustrative purposes only, as will be recognized and appreciated by those skilled in the art, various equivalent modifications are possible within the spirit and scope of the present invention. As noted, these modifications may be made to the present invention in accordance with the above description of the embodiments of the present invention, and these modifications will be within the spirit and scope of the present invention.

[0124] Systems and methods have been generally described herein as details that aid in understanding the present invention. In addition, various specific details have been given to provide an overall understanding of embodiments of the present invention. However, those skilled in the relevant art will recognize that embodiments of the present invention may be practiced without one or more of the specific details, or may be practiced using other devices, systems, accessories, methods, components, materials, parts, etc. In other cases, well-known structures, materials, and / or operations are not specifically shown or described in detail to avoid confusion with various aspects of embodiments of the present invention.

[0125] Thus, although the invention has been described herein with reference to specific embodiments thereof, freedom of modification, various changes and substitutions are intended within the foregoing disclosure, and it should be understood that in some cases, some features of the invention will be employed without the corresponding use of other features without departing from the scope and spirit of the proposed invention. Thus, many modifications may be made to adapt a particular environment or material to the essential scope and spirit of the invention. The invention is not intended to be limited to the specific terms used in the claims below and / or the specific embodiments disclosed as the best mode contemplated for carrying out the invention, but the invention will include any and all embodiments and equivalents falling within the scope of the appended claims. Thus, the scope of the invention will be determined solely by the appended claims.

Claims

1. A time synchronization method based on digital twins, characterized in that: include, receiving information data packets; Parsing the timestamp, device number and information content included in the information data packet; Constructing a digital twin model based on the timestamp, device number and information content parsed from the information data packet, wherein the information content contained in one or more information data packets constructs an event unit in the digital twin model; Screening out the same event unit in the digital twin model constructed by the information contents contained in the multiple information data packets as a multimodal cross-event unit; Obtaining multiple timestamps corresponding to the multimodal cross event unit; Obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to multiple timestamps corresponding to the multimodal cross event unit; Obtain multiple device numbers corresponding to the multimodal cross event unit; Performing time synchronization calibration on a plurality of devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit; Obtaining the distribution state of multiple timestamps corresponding to the multimodal cross event unit on the time axis; According to the distribution status of multiple timestamps on the time axis, abnormal timestamps are filtered out to obtain the distribution status of normal timestamps on the time axis; Get the interval time between each normal timestamp and the adjacent normal timestamp on the time axis; Obtain the average of the interval time periods between each normal timestamp and the adjacent normal timestamps as the normal timestamp interval average time period; Obtain the number of other normal timestamps within the mean time period of each normal timestamp before and after each normal timestamp on the time axis as the time period density of the normal timestamp; Calculate the mean value of the density of the time period where the normal timestamp is located; The actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit is obtained by screening according to the time period density of the normal timestamp and the mean value of the time period density of the normal timestamp.

2. The method according to claim 1, characterized in that The step of obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit by screening according to the time period density of the normal timestamp and the mean value of the time period density of the normal timestamp includes: Arrange the density of each normal timestamp from high to low to obtain a normal timestamp density sequence; According to the mean value of the density of the time period in which the normal timestamps are located, a normal timestamp density sequence is selected to obtain multiple high-density normal timestamps; Determine whether the distribution span of multiple high-density normal timestamps on the time axis exceeds the mean time period of the interval between two normal timestamps; If it does not exceed, the normal timestamp with the highest density in the time period in the normal timestamp density sequence is taken as the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit; If it exceeds, the device corresponding to the multimodal cross-event unit is marked as a time-unreliable device.

3. The method according to claim 1, characterized in that The method further comprises, Determine whether the number or proportion of time-unreliable devices exceeds the set value; If not, no operation is performed; If so, a synchronization calibration instruction including a standard timestamp is broadcast to all devices, and the synchronization calibration instruction requires the receiving device to package the receiving time as the timestamp, the receiving device number, and the received standard timestamp as the information content into an information data packet for transmission.

4. The method according to claim 1, characterized in that: The method further comprises, According to the corresponding relationship between the information data packet and the multimodal cross event unit, the correlation relationship between the information data packet and the actual occurrence time of the multimodal cross event is obtained; The actual occurrence times of multiple multimodal cross events corresponding to the same information data packet are screened out according to the correlation relationship between the information data packet and the actual occurrence time of the multimodal cross event; Determine whether the actual occurrence times of multiple multi-modal cross events corresponding to the same information data packet are the same; If they are the same, no operation will be performed; If they are not the same, the actual occurrence time of multiple multi-modal cross events corresponding to the same information data packet is used as the time to be corrected; Get multiple timestamps corresponding to each moment to be corrected; Correct the time to be corrected according to multiple timestamps corresponding to each time to be corrected, and obtain the corrected actual time of occurrence of the multi-modal cross event; The time synchronization of the corresponding multiple devices is calibrated according to the actual occurrence time of the corrected multi-modal cross event.

5. The method according to claim 4, characterized in that The step of correcting the time to be corrected according to the multiple timestamps corresponding to each time to be corrected to obtain the corrected actual time of occurrence of the multi-modal cross event includes: Get the distribution status of the normal timestamp corresponding to each moment to be corrected on the time axis; The accuracy of each time to be corrected is obtained according to the distribution state of the normal timestamp corresponding to the time to be corrected on the time axis; The accuracy of the time to be corrected is used as the weight of the corresponding time to be corrected, and the weighted average of all the times to be corrected is obtained as the actual occurrence time of the corrected multimodal cross-event.

6. The method according to claim 5, characterized in that The step of obtaining the accuracy of each time to be corrected according to the distribution state of the normal timestamp corresponding to the time to be corrected on the time axis, include, For each time to be corrected, Obtain the number of all normal timestamps corresponding to the time to be corrected according to the distribution state of the normal timestamps corresponding to the time to be corrected on the time axis; According to the distribution state of the normal timestamps corresponding to the time to be corrected on the time axis, the normal timestamps with the highest density in the time period corresponding to the time to be corrected and within the mean time period of the normal timestamps before and after the time axis are obtained as the possessing normal timestamps of the time to be corrected; Get the number of normal timestamps corresponding to the time to be corrected; The ratio of the number of normal timestamps corresponding to the time to be corrected to the number of all corresponding normal timestamps is taken as the accuracy of the time to be corrected.

7. A time synchronization method based on digital twins, characterized in that: include, Sending an information data packet as claimed in any one of claims 1 to 6; Accept the time synchronization calibration according to any one of claims 1 to 6.

8. A rail transit management method based on digital twins, characterized in that: receiving information data packets; Parsing the timestamp, device number and information content included in the information data packet, wherein the device includes a train, and the information content includes the running status of the train; A digital twin model is constructed according to the timestamp, device number and information content parsed from the information data packet, wherein the information content contained in one or more information data packets is used to construct an event unit in the digital twin model, and the event unit includes the arrival information of the train; Screening out the same event unit in the digital twin model constructed by the information contents contained in the multiple information data packets as a multimodal cross-event unit; Obtaining multiple timestamps corresponding to the multimodal cross event unit; Obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to multiple timestamps corresponding to the multimodal cross event unit; Obtain multiple device numbers corresponding to the multimodal cross event unit; Performing time synchronization calibration on a plurality of devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit; Obtaining the distribution state of multiple timestamps corresponding to the multimodal cross event unit on the time axis; According to the distribution status of multiple timestamps on the time axis, abnormal timestamps are filtered out to obtain the distribution status of normal timestamps on the time axis; Get the interval time between each normal timestamp and the adjacent normal timestamp on the time axis; Obtain the average of the interval time periods between each normal timestamp and the adjacent normal timestamps as the normal timestamp interval average time period; Obtain the number of other normal timestamps within the mean time period of each normal timestamp before and after each normal timestamp on the time axis as the time period density of the normal timestamp; Calculate the mean value of the density of the time period where the normal timestamp is located; The actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit is obtained by screening according to the time period density of the normal timestamp and the mean value of the time period density of the normal timestamp.

9. A rail transit management system based on digital twin, characterized in that: include, Device for transmitting information data packets, wherein the device comprises a train; A main control terminal, used for receiving the information data packet; Parsing the timestamp, device number and information content included in the information data packet, wherein the information content includes the train running status; A digital twin model is constructed according to the timestamp, device number and information content parsed from the information data packet, wherein the information content contained in one or more information data packets is used to construct an event unit in the digital twin model, and the event unit includes the arrival information of the train; Screening out the same event unit in the digital twin model constructed by the information contents contained in the multiple information data packets as a multimodal cross-event unit; Obtaining multiple timestamps corresponding to the multimodal cross event unit; Obtaining the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit according to multiple timestamps corresponding to the multimodal cross event unit; Obtain multiple device numbers corresponding to the multimodal cross event unit; Performing time synchronization calibration on a plurality of devices corresponding to the multimodal cross event unit according to the actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit; Obtaining the distribution state of multiple timestamps corresponding to the multimodal cross event unit on the time axis; According to the distribution status of multiple timestamps on the time axis, abnormal timestamps are filtered out to obtain the distribution status of normal timestamps on the time axis; Get the interval time between each normal timestamp and the adjacent normal timestamp on the time axis; Obtain the average of the interval time periods between each normal timestamp and the adjacent normal timestamps as the normal timestamp interval average time period; Obtain the number of other normal timestamps within the mean time period of each normal timestamp before and after each normal timestamp on the time axis as the time period density of the normal timestamp; Calculate the mean value of the density of the time period where the normal timestamp is located; The actual occurrence time of the multimodal cross event corresponding to the multimodal cross event unit is obtained by screening according to the time period density of the normal timestamp and the mean value of the time period density of the normal timestamp.

Images