Positioning method, device, electronic device, vehicle, storage medium and program product

Abstract

The present application provides a positioning method, device, electronic device, vehicle, storage medium and program product, which relate to the field of rail transit technology, and are at least used to solve the problem of how to improve the reliability and accuracy of rail transit vehicle positioning when the positioning system of the relevant technology has limitations or failures. The method includes: determining the travel distance of the rail transit vehicle within a preset time period based on the power consumption of the rail transit vehicle within a preset time period; and positioning the rail transit vehicle based on the travel distance.

Description

Technical Field

[0001] The present application relates to the field of rail transit technology, and in particular to a positioning method, device, electronic equipment, vehicle, storage medium and program product. Background Art

[0002] With the rapid development of science and technology, modern society has higher and higher requirements for the accuracy and reliability of positioning systems. In many fields such as intelligent transportation systems, self-driving cars, and logistics tracking, accurate vehicle positioning information has become an indispensable key technology. Vehicle positioning is not only related to driving safety, but also an important means to improve transportation efficiency and optimize resource allocation.

[0003] The positioning system in the related art, taking rail transit vehicles as an example, usually relies on speed sensors and ground transponders. The ground transponder provides position calibration when the rail transit vehicle passes by, and the speed sensor estimates the travel distance by measuring the real-time speed and travel time of the rail transit vehicle, and then the rail transit vehicle can be positioned based on the information measured by the ground transponder and the speed sensor. Although this positioning method has achieved the positioning of rail transit vehicles to a certain extent, the accuracy of speed sensors and ground transponders is easily affected by various factors, such as sensor accuracy, changes in environmental conditions, etc. Once the speed sensor fails or the ground transponder fails, the accuracy of the rail transit vehicle positioning will be greatly reduced, and may even lead to the complete loss of positioning information. Therefore, in the case of limitations or failures in the positioning system in the related art, how to improve the accuracy and reliability of rail transit vehicle positioning is crucial. Summary of the invention

[0004] The embodiments of the present application provide a positioning method, device, electronic device, vehicle, storage medium and program product, which are at least used to solve the problem of how to improve the reliability and accuracy of rail transit vehicle positioning when the positioning system of the related art has limitations or failures.

[0005] In a first aspect, a positioning method is provided, the method comprising:

[0006] Determining the travel distance of the rail transit vehicle within the preset time period based on the power consumption of the rail transit vehicle within the preset time period;

[0007] Locate rail transit vehicles based on travel distance.

[0008] Based on the positioning method provided in the embodiments of the present application, compared with the related art in which rail transit vehicles are positioned based on the information measured by the ground transponder and speed sensor in the positioning system, once the speed sensor fails or the ground transponder fails, the accuracy of the rail transit vehicle positioning will be greatly reduced, and may even lead to the complete loss of positioning information. The method provided in the present application determines the travel distance of the rail transit vehicle by the power consumption, and then locates the rail transit vehicle, which can reduce the dependence on speed sensors and ground transponders, avoid the positioning of rail transit vehicles affected by environmental restrictions or positioning system failures, and improve the reliability of rail transit vehicle positioning.

[0009] In addition, power consumption can accurately reflect the current driving information of rail transit vehicles in real time. The travel distance of rail transit vehicles can be determined based on power consumption, and then the rail transit vehicles can be positioned, which can improve the accuracy of rail transit vehicle positioning.

[0010] In some embodiments, positioning a rail transit vehicle based on the driving distance includes: determining an influence coefficient corresponding to an influencing factor of the driving distance; the influence coefficient is used to reflect the degree of influence of the influencing factor on the driving distance; based on the influence coefficient, correcting the driving distance to obtain a corrected driving distance; and positioning the rail transit vehicle based on the corrected driving distance.

[0011] In some embodiments, determining the influence coefficient corresponding to the influencing factor of the driving distance includes: determining the value of the influencing factor within the preset time period based on the driving information of the rail transit vehicle within the preset time period; and determining the influence coefficient corresponding to the value of the influencing factor within the preset time period based on the mapping relationship between the value of the influencing factor and the influence coefficient.

[0012] In some embodiments, the method further includes: determining update information of the mapping relationship for each positioning method based on the positioning methods supported by the rail transit vehicle; and optimizing the mapping relationship based on the update information of the mapping relationship for each positioning method.

[0013] In some embodiments, the positioning method includes: a first positioning method and a second positioning method; the first positioning method is used to reflect that the rail transit vehicle is positioned based on the vehicle speed sensor and the ground transponder; the second positioning method is used to reflect that the rail transit vehicle is positioned based on the correspondence between the power consumption and the travel distance of the rail transit vehicle. Based on the update information of the mapping relationship of each positioning method, the mapping relationship is optimized, including: obtaining the first update information of the mapping relationship of the first positioning method; obtaining the second update information of the mapping relationship of the second positioning method; determining the weight coefficient of each positioning method based on the historical update times of the mapping relationship; optimizing the mapping relationship based on the first update information, the second update information and the weight coefficient of each positioning method.

[0014] In some embodiments, based on the power consumption of the rail transit vehicle within the preset time period, the travel distance of the rail transit vehicle within the preset time period is determined, including: based on the driving resistance of the rail transit vehicle and the power consumption ratio of the rail transit vehicle, determining the correspondence between the power consumption and the travel distance of the rail transit vehicle; based on the correspondence and the power consumption of the rail transit vehicle within the preset time period, determining the travel distance of the rail transit vehicle within the preset time period.

[0015] In some embodiments, the influencing factors include at least one of the following: ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, curves of the driving route, cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power.

[0016] In a second aspect, a positioning device is provided, the device comprising:

[0017] A determination module, used to determine the travel distance of the rail transit vehicle within a preset time period based on the power consumption of the rail transit vehicle within the preset time period;

[0018] The positioning module is used to locate rail transit vehicles based on the travel distance.

[0019] In some embodiments, the positioning module is specifically used to determine the influence coefficient corresponding to the influencing factor of the driving distance; the influence coefficient is used to reflect the degree of influence of the influencing factor on the driving distance; based on the influence coefficient, the driving distance is corrected to obtain the corrected driving distance; based on the corrected driving distance, the rail transit vehicle is positioned.

[0020] In some embodiments, the determination module is specifically used to determine the value of the influencing factor within a preset time period based on the driving information of the rail transit vehicle within the preset time period; based on the mapping relationship between the value of the influencing factor and the influence coefficient, determine the influence coefficient corresponding to the value of the influencing factor within the preset time period.

[0021] In some embodiments, the device also includes: an optimization module; a determination module, which is also used to determine the update information of the mapping relationship for each positioning method based on the positioning methods supported by the rail transit vehicle; and an optimization module, which is used to optimize the mapping relationship based on the update information of the mapping relationship for each positioning method.

[0022] In some embodiments, the positioning method includes: a first positioning method and a second positioning method; the first positioning method is used to reflect that the rail transit vehicle is positioned based on the vehicle speed sensor and the ground transponder; the second positioning method is used to reflect that the rail transit vehicle is positioned based on the correspondence between the power consumption and the travel distance of the rail transit vehicle. The optimization module is specifically used to obtain the first update information of the mapping relationship of the first positioning method; obtain the second update information of the mapping relationship of the second positioning method; determine the weight coefficient of each positioning method based on the historical update times of the mapping relationship; optimize the mapping relationship based on the first update information, the second update information and the weight coefficient of each positioning method.

[0023] In some embodiments, the determination module is specifically used to determine the correspondence between the power consumption and the traveling distance of the rail transit vehicle based on the traveling resistance of the rail transit vehicle and the power consumption ratio of the rail transit vehicle; based on the corresponding relationship and the power consumption of the rail transit vehicle in the preset time period, determine the traveling distance of the rail transit vehicle within the preset time period.

[0024] In some embodiments, the influencing factors include at least one of the following: ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, curves of the driving route, cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power.

[0025] According to a third aspect, an electronic device is provided, comprising: a processor and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the positioning method described above.

[0026] In a fourth aspect, a vehicle is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the positioning method described above.

[0027] In a fifth aspect, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the positioning method of any of the above embodiments is implemented.

[0028] In a sixth aspect, a computer program product is provided, the computer program product comprising computer program instructions, which implement the positioning method of any of the above embodiments when executed by a processor.

[0029] For the specific description of the second to sixth aspects and their various implementations in the present application, reference may be made to the detailed description in the first aspect and its various implementations; and for the beneficial effects of the second to sixth aspects and their various implementations, reference may be made to the beneficial effects analysis in the first aspect and its various implementations, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solution in the present application, the drawings required for use in some embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only drawings of some embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0031] Figure 1 A schematic diagram of a positioning system provided in an embodiment of the present application;

[0032] Figure 2 A flowchart of a positioning method provided in an embodiment of the present application;

[0033] Figure 3 A flowchart of another positioning method provided in an embodiment of the present application;

[0034] Figure 4 A flowchart of another positioning method provided in an embodiment of the present application;

[0035] Figure 5 A flowchart of another positioning method provided in an embodiment of the present application;

[0036] Figure 6 A flowchart of another positioning method provided in an embodiment of the present application;

[0037] Figure 7 A flowchart of another positioning method provided in an embodiment of the present application;

[0038] Figure 8 A flowchart of another positioning method provided in an embodiment of the present application;

[0039] Fig. 9 A flow chart of an influence coefficient optimization provided in an embodiment of the present application;

[0040] Fig.10 A schematic diagram of the structure of a positioning device provided in an embodiment of the present application;

[0041] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in this application to clearly and completely describe the technical solutions in this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] It should be noted that, in this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0044] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0045] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" means one or more, and "multiple" means two or more.

[0046] With the rapid development of science and technology, modern society has higher and higher requirements for the accuracy and reliability of positioning systems. In many fields such as intelligent transportation systems, self-driving cars, and logistics tracking, accurate vehicle positioning information has become an indispensable key technology. Vehicle positioning is not only related to driving safety, but also an important means to improve transportation efficiency, optimize resource allocation, and determine the vehicle's cruising range. In response to the above problems, related technologies have proposed some methods. As an example, related technologies propose a method, device, electronic device, and storage medium for determining vehicle cruising range. The method obtains the slope information of the vehicle's current driving, the temperature information of this driving, the friction resistance of this driving, and the corrected remaining energy through the vehicle's navigation information, and then determines the vehicle's cruising range. Among them, the method also proposes that the cruising range can be updated through the ambient temperature, slope, and energy consumption accessories during driving, the energy consumption coefficient can be obtained through the average speed of the vehicle, the energy consumption ratio can be obtained through friction resistance and air resistance, and the corrected energy component can be obtained through altitude, slope, train mass, and gravity acceleration component. It can be seen that this method is aimed at predicting the cruising range, but does not realize the positioning of the vehicle. It does not consider the impact of battery usage time on vehicle driving and positioning, nor does it consider the impact of energy recovery on vehicle driving.

[0047] As another example, the related art proposes a method, device, equipment, vehicle and storage medium for predicting cruising range, which calculates the historical energy consumption rate of the vehicle through the road environment, driving power, and the power of other vehicle accessories, and determines the energy consumption rate of the vehicle based on the historical energy consumption rate, accessory power and driving power of the vehicle, and then obtains the cruising range based on the energy consumption rate. It can be seen that this method is also aimed at predicting the cruising range, but does not achieve vehicle positioning. It does not consider the loss of battery use or the impact of different users' driving habits. The obtained cruising range is only a simple prediction, and it is impossible to obtain the distance traveled by the vehicle based on the real-time status of the previous period.

[0048] In general, the relevant technology predicts the subsequent range by obtaining the current battery power, environment and other related factors of the vehicle. If there is no navigation or the environment changes, the prediction result will be inaccurate. At the same time, the relevant technology does not take into account that as the cumulative range of the battery increases, the mileage that can be run with the same battery power will also be different. In addition, the uncertainty of the range prediction in the relevant technology will affect the accuracy of the prediction, and then affect the prediction of the vehicle's driving distance.

[0049] In view of the above problems, the embodiments of the present application provide a positioning method, which can determine the driving distance of the vehicle based on the power consumption of the vehicle, thereby realizing the positioning of the vehicle. At the same time, the method provided by the present application can also correct the driving distance of the vehicle based on the cumulative battery mileage of the vehicle, the current ambient temperature, the slope and curve, the wind direction and the air resistance corresponding to the wind force level, the energy consumption of the auxiliary accessories, etc., thereby improving the accuracy and reliability of the vehicle positioning.

[0050] For ease of understanding, the positioning method provided by the present application is specifically introduced below with reference to the accompanying drawings.

[0051] Figure 1 A schematic diagram of a positioning system provided in an embodiment of the present application. Figure 1 As shown, the positioning system includes: a sensor 110, a vehicle-mounted system 120 and a control center 130. The vehicle-mounted system 120 is in communication connection with the sensor 110 and the control center 130 respectively.

[0052] In some embodiments, the sensor 110 may be one or more sensors, which is not limited in this embodiment of the present application.

[0053] The sensor 110 is used to obtain data related to driving and send the relevant data to the vehicle-mounted system 120. For example, the sensor 110 can collect data such as the current ambient temperature, slope and curve, wind direction and wind force level, traction and braking level of the rail transit vehicle, and energy consumption of auxiliary accessories, and send it to the vehicle-mounted system 120.

[0054] In some embodiments, the sensor 110 may be a vehicle sensor. For example, the vehicle sensor may be a temperature sensor, a humidity sensor, a slope sensor, a speed sensor, a wind sensor, and the like.

[0055] In some embodiments, the rail transit vehicle mentioned in the present application may be a cloud bus, a cloud track, a train, an EMU, a high-speed rail, etc. The embodiments of the present application do not limit the specific form of the rail transit vehicle.

[0056] The on-board system 120 is used to monitor the operating status of the rail transit vehicle, determine the data related to the driving, and exchange information with other modules inside the rail transit vehicle.

[0057] In some embodiments, the vehicle-mounted system 120 stores location information of ground transponders. The vehicle-mounted system 120 can determine the current location of the rail transit vehicle by determining the ground transponder that the rail transit vehicle is passing by at this time.

[0058] In some embodiments, when the rail transit vehicle starts running, the onboard system 120 can record the relationship between the power consumption and the travel distance, and synchronize the relationship to the control center 130.

[0059] In some embodiments, the vehicle-mounted system 120 can obtain driving-related data. For example, the vehicle-mounted system 120 can obtain data such as the current ambient temperature, slope and curve, wind direction and wind force level, traction and braking level of the vehicle, and energy consumption of auxiliary accessories from the sensor 110.

[0060] In some embodiments, the vehicle-mounted system 120 may also determine the influencing factors affecting the travel distance of the rail transit vehicle based on the above driving-related data, determine the influence coefficient of each influencing factor, and then send the influence coefficient to the control center 130. The influence coefficient is used to reflect the degree of influence of the influencing factor on the travel distance of the rail transit vehicle.

[0061] In some embodiments, the vehicle-mounted system 120 can also continuously optimize and update the influence coefficients corresponding to the influencing factors based on the driving information of the rail transit vehicle and the values ​​of each influencing factor. For example, the vehicle-mounted system 120 can verify the influence coefficients based on the data provided by the sensor 110, and optimize the influence factors and influence coefficients at specific times and locations based on the characteristics of different influencing factors.

[0062] In some embodiments, the onboard system 120 can correct the travel distance of the rail transit vehicle based on the influence coefficient, and locate the rail transit vehicle based on the corrected travel distance and the position information of the rail transit vehicle obtained by the ground transponder.

[0063] The control center 130 is used to monitor and manage rail transit vehicles.

[0064] In some embodiments, the control center 130 may receive the relationship between the power consumption and the driving distance sent by the vehicle system 120 .

[0065] In some embodiments, the control center 130 can receive the influence coefficient of each influencing factor sent by the on-board system 120, and when the rail transit vehicle needs to be positioned, based on the environment in which the rail transit vehicle is located, send the influence coefficient of each influencing factor that matches the rail transit vehicle environment to the on-board system 120.

[0066] In some embodiments, the control center 130 can optimize and record the influence coefficient of each influencing factor by comparing multiple rail transit vehicles, and output the influence coefficient to the on-board system 120 of the rail transit vehicle when a rail transit vehicle is powered on or a new vehicle is put online.

[0067] It should be noted that the above scenario is for the purpose of more clearly illustrating the technical solution of the embodiment of the present application, and does not constitute a limitation on the technical solution provided by the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution provided by the embodiment of the present application is also applicable to similar technical problems.

[0068] Figure 2 A positioning method is provided for an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0069] S101. Determine a travel distance of a rail transit vehicle within a preset time period based on an amount of power consumed by the rail transit vehicle within a preset time period.

[0070] As an example, the travel distance of the rail transit vehicle within the preset time period can be estimated based on the power consumption of the rail transit vehicle within the preset time period and the battery capacity in the rail transit vehicle. For example, the preset time period can be 30 minutes. If the total distance that the rail transit vehicle can travel from full charge to exhausted power is 200KM, then if the power consumption of the rail transit vehicle within 30 minutes is 10% of the battery capacity, then the travel distance of the rail transit vehicle within the preset time period is approximately 200*5%=20KM.

[0071] As another example, based on the historical driving data of rail transit vehicles, it can be determined that the relationship between power consumption and travel distance is: for every kilowatt-hour (kW·h) of power consumed, the rail transit vehicle travels an average of 5 km. If the power consumption of the rail transit vehicle in 30 minutes is 10 kW·h of power and the battery capacity is 100 kW·h, the travel distance of the rail transit vehicle at this time is approximately 50 km.

[0072] As another example, a distance prediction model may be constructed and trained based on the historical driving data of rail transit vehicles, and the power consumption in a preset time period may be used as the input of the distance prediction model to output the travel distance of the rail transit vehicle in the preset time period.

[0073] It should be noted that the above-mentioned method of determining the driving distance of rail transit vehicles based on power consumption is only some examples given in the embodiments of the present application. In actual implementation, more methods can be selected to determine the driving distance based on actual conditions and different needs, and the embodiments of the present application do not limit this.

[0074] S102: Positioning the rail transit vehicle based on the travel distance.

[0075] In some embodiments, the rail transit vehicle can be positioned based on the travel distance and the track map. For example, the track map includes information such as the length, shape, slope, curve, branch point, and station location of the route traveled by the rail transit vehicle. Based on the initial position of the rail transit vehicle (such as the starting station of the vehicle or the last known position), the travel direction of the rail transit vehicle, and the travel distance of the rail transit vehicle, the current position of the rail transit vehicle can be determined based on the track map data.

[0076] It can be understood that compared with the related art in which rail transit vehicles are positioned based on the information measured by the ground transponder and speed sensor in the positioning system, once the speed sensor fails or the ground transponder fails, the accuracy of the rail transit vehicle positioning will be greatly reduced, and may even lead to the complete loss of positioning information. The method provided in the present application determines the travel distance of the rail transit vehicle through the power consumption, and then positions the rail transit vehicle, which can reduce the dependence on speed sensors and ground transponders, avoid the positioning of rail transit vehicles due to environmental limitations or positioning system failures, and improve the reliability of rail transit vehicle positioning.

[0077] In addition, power consumption can accurately reflect the current driving information of rail transit vehicles in real time. The travel distance of rail transit vehicles can be determined based on power consumption, and then the rail transit vehicles can be positioned, which can improve the accuracy of rail transit vehicle positioning.

[0078] In some embodiments, Figure 3 As shown, the above step S101 can be specifically implemented as: steps S1011-S1012.

[0079] S1011. Determine a correspondence between power consumption and a travel distance of the rail transit vehicle based on the travel resistance of the rail transit vehicle and the power consumption ratio of the rail transit vehicle.

[0080] In some embodiments, the vehicle-mounted system or control center may integrate the driving resistance and power consumption ratio of rail transit vehicles of all lines under different driving conditions to determine the corresponding relationship between the power consumption of each rail transit vehicle and the driving distance of the rail transit vehicle. Then, the vehicle-mounted system or control center may weight the corresponding relationship between the power consumption of each rail transit vehicle and the driving distance of the rail transit vehicle based on the weight coefficient of each rail transit vehicle, and then determine the final corresponding relationship between the power consumption and the driving distance of the rail transit vehicle.

[0081] S1012: Determine the travel distance of the rail transit vehicle within the preset time period based on the corresponding relationship and the power consumption of the rail transit vehicle within the preset time period.

[0082] In some embodiments, the rail transit vehicle may convert the power consumption into a travel distance based on a corresponding relationship based on the power consumption of the rail transit vehicle in a preset time period.

[0083] It can be understood that the method provided in the present application takes into account the driving resistance and power consumption ratio of the vehicle rail transit vehicle when determining the driving distance, which can make the driving distance calculation based on power consumption more in line with the actual situation, thereby improving the accuracy of positioning.

[0084] In some embodiments, Figure 4 As shown, the above step S102 can be specifically implemented as: steps S1021-S1023.

[0085] S1021. Determine the influence coefficient corresponding to the influencing factor of the driving distance.

[0086] Among them, the influence coefficient is used to reflect the influence degree of the influencing factors on the driving distance.

[0087] In some embodiments, the influencing factors include at least one of the following: ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, curves of the driving route, cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power.

[0088] It should be noted that as the accumulated mileage of the battery increases, the rail transit vehicle consumes the same amount of electricity and travels a shorter distance. In addition, when other influencing factors are the same but the remaining battery power is different, the travel distance of the rail transit vehicle is also different.

[0089] It can be understood that the influencing factors in the method provided in the present application take into account multiple specific indicators such as ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, curves in the driving route, cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power, which can make the correction of the driving distance of rail transit vehicles (that is, the mileage and travel distance of rail transit vehicles) more accurate, thereby improving the comprehensiveness and adaptability of positioning.

[0090] As an example, a statistical analysis can be performed based on the historical driving data of rail transit vehicles to determine the correlation between various influencing factors and driving distance. For example, the linear correlation between the influencing factors and the driving distance can be quantified based on the Pearson correlation coefficient. For example, if it is found based on the analysis of historical driving data that, under the same other conditions, the driving distance of rail transit vehicles can increase by 0.5% for every 1°C increase in ambient temperature. At this time, an influence coefficient of 0.005 can be set for the ambient temperature.

[0091] As another example, a multivariate linear regression model can be established, in which the driving distance is used as the dependent variable, and the ambient temperature, wind force level, slope of the driving route, etc. are used as independent variables. By fitting the model, the regression coefficient of each independent variable is obtained. These regression coefficients are the influence coefficients of each influencing factor.

[0092] As another example, the corresponding influence coefficients of rail transit vehicles at different track positions may be different, that is, within a certain track range, the influence coefficients corresponding to the influencing factors of rail transit vehicles may be fixed. Therefore, rail transit vehicles can determine the influence coefficients corresponding to the influencing factors within different track ranges based on historical driving data. Subsequently, rail transit vehicles can determine the track range of rail transit vehicles in the track map based on the driving distance within a preset time period, and then determine the influence coefficients corresponding to the influencing factors within the track range.

[0093] In some embodiments, the initial influence coefficient of the influencing factor may be 1, or, if the influence coefficient of a certain influencing factor cannot be determined temporarily, the influence coefficient of the influencing factor may be first defined as 1, that is, the influencing factor has no influence on the driving distance.

[0094] S1022. Correct the driving distance based on the influence coefficient to obtain a corrected driving distance.

[0095] In some embodiments, the corrected driving distance may be the product of the influence coefficient and the driving distance before correction.

[0096] As an example, the influence coefficients of all influencing factors can be multiplied by the driving distance before correction to determine the corrected driving distance. For example, if it is known that the driving distance of the rail transit vehicle in a preset time period (that is, the driving distance before correction) is S, in the current environment of the rail transit vehicle, the influence coefficient of the influencing factor of ambient temperature and humidity is K1, the influence coefficient of the influencing factor of ambient wind direction and wind force level is K2, the influence coefficient of the influencing factor of the slope of the driving route and the influencing factor of the bend of the driving route is K3, the influence coefficient of the influencing factor of the battery cumulative mileage is K4, the influence coefficient of the influencing factor of the traction braking level is K5, the influence coefficient of the influencing factor of the energy consumption of the energy-consuming equipment is K6, and the influence coefficient of the influencing factor of the remaining battery power (that is, the current power of the rail transit vehicle) is K7, then the corrected driving distance S1 can be expressed as the following formula (1):

[0097] Formula (1).

[0098] As another example, the average value of the influence coefficients of all the influence factors may be determined, and then the average value may be multiplied by the driving distance before correction to determine the corrected driving distance.

[0099] It should be noted that the above-mentioned method of correcting the driving distance based on the influence coefficient is only an example given in the embodiment of the present application. In actual implementation, more methods can be selected to correct the driving distance based on the influence coefficient based on different actual conditions and needs. The embodiment of the present application does not limit this.

[0100] S1023. Position the rail transit vehicle based on the corrected travel distance.

[0101] In some embodiments, the location information of the ground transponder is stored in the rail transit vehicle. The rail transit vehicle can determine the location of the rail transit vehicle before the preset time period by determining the ground transponder that the rail transit vehicle passed before the preset time period. Then, based on the corrected driving distance and the location of the rail transit vehicle before the preset time period, the location of the rail transit vehicle after the preset time period can be determined, thereby locating the rail transit vehicle.

[0102] It is understandable that the rail transit vehicle positioning system in the relevant technology, taking rail transit vehicles as an example, usually relies on speed sensors and ground transponders (a ground transponder is arranged at a certain distance on the ground). The ground transponder provides position calibration when the rail transit vehicle passes by, and the speed sensor estimates the travel distance by measuring the real-time speed and travel time of the rail transit vehicle. Then the rail transit vehicle can be positioned based on the information measured by the ground transponder and the speed sensor. Although this positioning method has achieved the positioning of rail transit vehicles to a certain extent, the accuracy of speed sensors and ground transponders is easily affected by various factors, such as sensor accuracy, changes in environmental conditions, etc. Once the speed sensor fails or the ground transponder fails, the accuracy of the rail transit vehicle positioning will be greatly reduced, and may even lead to the complete loss of positioning information.

[0103] The method provided by the present application determines the driving distance by the power consumption, which can reduce the dependence on the speed sensor of the rail transit vehicle and avoid the influence of environmental restrictions or speed sensor failure on the determination of the driving distance. In addition, the method provided by the present application determines the influencing factors that affect the driving distance (such as ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, bends of the driving route, cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, remaining battery power, etc.), and assigns an influence coefficient of the degree of influence on the driving distance to each influencing factor, so as to correct the driving distance, which can make the driving distance more accurate and reliable, thereby improving the accuracy and reliability of positioning of rail transit vehicles.

[0104] In some embodiments, Figure 5 As shown, the above step S1021 can be specifically implemented as: steps S201-S202.

[0105] S201. Determine values ​​of influencing factors within a preset time period based on driving information of rail transit vehicles within a preset time period.

[0106] In some embodiments, the rail transit vehicle can collect the driving information of the rail transit vehicle within a preset time period based on sensors. For example, the sensors of the rail transit vehicle can collect data such as the current ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, curves of the driving route, battery cumulative mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power of the rail transit vehicle, and then determine the value of each influencing factor.

[0107] Exemplarily, the rail transit vehicle sensor may be a temperature sensor, a humidity sensor, a slope sensor, a speed sensor, a wind sensor, and the like.

[0108] S202: Determine the influence coefficient corresponding to the value of the influence factor within a preset time period based on the mapping relationship between the value of the influence factor and the influence coefficient.

[0109] In some embodiments, the rail transit vehicle can establish a mapping relationship database between the values ​​of the influencing factors and the influencing coefficients based on the historical driving data of the rail transit vehicle. Through the mapping relationship database, the influencing coefficient corresponding to the value of the influencing factor within a preset time period can be determined from the mapping relationship.

[0110] In some embodiments, the rail transit vehicle may also train a machine learning model, the input of which is the value of the influencing factor, and the output is the corresponding influencing coefficient, so as to determine the influencing coefficient corresponding to the value of the influencing factor within a preset time period. The machine learning model may be trained based on the historical driving data of the rail transit vehicle and the influencing coefficient corresponding to the historical driving data.

[0111] It is understandable that different values ​​of influencing factors correspond to different influencing coefficients. This application determines the values ​​of influencing factors based on the driving information of rail transit vehicles, which can fully consider the current environment of rail transit vehicles, and then determine the influence coefficients that match the values ​​of influencing factors of the current environment, which can more accurately determine the travel distance of rail transit vehicles, thereby improving positioning accuracy. In addition, different values ​​of influencing factors correspond to different influence coefficients, which can enable the positioning method to adapt to various environmental changes, such as different temperatures, humidity, wind force levels, etc., to ensure the flexibility, stability and reliability of the positioning method under variable conditions.

[0112] In some embodiments, Figure 6 As shown, the above method also includes: steps S301-S302.

[0113] S301. Based on the positioning methods supported by rail transit vehicles, determine update information of a mapping relationship for each positioning method.

[0114] In some embodiments, data provided by each positioning method may be collected, and the data provided by each positioning method may be compared with the actual driving conditions of the rail transit vehicle, thereby determining update information of the mapping relationship for each positioning method.

[0115] S302: Optimize the mapping relationship based on the update information of the mapping relationship of each positioning method.

[0116] In some embodiments, the influence of each positioning method on the mapping relationship can be determined based on the actual driving conditions of rail transit vehicles, and based on technologies such as data fusion, the updated information of each positioning method can be integrated and processed to optimize the mapping relationship.

[0117] It can be understood that the method provided in the present application can continuously optimize the positioning of rail transit vehicles by optimizing the mapping relationship (that is, the influence coefficient corresponding to the numerical value of the influencing factor) according to the positioning method of the rail transit vehicle, so that the rail transit vehicle can adapt to different positioning environments and conditions during positioning, thereby improving the accuracy of positioning.

[0118] In some embodiments, the positioning method includes: a first positioning method and a second positioning method. Among them, the first positioning method (also called the original positioning method) is used to reflect the positioning of the rail transit vehicle based on the vehicle speed sensor and the ground transponder. That is, as described in the above embodiments: the ground transponder provides position calibration when the rail transit vehicle passes by, and the speed sensor estimates the travel distance by measuring the real-time speed and travel time of the rail transit vehicle, and then the rail transit vehicle can be positioned based on the information measured by the ground transponder and the speed sensor. The second positioning method (also called the auxiliary positioning method) is used to reflect the positioning of the rail transit vehicle based on the correspondence between the power consumption and the travel distance of the rail transit vehicle, that is, as described in the above embodiments: based on the correspondence between the power consumption and the travel distance of the rail transit vehicle, the travel distance of the rail transit vehicle is determined, the travel distance is corrected, and then the rail transit vehicle is positioned based on the corrected travel distance.

[0119] It should be noted that the above positioning methods are only examples given in the embodiments of the present application. In actual implementation, there may be more or fewer positioning methods than the above embodiments, and the embodiments of the present application are not limited to this.

[0120] In some embodiments, such as Figure 7 As shown, the above step S302 can be specifically implemented as: steps S401-S404.

[0121] S401: Obtain first update information of a mapping relationship between a first positioning method and a first positioning method.

[0122] In some embodiments, when the original positioning system of the rail transit vehicle is normal, that is, when the rail transit vehicle supports the first positioning method, the first update information of the mapping relationship of the first positioning method can be determined based on the data collected by the ground transponder and the speed sensor. Exemplarily, the actual distance traveled by the rail transit vehicle is calculated by using the position determined when passing through the ground transponder and the distance accumulated by the speed sensor. Since the rail transit vehicle can obtain the travel distance of the rail transit vehicle in real time, it is possible to combine the optimization method of the influence coefficient corresponding to the numerical value of each influencing factor to calibrate and optimize each parameter more accurately.

[0123] In some embodiments, there are various methods for optimizing the influence coefficients corresponding to the values ​​of various influencing factors. The embodiments of the present application briefly illustrate the methods for optimizing the influence coefficients corresponding to the values ​​of some of the influencing factors.

[0124] For example, for the influencing factor of environmental wind direction and wind force level, its corresponding influence coefficient (i.e., its influence on the driving distance) can integrate the influence coefficients corresponding to the values ​​of environmental wind direction and wind force level of rail transit vehicles under different driving conditions of all lines, so as to determine the corresponding relationship between the values ​​of environmental wind direction and wind force level and the influence coefficient of each rail transit vehicle. Then, based on the weight coefficient of each rail transit vehicle, the corresponding relationship between the values ​​of environmental wind direction and wind force level and the influence coefficient of each rail transit vehicle is weighted, so as to determine the final corresponding relationship between the values ​​of environmental wind direction and wind force level and the influence coefficient.

[0125] For example, for the influencing factor of ambient temperature, other influencing factors (ambient wind direction, wind force level, slope of the driving route (select the same location), curves of the driving route (select the same location), cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power) can be kept the same first, and through the comparison of rail transit vehicles on all lines, the influence coefficient corresponding to the numerical value of ambient temperature can be continuously optimized in the verification of multiple rail transit vehicles.

[0126] For example, for the influencing factor of battery cumulative mileage, when the battery cumulative mileage is less than a preset threshold, the influence coefficient of the battery cumulative mileage on the driving distance can be set to 1, and then the influence coefficients corresponding to the values ​​of other influencing factors are optimized first. If the deviation of the driving distance determined based on other influencing factors is large, and the battery cumulative mileage is greater than or equal to the preset threshold, then the influence coefficient of the battery cumulative mileage on the driving distance is optimized when the values ​​of other influencing factors are relatively fixed. For example, the preset threshold can be 30,000 kilometers.

[0127] For example, for the influencing factors of the slope of the driving route and the curve of the driving route, the driving energy consumption of the rail transit vehicle can be theoretically calculated and preliminarily evaluated based on the slope and the curve. Among them, the calculation of the driving energy consumption of the slope is determined by considering the gravity component that the rail transit vehicle needs to overcome due to the height change during driving. For example, when the rail transit vehicle goes uphill, it needs to consume energy to improve the quality of the train; when going downhill, gravity can help the train obtain additional energy. The calculation of the driving energy consumption of the curve is determined by considering the additional energy required by the rail transit vehicle due to the increase in friction resistance at the curve. For example, when the rail transit vehicle is driving on a curve, the path of the outer wheel is longer than the inner wheel, and a greater centripetal force is required to maintain circular motion, which increases friction resistance and energy consumption. According to the above theoretical calculation, an initial influence coefficient can be determined for the slope and the curve, and this initial influence coefficient is used to quantify the influence of the slope and the curve on the driving distance. After determining the initial influence coefficient, these coefficients can be gradually adjusted and optimized through actual driving data when other influencing factors (such as environmental conditions, rail transit vehicle performance, etc.) are relatively stable. By collecting more operating data, the initial impact coefficient can be adjusted more accurately to reflect the real impact of slopes and curves on driving distance during the actual driving of rail transit vehicles.

[0128] For example, for the influencing factor of traction braking level, the relationship between the change in electric quantity and the travel distance corresponding to different traction braking levels can be statistically analyzed on a straight section. Simultaneously, in the subsequent driving process of rail transit vehicles, the influence coefficients corresponding to the traction braking levels of different rail transit vehicles on the straight section can be statistically analyzed to achieve parameter optimization and ensure the accuracy of the parameters.

[0129] S402: Obtain second update information of a mapping relationship between a second positioning method and a second positioning method.

[0130] In some embodiments, based on the second positioning method, the historical data of the power consumption and travel distance of the rail transit vehicle can be determined, and then combined with the optimization method of the influence coefficient corresponding to each influencing factor, each parameter can be calibrated and optimized more accurately to determine the second update information.

[0131] It should be noted that the specific content of the optimization method of the influence coefficient corresponding to the numerical value of each influencing factor can be referred to the specific introduction in the above step S301, and the embodiment of the present application will not be repeated here.

[0132] It can be understood that determining the second update information based on the second positioning method can ensure that the influence coefficient of each influencing factor can maintain accuracy in different operating scenarios, and the influence coefficient can be effectively optimized even when sensor data is unavailable or unreliable.

[0133] S403: Determine a weight coefficient of each positioning method based on the historical update times of the mapping relationship.

[0134] Exemplarily, if the number of historical updates is less than the preset update threshold, the weight coefficient of the first positioning method is 1, and the weight coefficient of the second positioning method is 0, that is, the mapping relationship is optimized based on the first update information. If the number of historical updates is greater than or equal to the preset update threshold, the weight coefficients of the first positioning method and the second positioning method are 0.5 respectively.

[0135] It should be noted that the above method of determining the weight coefficient of each positioning method is only an example given in this application. In actual implementation, the weight coefficients of different positioning methods can be comprehensively determined based on actual conditions, and the embodiments of this application are not limited to this.

[0136] S404: Optimize the mapping relationship based on the first update information, the second update information, and the weight coefficient of each positioning method.

[0137] In some embodiments, based on the first update information and the second update information, it can be determined whether the influence coefficient corresponding to the numerical value of each influencing factor has changed and the specific numerical value of the change, and then based on the weight coefficient, the specific numerical values ​​in the first update information and the second update information are weighted and summed to determine the optimized influence coefficient, thereby optimizing the above-mentioned mapping relationship.

[0138] It is understandable that the method provided by the present application determines the update information of the mapping relationship corresponding to each positioning method based on different positioning methods, determines the final update information of the mapping relationship based on the weight coefficient of each positioning method, and optimizes the mapping relationship. The update information in different positioning methods can be balanced by introducing weight coefficients, thereby enhancing the flexibility and adaptability of the mapping relationship optimization process and improving the accuracy of the mapping relationship optimization. In addition, by combining the two positioning methods, the optimization process of the mapping relationship can more comprehensively consider the various conditions of rail transit vehicle operation, and can ensure high accuracy and reliability of positioning whether real-time data is available or relying on historical data.

[0139] In some embodiments, after determining the above mapping relationship, the following applications can be made based on the above mapping relationship and the corresponding relationship between power consumption and driving distance: First, for the route of rail transit vehicles, the number of ground transponders arranged in the driving interval can be reduced, because the positioning in the driving interval can be relatively loose, and it can accept relatively large errors. At locations such as platforms that require precise parking, corresponding ground transponders are arranged according to relevant rules.

[0140] In some embodiments, if the rail transit vehicle is a Yunba, the driving range is the running range of the Yunba, and its route is relatively fixed. The Yunba is fully automatic driving. At a specific location, the energy consumption of traction and braking levels and auxiliary energy-consuming equipment is also relatively fixed. Therefore, with the increase of mileage and the self-learning of the system, the influence of slope curves, traction and braking levels and the energy consumption of auxiliary energy-consuming equipment on the driving distance tends to be fixed.

[0141] In addition, when positioning based on the first positioning method (also called the original positioning method) and the second positioning method (also called the auxiliary positioning method), the rail transit vehicle can obtain two driving distances (the driving distance accumulated by the original speed sensor and the corrected driving distance corresponding to the power consumption), and the rail transit vehicle can verify each other based on these two driving distances to make the positioning of the rail transit vehicle more accurate. Furthermore, when the rail transit vehicle is traveling at a low speed and the driving distance accumulated by the speed sensor is relatively inaccurate, the corrected driving distance corresponding to the power consumption can be used as a more accurate driving distance of the rail transit vehicle. In the process of optimizing the positioning of the rail transit vehicle, the weight of the driving distance corresponding to the power consumption is amplified. Finally, if the speed sensor fails, at this time, the rail transit vehicle can be assisted in positioning based on the corrected driving distance corresponding to the power consumption, ensuring that the rail transit vehicle can still be positioned normally when the original positioning method of the rail transit vehicle fails, thereby ensuring the reliability of positioning and the safety of the positioning system.

[0142] It is understandable that the method provided in the embodiment of the present application is based on various influencing factors. By self-learning the status of the rail transit vehicle and adding the calibration of the ground transponders at the platform and the entrance and exit positions, it can realize the auxiliary positioning of the rail transit vehicle on the basis of the first positioning method. At the same time, the method provided in the present application can update the position of the rail transit vehicle in the event of a speed sensor failure. The on-board system and the control center equipment can continuously optimize and update the relevant influencing factors through statistics and learning of a large amount of data. At the same time, the present invention can reduce costs and improve positioning accuracy when two positioning systems are present by cooperating with the original positioning system. When any of the two positioning systems fails, positioning can continue to be maintained, thereby improving the reliability of positioning.

[0143] To facilitate understanding, the positioning method of the embodiment of the present application is further described below in the form of examples.

[0144] Example 1: A method for positioning a rail transit vehicle by correcting the travel distance based on various influencing factors. Figure 8 FIG. 1 is a flowchart of the positioning method. Figure 8 As shown, the method can be implemented as the following steps:

[0145] Sa1. When a rail transit vehicle passes a ground transponder, the current position of the rail transit vehicle is determined based on the position of the ground transponder stored in the rail transit vehicle.

[0146] Sa2. Obtain the values ​​of various influencing factors in real time.

[0147] Exemplarily, the influencing factors include at least one of the following: ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, curves of the driving route, cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power.

[0148] Sa3. Determine the driving distance S based on the battery consumption.

[0149] Sa4. Determine the corresponding influence coefficient K1 based on the ambient temperature and ambient humidity.

[0150] Sa5. Determine the corresponding influence coefficient K2 based on the ambient wind direction and wind force level.

[0151] Sa6. Determine the corresponding influence coefficient K3 based on the slope of the driving route and the curve of the driving route.

[0152] Sa7. Determine the corresponding influence coefficient K4 based on the battery's accumulated mileage.

[0153] Sa8. Determine the corresponding influence coefficient K5 based on the traction brake level.

[0154] Sa9. Determine the corresponding influence coefficient K6 based on the energy consumption of the energy-consuming equipment.

[0155] Sa10. Determine a corresponding influence coefficient K7 based on the remaining battery power.

[0156] Sa11. Determine the corrected driving distance S1 based on the influence coefficients corresponding to the above-mentioned influence factors.

[0157] For example, .

[0158] Sa12. Positioning the rail transit vehicle based on the corrected travel distance S1 and the current position of the rail transit vehicle.

[0159] Sa13, end.

[0160] Example 2: Rail transit vehicles support original positioning mode and auxiliary positioning mode, and the influence coefficient optimization process of factors affecting the travel distance. Fig. 9 A flow chart of influence coefficient optimization provided in an embodiment of the present application.

[0161] like Fig. 9 As shown in the figure, the optimization process can be specifically implemented as follows: when the rail transit vehicle starts to locate, if the rail transit vehicle uses the auxiliary positioning method for the first time, the on-board system obtains the initial influence coefficient Kn of each influencing factor on the driving distance based on experiments. If the rail transit vehicle does not use the auxiliary positioning method for the first time, the on-board system obtains the influence coefficient Kn of each influencing factor on the driving distance from the control center.

[0162] The on-board system can continuously optimize and update the corresponding influence coefficient Kn based on the current values ​​of each influencing factor. At the same time, the control center records the influence coefficient Kn of each influencing factor on the driving distance, and outputs the required influence coefficient Kn according to the environment in which each rail transit vehicle is located.

[0163] Next, as an example, the on-board system optimizes the influence coefficient Kn by continuously learning and proposing statistics of probabilistic errors. The control center optimizes and records the influence coefficient Kn by comparing multiple rail transit vehicles. When a rail transit vehicle is newly powered on or a new vehicle is put into operation, the required influence coefficient Kn is output. As another example, rail transit vehicles can verify relevant influencing factors through their own speed sensors, and optimize the influencing factors Kn of specific time and location according to the characteristics of different influencing factors. Finally, the influence coefficient Kn can be continuously optimized by verifying the two positioning methods. Exemplarily, for the first ten thousand times of optimization of each influence coefficient, the verification of the original positioning method shall prevail. When the number of optimizations of each influence coefficient is greater than ten thousand times, the influence coefficient Kn is optimized with the original positioning method and the auxiliary positioning method each accounting for 50% of the weight.

[0164] It is understandable that the method provided by the present application will combine the speed sensor and the ground transponder to update and optimize the influence coefficient of each influencing factor in real time, and achieve self-learning of the relevant influence coefficients through the self-verification of the system. The influence coefficients of the relevant influencing factors can be made accurate through comparison of different rail transit vehicles and verification of multiple data of the rail transit vehicle.

[0165] It should be noted that the above embodiment uses rail transit vehicles as an example to illustrate the positioning method provided by the present application. In actual implementation, the positioning method provided by the embodiment of the present application can also be applied to other types of vehicles besides rail transit vehicles, and the embodiment of the present application does not limit this.

[0166] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the method. It is understandable that, in order to realize the above functions, the positioning device includes at least one of the hardware structure and software modules corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the embodiments of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the embodiments of the present application.

[0167] It is understandable that, in order to realize the above functions, the positioning device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in conjunction with the algorithm steps of each example described in the embodiments of the present application, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0168] The embodiment of the present application can divide the functional modules of the positioning device according to the above method embodiment. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one functional module. The above integrated module can be implemented in the form of hardware or software. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. The following is an example of dividing each functional module corresponding to each function.

[0169] Fig.10 10 is a schematic diagram of the structure of a positioning device provided in an embodiment of the present application. As shown in FIG10 , the positioning device 200 includes: a determination module 201 , a positioning module 202 and an optimization module 203 .

[0170] The determination module 201 is used to predict the travel distance of the rail transit vehicle within the preset time period based on the power consumption of the rail transit vehicle within the preset time period;

[0171] The positioning module 202 is used to locate the rail transit vehicle based on the travel distance.

[0172] In some embodiments, the positioning module 202 is specifically used to determine the influence coefficient corresponding to the influencing factor of the driving distance; the influence coefficient is used to reflect the degree of influence of the influencing factor on the driving distance; based on the influence coefficient, the driving distance is corrected to obtain the corrected driving distance; based on the corrected driving distance, the rail transit vehicle is positioned.

[0173] In some embodiments, the determination module 201 is specifically used to determine the value of the influencing factor within the preset time period based on the driving information of the rail transit vehicle within the preset time period; based on the mapping relationship between the value of the influencing factor and the influence coefficient, determine the influence coefficient corresponding to the value of the influencing factor within the preset time period.

[0174] In some embodiments, the determination module 201 is further used to determine the update information of the mapping relationship for each positioning method based on the positioning methods supported by the rail transit vehicle; the optimization module 203 is used to optimize the mapping relationship based on the update information of the mapping relationship for each positioning method.

[0175] In some embodiments, the positioning method includes: a first positioning method and a second positioning method; the first positioning method is used to reflect that the rail transit vehicle is positioned based on the vehicle speed sensor and the ground transponder; the second positioning method is used to reflect that the rail transit vehicle is positioned based on the correspondence between the power consumption and the travel distance of the rail transit vehicle. The optimization module 203 is specifically used to obtain the first update information of the mapping relationship of the first positioning method; obtain the second update information of the mapping relationship of the second positioning method; determine the weight coefficient of each positioning method based on the historical update times of the mapping relationship; optimize the mapping relationship based on the first update information, the second update information and the weight coefficient of each positioning method.

[0176] In some embodiments, the determination module 201 is specifically used to determine the correspondence between the power consumption and the traveling distance of the rail transit vehicle based on the traveling resistance of the rail transit vehicle and the power consumption ratio of the rail transit vehicle; based on the corresponding relationship and the power consumption of the rail transit vehicle in the preset time period, determine the traveling distance of the rail transit vehicle within the preset time period.

[0177] In some embodiments, the influencing factors include at least one of the following: ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, curves of the driving route, cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power.

[0178] In the case of implementing the functions of the above-mentioned integrated modules in the form of hardware, the embodiments of the present application provide a possible structure of the electronic device involved in the above-mentioned embodiments. Fig.11As shown, the electronic device 300 includes: a processor 302 and a bus 304. Optionally, the electronic device 300 may further include a memory 301; optionally, the electronic device 300 may further include a communication interface 303.

[0179] The processor 302 may be a processor that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of the present application. The processor 302 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of the present application. The processor 302 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0180] The communication interface 303 is used to connect with other devices via a communication network, which may be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0181] The memory 301 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program codes in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0182] As a possible implementation, the memory 301 can exist independently of the processor 302, and the memory 301 can be connected to the processor 302 via a bus 304 to store instructions or program codes. When the processor 302 calls and executes the instructions or program codes stored in the memory 301, the positioning method provided in the embodiment of the present application can be implemented.

[0183] In another possible implementation, the memory 301 may also be integrated with the processor 302 .

[0184] The bus 304 may be an extended industry standard architecture (EISA) bus, etc. The bus 304 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.11 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0185] Some embodiments of the present application provide a computer-readable storage medium (for example, a non-transitory computer-readable storage medium), in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes a positioning method as in any of the above embodiments.

[0186] Exemplarily, the above-mentioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks or tapes, etc.), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards and flash memory devices (e.g., erasable programmable read-only memory (EPROM), cards, sticks or key drives, etc.). The various computer-readable storage media described in this application may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0187] An embodiment of the present application provides a computer program product comprising instructions. When the computer program product is run on a computer, the computer is enabled to execute the positioning method of any one of the above embodiments.

[0188] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A positioning method, characterized in that: The method comprises: Determining the travel distance of the rail transit vehicle within the preset time period based on the power consumption of the rail transit vehicle within the preset time period; Determining the value of the influencing factor within the preset time period based on the driving information of the rail transit vehicle within the preset time period; Based on the mapping relationship between the value of the influencing factor and the influencing coefficient, determining the influencing coefficient corresponding to the value of the influencing factor within the preset time period; the influencing coefficient is used to reflect the degree of influence of the influencing factor on the driving distance; Based on the influence coefficient, correct the driving distance to obtain a corrected driving distance; Positioning the rail transit vehicle based on the corrected travel distance; Based on the positioning methods supported by the rail transit vehicle, determining update information of each positioning method for the mapping relationship; The mapping relationship is optimized based on the update information of the mapping relationship by each positioning method.

2. The method according to claim 1, characterized in that The positioning method includes: a first positioning method and a second positioning method; the first positioning method is used to reflect that the rail transit vehicle is positioned based on a vehicle speed sensor and a ground transponder; the second positioning method is used to reflect that the rail transit vehicle is positioned based on a corresponding relationship between the power consumption and the travel distance of the rail transit vehicle; The optimizing the mapping relationship based on the update information of the mapping relationship by each positioning mode includes: Acquire first update information of the mapping relationship between the first positioning method and the first positioning method; Acquire second update information of the mapping relationship between the second positioning method and the second positioning method; Determining a weight coefficient of each positioning method based on the historical update times of the mapping relationship; The mapping relationship is optimized based on the first update information, the second update information and the weight coefficient of each positioning method.

3. The method according to claim 1, characterized in that: The determining, based on the power consumption of the rail transit vehicle in the preset time period, the travel distance of the rail transit vehicle in the preset time period comprises: Determining a corresponding relationship between the power consumption and the travel distance of the rail transit vehicle based on the travel resistance of the rail transit vehicle and the power consumption ratio of the rail transit vehicle; Based on the corresponding relationship and the power consumption of the rail transit vehicle in the preset time period, the travel distance of the rail transit vehicle in the preset time period is determined.

4. The method according to claim 1, characterized in that The influencing factors include at least one of the following: Ambient temperature, ambient humidity, ambient wind direction, wind force level, slope of the driving route, curves of the driving route, cumulative battery mileage, traction braking level, energy consumption of energy-consuming equipment, and remaining battery power.

5. A positioning device, characterized in that: The device comprises: A determination module, configured to determine a travel distance of the rail transit vehicle within a preset time period based on the power consumption of the rail transit vehicle within the preset time period; The determination module is specifically used to determine the value of the influencing factor within the preset time period based on the driving information of the rail transit vehicle within the preset time period; Based on the mapping relationship between the value of the influencing factor and the influencing coefficient, determining the influencing coefficient corresponding to the value of the influencing factor within the preset time period; the influencing coefficient is used to reflect the degree of influence of the influencing factor on the driving distance; the positioning module is specifically used to correct the driving distance based on the influencing coefficient to obtain the corrected driving distance; Positioning the rail transit vehicle based on the corrected travel distance; The determination module is further used to determine the update information of each positioning method on the mapping relationship based on the positioning methods supported by the rail transit vehicle; The optimization module is used to optimize the mapping relationship based on the update information of the mapping relationship for each positioning method.

6. An electronic device, characterized in that: include: a processor and a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 4.

7. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method according to any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 4.

9. A computer program product, characterized in that The computer program product comprises computer instructions, and when the computer instructions are executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 4.

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