Data processing method, device management system

Abstract

The present specification provides a data processing method and a device management system, wherein the data processing method comprises: determining device environment data and device attribute data of a first mobile device, wherein the device environment data is environment data of a target device environment in which the first mobile device is located, and the device attribute data is data generated by the first mobile device when moving in the target device environment; determining a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data; generating mobile device control data based on the device correction parameter, and controlling a second mobile device to move in the target device environment by using the mobile device control data; and avoiding the problem that environment factors in a special mobile scenario cause a large security risk of a mobile device during movement, thereby reducing the security risk of a mobile device in a special environment.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of computer, in particular to a data processing method. The present specification also relates to an equipment management system, a computing device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] With the continuous development of mobile transportation technology, mobile devices are also applied in more scenarios. However, in special mobile scenarios such as extreme terrain, extreme weather, unknown environment, and the like, these environmental factors can cause a great safety risk of the mobile device during movement. Therefore, how to reduce the safety risk of the mobile device in special environments has become a technical problem to be solved. SUMMARY

[0003] Therefore, the embodiments of the present specification provide a data processing method. One or more embodiments of the present specification also relate to a data processing apparatus, an equipment management system, a computing device, a computer readable storage medium and a computer program product to solve the technical defects in the prior art.

[0004] According to a first aspect of the embodiments of the present specification, a data processing method is provided, comprising:

[0005] determining device environment data and device attribute data of a first mobile device, wherein the device environment data is environment data of a target device environment in which the first mobile device is located, and the device attribute data is data generated by the first mobile device during movement in the target device environment;

[0006] determining a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data;

[0007] generating mobile device control data based on the device correction parameter, and controlling a second mobile device to move in the target device environment by using the mobile device control data.

[0008] According to a second aspect of the embodiments of the present specification, a data processing apparatus is provided, comprising:

[0009] a data determination module configured to determine device environment data and device attribute data of a first mobile device, wherein the device environment data is environment data of a target device environment in which the first mobile device is located, and the device attribute data is data generated by the first mobile device during movement in the target device environment;

[0010] a parameter determination module configured to determine a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data;

[0011] a device control module configured to generate mobile device control data based on the device correction parameter, and control the second mobile device to move in the target device environment by using the mobile device control data.

[0012] According to a third aspect of the embodiments of the present specification, a device management system is provided, comprising a first mobile device, a second mobile device and a device control unit, wherein,

[0013] the first mobile device is configured to determine device environment data corresponding to a target device environment and device attribute data, wherein the first mobile device moves in the target device environment, and the device attribute data is data generated by the first mobile device in the moving process, and send the device environment data and the device attribute data to the device control unit;

[0014] the device control unit is configured to determine device environment data and device attribute data of the first mobile device, wherein the device environment data is environment data of a target device environment where the first mobile device is located, and the device attribute data is data generated by the first mobile device in the moving process in the target device environment, determine a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data, generate mobile device control data based on the device correction parameter, and control the second mobile device to move in the target device environment by using the mobile device control data.

[0015] According to a fourth aspect of the embodiments of the present specification, a computing device is provided, comprising:

[0016] a memory and a processor;

[0017] the memory is configured to store computer programs / instructions, and the processor is configured to execute the computer programs / instructions, which realize the steps of the above data processing method when executed by the processor.

[0018] According to a fifth aspect of the embodiments of the present specification, a computer readable storage medium is provided, which stores computer programs / instructions, which realize the steps of the above data processing method when executed by the processor.

[0019] According to a sixth aspect of the embodiments of the present specification, a computer program product is provided, comprising computer programs / instructions, which realize the steps of the above data processing method when executed by the processor.

[0020] The data processing method provided by one or more embodiments of the present specification can determine device environment data of a target device environment in which a first mobile device is located, and device attribute data generated by the first mobile device moving in the target device environment; determine a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data, and generate mobile device control data based on the device correction parameter, so as to control a second mobile device to move in the target device environment by using the mobile device control data; and thus, the second mobile device is guided to move in the target device environment based on the movement of the first mobile device in the target device environment, and the problem that the environment factors of a special movement scenario can cause a large security risk of the mobile device during movement is avoided, and the security risk of the mobile device in a special environment is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is an application diagram of a data processing method provided by one embodiment of the present specification;

[0022] Figure 2 is a flowchart of a data processing method provided by one embodiment of the present specification;

[0023] Figure 3 is a result diagram of Kalman filtering in a data processing method provided by one embodiment of the present specification;

[0024] Figure 4 is a processing process flowchart of a data processing method provided by one embodiment of the present specification;

[0025] Figure 5 is a structure diagram of a device management system provided by one embodiment of the present specification;

[0026] Figure 6 is a structure diagram of a data processing device provided by one embodiment of the present specification;

[0027] Figure 7 is a structure block diagram of a computing device provided by one embodiment of the present specification. DETAILED DESCRIPTION

[0028] In the following description, many specific details are set forth in order to provide a thorough understanding of the present specification. However, the present specification can be practiced in many different ways beyond the specific embodiments described and it is therefore intended that the present specification not be limited in any way by the description and drawings below.

[0029] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a," "the," and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0030] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0031] In addition, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0032] With the continuous development of mobile transportation technology, mobile devices are also being used in more scenarios; however, in special mobile scenarios such as extreme terrain, extreme weather, and unknown environments, these environmental factors will cause mobile devices to have greater safety risks during the movement process.

[0033] For example, in the field of rail transportation, extreme weather conditions pose a major challenge to driving safety; the following are some specific technical issues:

[0034] 1. Extreme weather affects rail transit safety:

[0035] Weather factors: Extreme weather conditions such as rain, snow, fog, and strong winds can reduce track friction and increase the risk of trains slipping. Especially during typhoon and flood seasons, strong winds and heavy rain can cause unstable train operations and even lead to accidents.

[0036] High-temperature rail expansion: Under high-temperature conditions, the rails may deform due to thermal expansion and contraction, affecting the geometry of the rails and increasing the risk of train derailment.

[0037] Long braking distance: Due to weather influence, the braking distance of the train may become longer, and the options for avoiding in emergency situations are limited, which increases the safety risk.

[0038] Few on-board protections: In extreme weather conditions, if there are many passengers on the train and there are insufficient on-board protection measures, the safety of passengers will be at greater risk in the event of an accident.

[0039] 2. High pressure on rail transit operation in extreme weather:

[0040] Under the premise of ensuring safety, rail transit operation in extreme weather also needs to consider efficiency. For example, in rainy and snowy weather, how to reasonably arrange the train timetable to reduce delays caused by weather, while ensuring the safety of passengers, is a big test for operation management.

[0041] 3. Difficulty in assessing the actual impact of extreme weather on rail transit operation:

[0042] Friction coefficient changes: The friction coefficient between the track and the wheel changes significantly under different weather conditions, which directly affects the acceleration, braking and turning performance of the train; accurately assessing the impact of these changes on the safety of train operation is a technical challenge.

[0043] High-temperature rail expansion assessment: High temperatures can cause the track to expand, affecting the geometric state of the track and the safe operation of the train. How to accurately predict and assess the impact of high temperatures on the track is also a problem that needs to be solved.

[0044] To address the above problems, the present specification provides two solutions; the first solution is a city rail transit train control system based on vehicle-to-vehicle communication, which includes an intelligent train monitoring ITS system, a train management platform TMC, a data communication system DCS, and an intelligent vehicle controller IVOC installed on each train. The ITS system, TMC and IVOC are connected by DCS communication. Among them, the IVOC of all online trains will report the first train operation information to the ITS system and the second train operation information to the TMC according to the preset period, the TMC will send the second train operation information it receives to the ITS system, and the ITS system will determine the following train that needs to be virtually coupled and the corresponding head train according to the first train operation information and / or the second train operation information, and issue a virtual coupling operation instruction to the IVOC of the head train to realize virtual coupling between trains.

[0045] However, this solution has a major flaw: this solution mainly considers the sharing of train position, speed, acceleration, etc. between front and rear trains to achieve virtual coupling and other functions, without considering the inclusion of train environment such as track braking coefficient in the coordination content.

[0046] The first scheme is a track dynamic irregularity real-time acquisition method for a rail transit line section. The track static irregularity varying with the mileage of the line section is acquired by measuring the track of the line section. A vehicle-track dynamics model for the line section is established. The track static irregularity is input into the dynamics model, and the dynamics response of the vehicle and the track is calculated. The track dynamic irregularity is acquired according to the dynamics response of the wheel set and the rail. The track quality index TQI of the line section is acquired, which is used for track dynamic irregularity grading evaluation.

[0047] However, this scheme also has a major defect: the influence of data communication between preceding and following vehicles and abnormal weather on the braking coefficient is not considered.

[0048] Based on this, in the present specification, a data processing method is provided, and the present specification also relates to a data processing device, an equipment management system, a computing device, a computer readable storage medium, and a computer program product, which are described in detail one by one in the following embodiments.

[0049] Referring to Figure 1 , Figure 1 application of the data processing method provided by one embodiment of the present specification is shown, which is based on Figure 1 It can be known that the preceding vehicle can collect real-time driving related vehicle state and track state data, and upload the processed data to the control center through train-ground wireless communication. The control center analyzes and evaluates the vehicle state and the track state based on the vehicle state and the track state data, simulates the future driving state and the risk of each vehicle in the line network, and then sends driving scheduling instructions to the following vehicle based on the system analysis result. The following vehicle adjusts the driving strategy based on the received driving scheduling instructions, so as to realize the auxiliary evaluation of the actual influence of extreme weather on rail transit driving through train-to-train linkage technology, improve the safety and reliability of the system, reduce the accident risk, and ensure the safety of passengers.

[0050] Referring to Figure 2 , Figure 2 a flowchart of the data processing method provided by one embodiment of the present specification is shown, which specifically includes the following steps:

[0051] Step 202: determining the device environment data of the first mobile device and the device attribute data, wherein the device environment data is the environment data of the target device environment where the first mobile device is located, and the device attribute data is the data generated by the first mobile device when moving in the target device environment.

[0052] The mobile device can be understood as a device capable of moving, and can be different in different application scenarios of the data processing method provided in the specification. In the case of application of the data processing method provided in the specification to the rail transit scenario, the mobile device can be a rail transit vehicle, for example, the mobile device can be a train, a subway train, etc. In the case of application of the data processing method provided in the specification to the aviation scenario, the mobile device can be an aircraft, for example, the mobile device can be a drone, an airplane, etc. In the case of application of the data processing method provided in the specification to the waterway transportation scenario, the mobile device can be a water transport vehicle, for example, the mobile device can be a ship, a submarine, etc. In the case of application of the data processing method provided in the specification to the traffic scenario, the mobile device can be a vehicle, for example, the mobile device can be a bus, a self-driving car, etc. That is, the mobile device can be a train, a vehicle, an aircraft, a ship, etc.

[0053] The target device environment can be understood as an environment or area where the mobile device is located, for example, a specific rail area where the train travels, a specific area where the aircraft flies, a specific water area or a specific sea area where the ship travels, etc. It should be noted that the target device environment can be an environment or area where extreme weather occurs, for example, an environment or area where extreme weather such as heavy rain, heavy snow, high temperature, low temperature, etc. occurs. Alternatively, the target device area can be a complex terrain environment, for example, a mountain, a plateau, a snowfield, an environment with high incidence of mudslides / landslides, an environment with dense reefs, etc.

[0054] In one or more embodiments provided in the specification, the mobile device includes at least two, i.e., a first mobile device and a second mobile device. The first mobile device can be understood as any one of the at least two mobile devices. The second mobile device can be understood as one or more other mobile devices in addition to the first mobile device among the at least two mobile devices. For example, the first mobile device can be a first train, and the second mobile device can be a second train. The first train is any one of the at least two trains, and the second train is one or more other trains in addition to the first train among the at least two trains. Moreover, the time when the first mobile device moves to the target device environment can be greater than the time when the second mobile device moves to the target device environment.

[0055] For example, the data processing method provided in the specification is applied to the rail transit scenario, the mobile device is a train, the first mobile device is a front train, the second mobile device is a rear train, and the target device environment is a rail area where extreme weather occurs.

[0056] In order to cope with the extreme weather conditions pose a major challenge to driving safety, the rail transit industry needs to continuously research and develop new technologies, new materials and new methods to improve the safety and reliability of rail transit system in extreme weather conditions. At the same time, strengthening the formulation and drilling of emergency plans, improving the ability to cope with extreme weather, is also an important measure to ensure the safe operation of rail transit. The data processing method provided in the specification can assist in evaluating the actual impact of extreme weather on rail transit driving and the actual vehicle condition of different vehicles through historical data and trend analysis and vehicle-to-vehicle interaction technology, so as to ensure driving safety in extreme weather.

[0057] Based on this, the front vehicle (first mobile device) can be the train that first arrives at the rail area where the extreme weather occurs; the rear vehicle (i.e. the second mobile device) can be understood as the train that arrives at the rail area where the extreme weather occurs later than the front vehicle; in the subsequent processing process, the driving experience of the front vehicle in extreme weather can be used to guide the driving of the rear vehicle in extreme weather and ensure driving safety.

[0058] Among them, the device environment data can be understood as the environment data corresponding to the target device environment, for example, the device environment data includes but is not limited to track section information, track abnormal gap information, track expansion information, track temperature information, coordinate information, terrain information, weather information and / or wind speed, etc. The device environment data can be the device environment data after data preprocessing of the first mobile device, and the data preprocessing can be realized by a data processing algorithm.

[0059] The device attribute data can be understood as the attribute data of the first mobile device during the movement of the first mobile device in the target device environment; the device attribute data includes but is not limited to speed information, acceleration information, load information, vibration information, traction power, braking force and / or braking power, etc. The device attribute data can be the device attribute data after data preprocessing of the first mobile device, and the data preprocessing can be realized by a data processing algorithm.

[0060] In one or more embodiments provided in the specification, at least two types of data acquisition devices (i.e. environment data acquisition device, attribute data acquisition device) can be configured on the mobile device to collect device environment data and device attribute data, so that subsequent mobile device control data can be determined based on the data, and then guide the movement of the second mobile device. The specific implementation is as follows:

[0061] The determination of the device environment data and the device attribute data of the first mobile device comprises:

[0062] receive the device environment data and the device attribute data sent by the first mobile device, wherein the device environment data is obtained by an environment data collection device configured on the first mobile device, and the device attribute data is obtained by processing attribute data to be processed collected by an attribute data collection device configured on the first mobile device using a data processing algorithm.

[0063] The environment data collection device can be understood as a device for collecting device environment data. The environment data collection device can be a sensor or a system for collecting device environment data. For example, the environment data collection device can be an axle positioning system for accurately calculating the position of the train. The environment data collection device can be a temperature sensor for collecting the temperature of the target device environment or the track temperature. The environment data collection device can be a weather detection system for detecting weather information (such as heavy snow, heavy rain, etc.) of the target device environment.

[0064] The attribute data collection device can be understood as a device for collecting device attribute data. The attribute data collection device can be a device attribute data for collecting device attribute data. For example, the environment data collection device includes but is not limited to a speed sensor, an accelerometer, a load meter, a vibration sensor, etc. The speed sensor is used to accurately calculate the speed of the train. The traction power and the braking force / braking power data can be obtained from the power system and the braking system of the train, such as by a sensor. The traction power and the braking force / braking power data are used to analyze the power performance and the braking effect of the train. The driving vibration data is used to monitor the flatness of the track and potential track problems.

[0065] The attribute data to be processed can be understood as attribute data collected by the attribute data collection device, which needs to be optimized, filtered, or screened, etc. Correspondingly, the device attribute data can be data obtained by processing the attribute data to be processed.

[0066] The data processing algorithm can be understood as an algorithm for optimizing, filtering, or screening the attribute data to be processed. For example, the data processing algorithm can be a Kalman filtering algorithm, a K-nearest neighbor algorithm, etc.

[0067] In the above example, in order to assist in evaluating the actual impact of extreme weather on rail transit driving and the actual vehicle condition of different vehicles by using the car-to-car driving technology, thereby guiding the process of rail transit driving, first, the front vehicle needs to collect driving related vehicle state, track state, etc. data in real time.

[0068] Specifically, the front car collects data in real time through various sensors installed on the train, including but not limited to speed sensors, accelerometers, load meters, vibration sensors, axle positioning systems, etc. Speed sensors and axle positioning data are used to accurately calculate the position and speed of the train; traction power and braking force / braking power data are obtained from the train's power system and braking system for analyzing the train's power performance and braking effect. Train vibration data is used to monitor track flatness and potential track problems.

[0069] Secondly, the front car uses Kalman filtering algorithm and K-nearest neighbor algorithm to process real-time data.

[0070] Among them, the K-nearest neighbor (KNN) algorithm is used for classification and regression, which is used here to analyze vehicle number, train vibration peak value, speed, acceleration and load data, so as to identify abnormal gaps between tracks or track expansion problems and other track state data.

[0071] Among them, Kalman filtering is a mathematical algorithm used to estimate the state of a linear dynamic system, which optimally estimates the position, speed, acceleration, traction power and braking force / braking power of the train through sensor data, reducing errors and noise. K-nearest neighbor (KNN) algorithm is used for classification and regression, which is used here to analyze vehicle number, train vibration peak value, speed, acceleration and load data, so as to identify abnormal gaps between tracks or track expansion problems.

[0072] The state value evaluated by the Kalman filtering algorithm is (x position, v speed, a acceleration, p traction power, q braking power, f running resistance, m load, u1 friction correction coefficient of the car, u2 friction correction coefficient of the current section, and other track state data). The input value evaluated by the Kalman filtering algorithm is the measurement of position, speed, power and load by different sensors, as well as the previous survey of the slope b of the current section (positive for uphill). The physical model corresponding to the vehicle is (g is the acceleration of gravity).

[0073] Through the Kalman filtering algorithm, the multi-sensor data can be weighted and fused, and the optimal state estimation value can be calculated. For details, please refer to Figure 3 , Figure 3 is a schematic diagram of the result of Kalman filtering in a data processing method provided by an embodiment of the present specification.

[0074] For example, in the acceleration state or deceleration state, the processing method of a (acceleration) by the Kalman filtering algorithm can be seen from the following formula:

[0075] Acceleration state a = (p*u1*u2 / v – f) / m – g*sin(b)

[0076] Deceleration state a = -(q*u1*u2 / v + f) / m - g*sin(b)

[0077] Finally, the front vehicle uploads the processed data to the control center through the train-ground wireless communication; specifically, the processed data (i.e., device environment data and device attribute data) is uploaded to the control center in real time through the train-ground wireless communication system for further analysis and decision-making.

[0078] Step 204: determining a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data.

[0079] The device correction parameter can be understood as a parameter that can correct the movement state of the second mobile device, for example, the device correction parameter can be device risk data and / or friction correction parameter; when the first mobile device moves in the target device environment, the movement state of the first mobile device has defects due to environmental factors, therefore, the device correction parameter for correcting the device movement defects is needed to guide the subsequent second mobile device to move in the target device environment.

[0080] In one or more embodiments provided in the specification, the data processing method provided in the specification can assist in evaluating the potential risks of extreme weather on rail transit driving through historical data and trend analysis, and train-to-train linkage technology, and the actual vehicle conditions of different vehicles, and guiding the second mobile device to drive safely, and the specific implementation manner is:

[0081] The device correction parameter corresponding to the first mobile device is determined according to the device environment data and the device attribute data, including:

[0082] The first device correction parameter corresponding to the first mobile device is determined by using the device attribute data;

[0083] The associated device data associated with the device environment data is determined from the historical device data, and the second device correction parameter corresponding to the first mobile device is determined based on the device environment data and the associated device data;

[0084] The device risk data corresponding to the first mobile device is determined by using the device environment data and the device attribute data;

[0085] The first device correction parameter, the second device correction parameter, and the device risk data are taken as the device correction parameter corresponding to the first mobile device.

[0086] The first device correction parameter can be understood as a friction correction coefficient of the first mobile device in the target device environment; and the second device correction parameter can be understood as a predicted friction correction coefficient obtained by predicting a movement of the first mobile device in a next device environment of the target device environment.

[0087] The device risk data can be understood as a potential risk of the first mobile device in a next device environment or a next time range of the target device environment, for example, the device risk data can be a risk of track deformation, track wetness, train rollover, etc.; and the device risk data can be understood as risk data obtained by predicting a risk of a movement of the first mobile device in a next device environment of the target device environment.

[0088] The historical device data can be understood as device attribute data and device environment data determined by the mobile device in a historical movement, for example, historical vehicle state information, historical track state data, historical weather data, etc. collected by the train in a historical movement on a predicted track.

[0089] The associated device data can be understood as data in the historical device data that has an association relationship with the device environment data; the association relationship can be a location association relationship; that is, the associated device data can be historical device data determined from the historical device data, in which location information (such as coordinates, track locations, etc.) is associated with location information of the device environment data; for example, the associated device data can be historical track state data determined from historical track state data collected by the train, in which a track location is the same as a track location of the current collected track state data, and / or historical track state data determined from historical track state data collected by the train, in which a track location exceeds a track location of the current collected device environment data, and a difference between the track location of the historical track state data and the track location of the current collected device environment data can be less than or equal to a preset distance range (for example, 5 kilometers, 10 kilometers).

[0090] Alternatively, the associated device data can be understood as data in the historical device data that has an environment association relationship with the device environment data; that is, the associated device data can be historical device data determined from the historical device data, in which environment information (such as weather, temperature, etc.) is the same as or similar to environment information of the device environment data.

[0091] Specifically, the data processing method provided by the present specification can be applied to a device control unit, which can be a control center, a server, a cloud server, a client, a device control system, etc. Each mobile device can be controlled to move through the device control unit.

[0092] Based on this, after receiving the device environment data and the device attribute data sent by the first mobile device, the device control unit can first calculate the first device correction parameter corresponding to the first mobile device using the device attribute data;

[0093] Secondly, the associated device data associated with the device environment data can be determined from the historical device data, which can be historical device data with environmental association and / or location association, and based on the device environment data and the associated device data, the second device correction parameter that the first mobile device may generate in the next device environment is predicted;

[0094] Finally, the device risk data corresponding to the device risk that the first mobile device may occur during the movement is simulated using the device environment data and the device attribute data;

[0095] After the above data determination is completed, the first device correction parameter, the second device correction parameter and the device risk data can be used as the device correction parameter corresponding to the first mobile device.

[0096] In the data processing method provided in the specification, the first device correction parameter corresponding to the first mobile device can be calculated using the device attribute data; for example, the control center uses the received data to evaluate the real-time state of the vehicle, including the calculation of the friction correction coefficient, which is based on the acceleration and deceleration performance of the preceding vehicle on the recent road section, which is obtained based on the device attribute data.

[0097] Based on the location information, weather information and other data in the device environment data, the associated device data similar to the device environment data can be determined from the historical device data, and the second device correction parameter corresponding to the first mobile device can be calculated based on the device environment data and the associated device data.

[0098] And the device risk data corresponding to the first mobile device can be calculated using the device environment data and the device attribute data, so as to realize the calculation and prediction of the potential risk of rail transit driving in extreme weather and the actual vehicle condition of different vehicles.

[0099] In one or more embodiments provided in the specification, the first device correction parameter is a first friction correction coefficient, and the second device correction parameter is a second friction correction coefficient;

[0100] The device attribute data is used to determine the first device correction parameter corresponding to the first mobile device, including:

[0101] input the device attribute data into the trained friction correction coefficient prediction model to obtain the first friction correction coefficient corresponding to the first mobile device; or

[0102] The device friction coefficient of the first mobile device is calculated based on the device attribute data, and the first friction correction coefficient corresponding to the first mobile device is calculated based on the device friction coefficient and a reference friction correction coefficient.

[0103] The second device correction parameter corresponding to the first mobile device is determined based on the device environment data and the associated device data, including:

[0104] The device environment data and the associated device data are input into a device attribute prediction model to obtain predicted device attribute data corresponding to the first mobile device.

[0105] The second friction correction coefficient corresponding to the first mobile device is determined using the predicted device attribute data.

[0106] The friction correction coefficient prediction model can be understood as a model for predicting the friction correction coefficient, which can be a mathematical model, a simulation model, a neural network model, or a deep learning model. For example, the friction correction coefficient prediction model can be a real-time analysis algorithm model, which can be used to analyze and determine the friction correction coefficient of the train in real time.

[0107] The device attribute prediction model can be understood as a model for predicting the moving attribute data of the mobile device in the next time range or distance range, which can be a mathematical model, a simulation model, a neural network model, or a deep learning model. For example, the device attribute prediction model can be a state evaluation model.

[0108] The second friction correction coefficient corresponding to the first mobile device is determined using the predicted device attribute data, which can be referred to the operation corresponding to "determining the first device correction parameter corresponding to the first mobile device using the device attribute data" in the above embodiments, which will not be repeated here.

[0109] Using the above example, the control center analyzes and evaluates the vehicle state and track state based on the real-time uploaded data, and simulates the future driving state and risk of each vehicle in the line network; the specific execution mode is:

[0110] Firstly, the control center uses the received data to evaluate the real-time state of the vehicle, including the calculation of the friction correction coefficient, which is based on the acceleration and deceleration performance of the vehicle on the recent road section, and this can be achieved through the real-time analysis algorithm model of the control center; by inputting the received data into the real-time analysis algorithm model, the friction correction coefficient of the front vehicle (i.e. the first friction correction coefficient) can be obtained.

[0111] It should be noted that the way to calculate the friction correction coefficient of the front vehicle can also be:

[0112] Firstly, the control center determines the v speed, a acceleration, q braking power and other data of the front vehicle from the received data, and recalculates the actual friction coefficient of the front vehicle on the recent road section (i.e. the device friction coefficient);

[0113] Secondly, based on the actual friction coefficient and the reference friction system, the friction correction coefficient of the front vehicle (i.e. the first friction correction coefficient) is calculated, which is used to evaluate the real-time state of the front vehicle.

[0114] Secondly, the control center evaluates the friction correction coefficient of the front road section, which is based on historical data and trend analysis, and the specific way is:

[0115] 1. Based on the current position information of the front vehicle and the current weather information (such as heavy rain weather), the historical vehicle state information and historical track state information (i.e. associated device data) corresponding to the front road section of the front vehicle under heavy rain weather conditions are determined from the historical received vehicle data (i.e. historical device data);

[0116] 2. The current position information, current weather information, historical vehicle state information and historical track state information of the front vehicle are input into the state evaluation model of the control center for prediction, so as to predict the predicted vehicle state information (i.e. predicted device attribute data) of the front vehicle on the front road section;

[0117] 3. Based on the predicted vehicle state information, the friction correction coefficient of the vehicle on the front road section (i.e. the second friction correction coefficient) is estimated.

[0118] In one or more embodiments provided in the specification, the determination of the device risk data corresponding to the first mobile device by using the device environment data and the device attribute data includes:

[0119] Using a simulation algorithm, the first mobile device is simulated for device movement according to the device environment data and the device attribute data, and device movement simulation data of the first mobile device is obtained;

[0120] Based on the device movement simulation data, the device risk data corresponding to the first mobile device is determined.

[0121] The simulation algorithm can be understood as an algorithm for simulating the movement of a mobile device. For example, the simulation algorithm can be a data model, a simulation algorithm, or a dynamic model.

[0122] The device movement simulation data may be understood as device attribute data obtained by simulating the movement of the first mobile device. For example, the device movement model data may be a dynamic response.

[0123] Continuing with the previous example, the control center can use a simulation algorithm to predict the vehicle's driving status and potential risks within the next five minutes, providing a basis for scheduling decisions. The simulation algorithm can be a dynamic model. Based on this, the control center inputs the received data into a pre-established dynamic model to simulate and calculate the dynamic response of the vehicle and track. Then, based on the dynamic response, the control center determines the vehicle's driving status and potential risks within the next five minutes.

[0124] In the above embodiment, the first mobile device is simulated by a simulation algorithm to determine possible risks, thereby guiding the second mobile device to operate safely and avoid possible risks.

[0125] Step 206: Generate mobile device control data based on the device modification parameter, and use the mobile device control data to control the second mobile device to move in the target device environment.

[0126] Mobile device control data can be data that controls the movement of a mobile device; such data can include mobile device control parameters or device movement prompts. For example, the control center determines dispatch information (i.e., mobile device control data) for the following vehicle based on the calculated and evaluated friction correction coefficient, the vehicle's driving status, and potential risks, and transmits the dispatch information to the following vehicle via a driving dispatch instruction.

[0127] Continuing with the above example, the data processing method provided in this manual can realize the actual impact of extreme weather on rail transit operation based on vehicle-to-vehicle linkage assistance; the specific method is to use the vehicle-to-vehicle communication system to collect and analyze real-time data to evaluate the impact of extreme weather conditions on train operation.

[0128] By analyzing the previous train's operating data (such as position, speed, and acceleration) under extreme weather conditions, the current train's operating safety can be predicted. Furthermore, by aggregating historical trend data from multiple trains under similar weather conditions, the overall impact of extreme weather on rail transit operations can be more accurately assessed.

[0129] This evaluation method based on historical data and trend analysis provides important decision support for rail transit operation. It can help the operation management department better understand the specific impact of extreme weather on train safety, so as to take corresponding preventive measures such as adjusting running speed, strengthening maintenance inspection, etc., to ensure the safety of passengers.

[0130] In one or more embodiments provided in the specification, for unmanned trains, the unmanned trains can be controlled to safely travel in extreme weather environments by issuing train strategies in the following manner:

[0131] The mobile device control data is generated based on the device correction parameters, and the second mobile device is controlled to move in the target device environment using the mobile device control data, including:

[0132] Based on the device correction parameters, mobile device control parameters for the second mobile device are generated;

[0133] The mobile device control parameters are sent to the second mobile device to update the local mobile device control strategy using the mobile device control parameters, obtain an updated mobile device control strategy, and move in the target device environment according to the updated mobile device control strategy.

[0134] The mobile device control strategy can be understood as a strategy for controlling the movement of the mobile device; the local mobile device control strategy can be understood as a strategy stored locally by the second mobile device for controlling the movement of the second mobile device, for example, the mobile device control strategy can be a train strategy.

[0135] The mobile device control parameters can be understood as parameters for controlling the movement of the second mobile device, for example, the mobile device control parameters can be parameters such as whether to enter or exit the extreme weather driving mode, and whether to adjust the speed limit and safety distance, etc.

[0136] It should be noted that the mobile device control strategy contains multiple types of mobile device control parameters to control the movement of the second mobile device.

[0137] Continuing with the above example, the control center can issue train dispatch instructions to the rear vehicle based on the system analysis results, where the train dispatch instructions include information such as whether to enter or exit the extreme weather driving mode, and whether to adjust the speed limit and safety distance, etc.

[0138] After the train receives the train dispatching instruction, the train can adjust the train strategy, and the specific mode is as follows: in the case of unmanned driving mode, after the train receives the train dispatching instruction, the train adjusts the parameter information in the train strategy (i.e. mobile device control strategy) according to the dispatching information carried in the train dispatching instruction, such as changing the speed or keeping a larger safety distance, so as to drive more safely according to the train strategy.

[0139] In one or more embodiments provided in the specification, the mobile device control data is generated based on the device correction parameter, and the second mobile device is controlled to move in the target device environment by using the mobile device control data, including:

[0140] Based on the device correction parameter, device movement prompt information for the second mobile device is generated;

[0141] The device movement prompt information is sent to the second mobile device, so that the second mobile device moves in the target device environment according to the device movement prompt information.

[0142] In the above example, after the control center evaluates the vehicle state and track state based on real-time data analysis and simulates the future driving state and risk of each vehicle in the line network, the control center can send a train dispatching instruction to the rear vehicle based on the system analysis result.

[0143] In the case of receiving the dispatching instruction, the rear vehicle adopts manual driving mode, so it can predict the driving result and adjust the driving strategy, such as changing the speed or keeping a larger safety distance; even in the manual driving mode, the system can provide the predicted driving result to assist the driver in making decisions and ensure the safe driving of the train.

[0144] In one or more embodiments provided in the specification, when the train returns to the warehouse, the full amount of data in the entire driving process can be provided to the control center for in-depth analysis and control of the safe driving of the rear vehicle, and the specific implementation mode is as follows:

[0145] The data processing method further includes:

[0146] In the case that the first mobile device moves to the device correction area, the full amount of device environment data and the full amount of device attribute data of the first mobile device are determined, wherein the full amount of device environment data is the environment data of the preset moving path corresponding to the first mobile device, the full amount of device attribute data is the data generated by the first mobile device moving in the preset moving path, and the target device environment is part of the environment in the preset moving path.

[0147] determine a full-device correction parameter corresponding to the first mobile device based on the full-device environment data and the full-device attribute data;

[0148] generate mobile device control data based on the full-device correction parameter, and control the second mobile device to move in the target device environment by using the mobile device control data.

[0149] The device modification area can be understood as an area for performing modification, maintenance, and other modification operations on the mobile device. For example, the device modification area can be a garage, a vehicle depot of a train, a wharf, an airport, or the like.

[0150] The preset movement path can be understood as a movement path of the first mobile device. For example, the preset movement path can be a travel route of a train. The preset movement path of the first mobile device can be the same as the preset movement path of the second mobile device.

[0151] The full-device environment data can be understood as device environment data corresponding to all environments on the entire preset movement path.

[0152] The full-device attribute data can be understood as device attribute data generated by the first mobile device during movement on the entire preset movement path.

[0153] Continuing with the above example, when the train returns to the vehicle depot, all the train data, including the detailed data of this run, are uploaded to the control center for more in-depth analysis. The control center analyzes and evaluates the vehicle state and track state based on the full data, and simulates the future driving state and risks of each vehicle in the network. The specific method is as follows:

[0154] The control center uses the received data to evaluate the real-time state of the vehicle, including the calculation of the friction correction coefficient, which is based on the acceleration and deceleration performance of the vehicle in the recent section.

[0155] The control center also evaluates the friction correction coefficient of the front section, which is based on historical data and trend analysis.

[0156] The control center predicts the driving state and potential risks of the vehicle within the next 5 minutes through simulation algorithms, providing a basis for dispatching decisions.

[0157] Finally, the control center determines the dispatching information for the following train based on the calculated and evaluated friction correction coefficient and the driving state and potential risks of the vehicle, and sends the dispatching information to the following train through the train dispatching instruction, so as to control the following train to drive more safely according to the train strategy.

[0158] In one or more embodiments provided in the specification, after the train returns to the depot, all the train data is uploaded to the front train for more in-depth model training, thereby ensuring the performance of the model, and the specific implementation is as follows:

[0159] After determining the full-quantity device environment data and the full-quantity device attribute data of the first mobile device in the case that the first mobile device moves to the device trimming area, the method further includes:

[0160] Based on the full-quantity device environment data and the full-quantity device attribute data, the friction correction coefficient prediction model, the device attribute prediction model, and the abnormality identification model are optimized to obtain the optimized friction correction coefficient prediction model, the optimized device attribute prediction model, and the optimized abnormality identification model.

[0161] In the above example, the control center updates and optimizes the state evaluation model, the abnormality identification model, and the real-time analysis algorithm model based on the full-quantity data to improve the accuracy and efficiency of the system.

[0162] In one or more embodiments provided in the specification, after determining the full-quantity device environment data and the full-quantity device attribute data of the first mobile device in the case that the first mobile device moves to the device trimming area, the method further includes:

[0163] The full-quantity device environment data and the full-quantity device attribute data are identified by the abnormality identification model to obtain the abnormality identification result corresponding to the first mobile device.

[0164] Based on the abnormality identification result, the first mobile device is trimmed.

[0165] In the above example, the data processing method provided in the specification can assist in evaluating the actual vehicle conditions of different vehicles based on train-to-train cooperation. Through real-time monitoring and analysis of the running state of the vehicle, especially the key parameters such as wheel wear and brake system aging, through comparison of these parameters of different vehicles, potential safety hazards can be found in time, and corresponding maintenance measures can be taken. For example, when the wheel wear of a train is determined to exceed the safety standard through the genetic identification model, the system will immediately issue a warning to prompt the need for replacement or repair; similarly, when the brake system of a train is aging or the performance is declining, the system will also issue a warning in time to prevent potential brake failure accidents.

[0166] This real-time monitoring and evaluation method based on train-to-train cooperation provides important technical support for the maintenance and safety management of rail transit vehicles. It can help the vehicle maintenance team to more accurately diagnose the actual vehicle conditions of the vehicle, find problems in time and take corresponding maintenance measures, thereby ensuring the safe operation of the vehicle.

[0167] The data processing method provided by one or more embodiments of the present specification can determine device environment data of a target device environment in which a first mobile device is located, and device attribute data generated by movement of the first mobile device in the target device environment; determine a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data, and generate mobile device control data based on the device correction parameter, so as to control movement of a second mobile device in the target device environment by using the mobile device control data; and thus, the movement of the second mobile device in the target device environment is guided based on the movement of the first mobile device in the target device environment, and the problem that a large safety risk exists in the movement of the mobile device due to environmental factors in a special movement scenario is avoided, and the safety risk of the mobile device in a special environment is reduced.

[0168] The following description is made in conjunction with the accompanying Figure 4 The data processing method provided by the present specification is further described by taking an application of the data processing method in a rail transit scenario as an example. In the application, the data processing method is used to guide the movement of a second mobile device in a target device environment based on the movement of a first mobile device in the target device environment. Figure 4 A process flow diagram of a data processing method provided by one embodiment of the present specification is shown, and the data processing method includes the following steps:

[0169] Step 402: The front vehicle collects data.

[0170] Specifically, the front vehicle collecting data can be understood as the front vehicle collecting data related to the state of the vehicle, the state of the track, and the like in real time, and the specific data collection process is as follows: the front vehicle collects data in real time through various sensors installed on the train.

[0171] The sensors include but are not limited to a speed sensor, an accelerometer, a load meter, a vibration sensor, an axle positioning system, and the like. The speed data collected by the speed sensor is used to accurately calculate the speed of the train;

[0172] The axle positioning data collected by the axle positioning system is used to accurately calculate the position of the train;

[0173] The traction power, braking force, and braking power data are obtained from the power system and the braking system of the train through the sensors, and are used to analyze the power performance and braking effect of the train;

[0174] The driving vibration data collected by the vibration sensor is used to monitor the flatness of the track and potential track problems.

[0175] The vehicle load data collected by the load meter is used to calculate the friction correction parameter.

[0176] Step 404: The front vehicle preprocesses the data.

[0177] Specifically, the front vehicle preprocessing data can be understood as the front vehicle using Kalman filtering algorithm and K-nearest neighbor algorithm to process the real-time collected data.

[0178] Kalman filtering is a mathematical algorithm for estimating the state of a linear dynamic system; through Kalman filtering algorithm, the data collected by sensors are processed, so as to make optimal estimation of the position, speed, acceleration, traction power and braking power of the train, and reduce errors and noises.

[0179] Among them, the state value evaluated by Kalman filtering algorithm is (x position, v speed, a acceleration, p traction power, q braking power, f running resistance, m load, u1 friction correction coefficient of the vehicle, u2 friction correction coefficient of the current section, etc. Track state data). The input value evaluated by Kalman filtering algorithm is the measurement of position, speed, power and load by different sensors, and the previous survey of the current section slope b (positive for uphill). The physical model corresponding to the vehicle is (g is the acceleration of gravity).

[0180] Through Kalman filtering algorithm, the multi-sensor data can be weighted and fused, and the optimal state estimation value can be calculated

[0181] For example, in the acceleration state or deceleration state, the processing method of Kalman filtering algorithm for a (acceleration) can be seen from the following formula:

[0182] Acceleration state a = (p*u1*u2 / v – f) / m – g*sin(b)

[0183] Deceleration state a = -(q*u1*u2 / v + f) / m – g*sin(b)

[0184] K-nearest neighbor (K-Nearest Neighbors, KNN) algorithm is used for classification and regression, which is used here to analyze vehicle number, peak value of driving vibration, speed, acceleration and load data, so as to identify the abnormal gap or track expansion problem between tracks and other track state data.

[0185] Step 406: the front vehicle uploads the key data in real time.

[0186] Specifically, the front vehicle uploads the key data in real time, which can be understood as that the front vehicle uploads the preprocessed data to the control center in real time through the train-ground wireless communication system, so as to make further analysis and decision.

[0187] Step 408: the control center analyzes the data in real time.

[0188] Specifically, the control center analyzes the data in real time, which can be understood as the control center evaluating the vehicle state and track state based on real-time data analysis, and simulating the future driving state and risk of each vehicle in the line network; The specific execution steps are:

[0189] 1. The control center uses the received data to evaluate the real-time state of the vehicle, including the calculation of the friction correction coefficient, which is based on the acceleration and deceleration performance of the vehicle in the last section, and the specific way can be realized through the real-time analysis algorithm model of the control center; By inputting the received data into the real-time analysis algorithm model, the friction correction coefficient of the front vehicle can be obtained.

[0190] It should be noted that the way to calculate the friction correction coefficient of the front vehicle can also be:

[0191] First, the control center determines the v speed, a acceleration, q brake power and other data of the front vehicle from the received data, and recalculates the actual friction coefficient of the front vehicle in the last section;

[0192] Second, based on the actual friction coefficient and the reference friction system, the friction correction coefficient of the front vehicle is calculated to evaluate the real-time state of the front vehicle.

[0193] 2. The control center evaluates the friction correction coefficient of the front section, which is based on historical data and trend analysis, and the specific way is:

[0194] First, based on the current position information of the front vehicle and the current weather information (such as heavy rain weather), the historical vehicle state information and historical track state information corresponding to the front section of the front vehicle under heavy rain weather conditions are determined from the historical received vehicle data;

[0195] Second, the current position information, current weather information, historical vehicle state information and historical track state information of the front vehicle are input into the state evaluation model of the control center for prediction, so as to predict the predicted vehicle state information of the front vehicle in the front section;

[0196] Finally, based on the predicted vehicle state information, the friction correction coefficient of the vehicle in the front section is estimated;

[0197] The calculation method of the friction correction coefficient can refer to the content corresponding to step 1 above.

[0198] 3. The control center predicts the driving state and potential risk of the vehicle within the next 5 minutes through a simulation algorithm, which provides a basis for dispatching decisions.

[0199] Among them, the simulation algorithm can be a dynamics model, based on which the driving state and potential risk are determined through the simulation algorithm in the following way:

[0200] Firstly, the control center inputs the received data into a pre-established dynamic model to simulate the dynamic response of the vehicle and the track;

[0201] Secondly, based on the dynamic response, the driving state and potential risks of the vehicle within the next 5 minutes are determined.

[0202] Step 410: Send dispatch assistance prompts.

[0203] Specifically, sending dispatch assistance prompts can be understood as the dispatch personnel of the control center issuing driving dispatch instructions based on the system analysis results.

[0204] Among them, the driving dispatch instructions include whether to enter or exit the extreme weather driving mode, whether to adjust the speed limit and safety distance, and other information.

[0205] Step 412: Adjust the driving strategy of the rear vehicle.

[0206] Specifically, adjusting the driving strategy of the rear vehicle can be understood as the rear vehicle adjusting the driving strategy according to the received driving dispatch instructions.

[0207] It should be noted that in the case of unmanned driving mode of the train, after the rear vehicle receives the driving dispatch instructions, it adjusts its driving strategy according to the dispatch information carried in the driving dispatch instructions, such as changing the speed or maintaining a larger safety distance.

[0208] In the case of manual driving mode of the front vehicle, after the rear vehicle receives the driving dispatch instructions, it can display the predicted driving result to the driver to assist the driver in making decisions.

[0209] The above steps are to upload the vehicle state data and track state data to the control center for analysis in real time during the driving of the front vehicle; while in the case of returning to the garage or arriving at the vehicle depot, the full amount of data of this driving can be uploaded to the control center for in-depth analysis, which can be referred to the following steps 414 and 416.

[0210] Step 414: The front vehicle uploads full amount of data when returning to the garage.

[0211] Specifically, the front vehicle uploading full amount of data when returning to the garage can be understood as uploading full amount of driving data to the control center after the front vehicle returns to the garage or the vehicle depot.

[0212] The full amount of driving data includes all detailed data of this operation, which is used for more in-depth analysis and model training.

[0213] Step 416: The control center trains and updates the model.

[0214] Specifically, the control center trains the update model, which can be understood as the control center updating and optimizing the state evaluation model, the anomaly identification model, and the real-time analysis algorithm model based on the full-quantity data to improve the accuracy and efficiency of the system.

[0215] In addition, after receiving the full-quantity data, the control center can also perform the operation corresponding to step 408 based on the full-quantity data, that is, based on the full-quantity data, the vehicle state and the track state are analyzed and evaluated, and the future driving state and risk of each vehicle in the line network are simulated, thereby guiding the following vehicle to drive in extreme weather.

[0216] Furthermore, after receiving the full-quantity data, the control center can also perform anomaly identification on the vehicle based on the full-quantity data, and the specific method is: inputting the full-quantity data into the anomaly identification model for anomaly identification, thereby determining whether the vehicle has an abnormal fault problem.

[0217] For example, when the wheel wear of a train exceeds the safety standard, the system will immediately issue an alarm to prompt the need for replacement or repair. Similarly, when the braking system of a train is aging or its performance is declining, the system will also issue an alarm in time to prevent potential brake failure accidents.

[0218] This real-time monitoring and evaluation method based on vehicle-to-vehicle interaction provides important technical support for the maintenance and safety management of rail transit vehicles. It can help the vehicle maintenance team more accurately diagnose the actual vehicle condition, timely find problems and take corresponding maintenance measures, thereby ensuring the safe operation of the vehicle.

[0219] Based on the above steps, the data processing method provided by the present specification provides a real-time data analysis and evaluation method based on vehicle-to-vehicle interaction and a vehicle-to-vehicle interaction rail transit extreme weather dispatching auxiliary system, which can assist in evaluating the actual impact of extreme weather on rail transit driving and the actual condition of different vehicles, based on historical data to evaluate the friction correction coefficient and other driving state data of vehicles and tracks in extreme weather such as rain and snow, and share data and adjust driving strategies among the front vehicle, the control center, and the rear vehicle; This provides important technical support for rail transit operation and safety management, helps to improve the safety and reliability of the system, reduces the risk of accidents, and ensures the safety of passengers.

[0220] In addition, the evaluation method based on historical data and trend analysis in the data processing method provides important decision support for rail transit operation. It can help the operation and management department better understand the specific impact of extreme weather on driving safety, so as to take corresponding preventive measures such as adjusting the running speed and strengthening maintenance inspection to ensure the safety of passengers.

[0221] The data processing method based on real-time monitoring and evaluation of vehicle-to-vehicle interaction provides important technical support for the maintenance and safety management of rail transit vehicles. It can help the vehicle maintenance team to more accurately diagnose the actual vehicle condition, find problems in time and take corresponding maintenance measures, so as to ensure the safe operation of the vehicle.

[0222] Referring to Figure 5 The present specification also provides an equipment management system embodiment, Figure 5 A structural schematic diagram of an equipment management system provided by an embodiment of the present specification is shown. As Figure 5 shown, the system includes a first mobile device 502, a second mobile device 504, and an equipment control unit 506, wherein,

[0223] The first mobile device 502 is configured to determine equipment environment data corresponding to a target equipment environment and equipment attribute data, wherein the first mobile device 502 moves in the target equipment environment, and the equipment attribute data is data generated by the first mobile device 502 during movement. The equipment environment data and the equipment attribute data are sent to the equipment control unit 506;

[0224] The equipment control unit 506 is configured to determine the equipment environment data and the equipment attribute data of the first mobile device 502, wherein the equipment environment data is the environment data of the target equipment environment in which the first mobile device 502 is located, and the equipment attribute data is the data generated by the first mobile device 502 during movement in the target equipment environment. According to the equipment environment data and the equipment attribute data, the equipment correction parameter corresponding to the first mobile device 502 is determined, the mobile device control data is generated based on the equipment correction parameter, and the second mobile device 504 is controlled to move in the target equipment environment by using the mobile device control data.

[0225] Optionally, the first mobile device is further configured to:

[0226] acquire the equipment environment data by using an environment data acquisition device, and collect the to-be-processed attribute data by using an attribute data acquisition device, wherein the environment data acquisition device and the attribute data acquisition device are configured in the first mobile device;

[0227] process the to-be-processed attribute data by using a data processing algorithm to obtain the equipment attribute data, wherein the data processing algorithm includes a Kalman filtering algorithm and a K-nearest neighbor algorithm.

[0228] The device management unit of the device management system provided by one or more embodiments of the present specification can determine device environment data of a target device environment in which a first mobile device is located, and device attribute data generated by movement of the first mobile device in the target device environment; determine a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data, and generate mobile device control data based on the device correction parameter, so as to control movement of a second mobile device in the target device environment by using the mobile device control data; and thus, movement of the second mobile device in the target device environment is guided based on the movement of the first mobile device in the target device environment, and the problem that a large security risk exists in the movement of the mobile device due to environmental factors in a special movement scenario is avoided, and the security risk of the mobile device in a special environment is reduced.

[0229] The above is a schematic scheme of the device management system of the present embodiment. It should be noted that the technical scheme of the device management system and the technical scheme of the above data processing method belong to the same concept, and the details of the technical scheme of the device management system that are not described in detail can be referred to the description of the technical scheme of the data processing method.

[0230] Corresponding to the above method embodiments, the present specification also provides data processing device embodiments, Figure 6 A structural schematic diagram of a data processing device provided by one embodiment of the present specification is shown. As shown in the figure, Figure 6 The device includes:

[0231] The data determination module 602 is configured to determine device environment data of a first mobile device and device attribute data, wherein the device environment data is environment data of a target device environment in which the first mobile device is located, and the device attribute data is data generated by movement of the first mobile device in the target device environment;

[0232] The parameter determination module 604 is configured to determine a device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data.

[0233] The device control module 606 is configured to generate mobile device control data based on the device correction parameter, and control movement of a second mobile device in the target device environment by using the mobile device control data.

[0234] Optionally, the data determination module 602 is further configured to:

[0235] receive the device environment data and the device attribute data sent by the first mobile device, wherein the device environment data is obtained by an environment data collection device configured on the first mobile device, and the device attribute data is obtained by processing attribute data to be processed collected by an attribute data collection device configured on the first mobile device using a data processing algorithm.

[0236] Optionally, the parameter determination module 604 is further configured to:

[0237] determine the first device correction parameter corresponding to the first mobile device by using the device attribute data;

[0238] determine associated device data associated with the device environment data from historical device data, and determine the second device correction parameter corresponding to the first mobile device based on the device environment data and the associated device data;

[0239] determine the device risk data corresponding to the first mobile device by using the device environment data and the device attribute data;

[0240] use the first device correction parameter, the second device correction parameter, and the device risk data as the device correction parameter corresponding to the first mobile device.

[0241] Optionally, the first device correction parameter is a first friction correction coefficient, and the second device correction parameter is a second friction correction coefficient.

[0242] The parameter determination module 604 is further configured to:

[0243] input the device attribute data into a trained friction correction coefficient prediction model to obtain the first friction correction coefficient corresponding to the first mobile device; or

[0244] calculate the device friction coefficient of the first mobile device based on the device attribute data, and calculate the first friction correction coefficient corresponding to the first mobile device based on the device friction coefficient and a reference friction coefficient.

[0245] The parameter determination module 604 is further configured to:

[0246] input the device environment data and the associated device data into a device attribute prediction model to obtain predicted device attribute data corresponding to the first mobile device;

[0247] determine the second friction correction coefficient corresponding to the first mobile device by using the predicted device attribute data.

[0248] Optionally, the parameter determination module 604 is further configured to:

[0249] simulate device movement of the first mobile device according to the device environment data and the device attribute data by using a simulation algorithm, to obtain device movement simulation data of the first mobile device;

[0250] determine the device risk data corresponding to the first mobile device based on the device movement simulation data.

[0251] Optionally, the device control module 606 is further configured to:

[0252] generate mobile device control parameters for the second mobile device based on the device correction parameter;

[0253] send the mobile device control parameters to the second mobile device, so that the second mobile device updates a local mobile device control strategy by using the mobile device control parameters, to obtain an updated mobile device control strategy, and moves in the target device environment according to the updated mobile device control strategy.

[0254] Optionally, the device control module 606 is further configured to:

[0255] generate device movement prompt information for the second mobile device based on the device correction parameter;

[0256] send the device movement prompt information to the second mobile device, so that the second mobile device moves in the target device environment according to the device movement prompt information.

[0257] Optionally, the data processing apparatus further comprises a full-amount data processing module configured to:

[0258] in a case where the first mobile device moves to a device modification area, determine full-amount device environment data and full-amount device attribute data of the first mobile device, wherein the full-amount device environment data is environment data of a preset movement path corresponding to the first mobile device, the full-amount device attribute data is data generated by movement of the first mobile device in the preset movement path, and the target device environment is part of the environment in the preset movement path;

[0259] determine a full-amount device correction parameter corresponding to the first mobile device based on the full-amount device environment data and the full-amount device attribute data.

[0260] Generate mobile device control data based on the full-amount device correction parameter, and control the second mobile device to move in the target device environment by using the mobile device control data.

[0261] Optionally, the data processing apparatus further comprises a model optimization module configured to:

[0262] Optimize the friction correction coefficient prediction model, the device attribute prediction model and the anomaly identification model based on the full-amount device environment data and the full-amount device attribute data, to obtain the optimized friction correction coefficient prediction model, the optimized device attribute prediction model and the optimized anomaly identification model.

[0263] Optionally, the data processing apparatus further comprises an anomaly identification module configured to:

[0264] Anomaly identification is performed on the full-amount device environment data and the full-amount device attribute data by using the anomaly identification model, to obtain the anomaly identification result corresponding to the first mobile device.

[0265] Based on the anomaly identification result, the first mobile device is modified.

[0266] Optionally, the time for the first mobile device to move to the target device environment is greater than the time for the second mobile device to move to the target device environment, the first mobile device is a first train, and the second mobile device is a second train.

[0267] The data processing apparatus provided by one or more embodiments of the present specification can determine the device environment data of the target device environment in which the first mobile device is located, and the device attribute data generated by the first mobile device moving in the target device environment; determine the device correction parameter corresponding to the first mobile device according to the device environment data and the device attribute data, and generate mobile device control data based on the device correction parameter, so as to control the second mobile device to move in the target device environment by using the mobile device control data; the first mobile device in the target device environment is guided to move in the target device environment, and the problem that the environment factors of the special moving scenario will cause a large safety risk of the mobile device during the moving process is avoided, and the safety risk of the mobile device in the special environment is reduced.

[0268] The above is a schematic scheme of the data processing apparatus of the present embodiment. It should be noted that the technical scheme of the data processing apparatus and the technical scheme of the data processing method described above belong to the same concept, and the details of the technical scheme of the data processing apparatus which are not described in detail can be referred to the description of the technical scheme of the data processing method.

[0269] Figure 7 A structural block diagram of a computing device 700 is shown, according to an embodiment of the present specification. The components of the computing device 700 include, but are not limited to, a memory 710 and a processor 720. The processor 720 is connected with the memory 710 through a bus 730, and a database 750 is used to save data.

[0270] The computing device 700 also includes an access device 740 that enables the computing device 700 to communicate via one or more networks 760. Examples of these networks include the public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or combinations of such networks, such as the Internet. The access device 740 can include one or more of any type of network interface (for example, a network interface card (NIC)), wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0271] In an embodiment of the present specification, the above-mentioned components of the computing device 700 and other components not shown in the Figure 7 may be connected with each other, for example, through a bus. It should be understood that Figure 7 The structural block diagram of the computing device shown is only for the purpose of example, and is not a limitation on the scope of the present specification. Other components can be added or replaced as needed by those skilled in the art.

[0272] The computing device 700 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 700 can also be a mobile or stationary server.

[0273] The processor 720 implements the steps of the data processing method when executing the computer program / instructions.

[0274] The above is a schematic solution of the computing device of the embodiment. It should be noted that the technical solution of the computing device and the technical solution of the data processing method belong to the same concept, and the details of the technical solution of the computing device that are not described in detail can be referred to the description of the technical solution of the data processing method.

[0275] An embodiment of the present specification further provides a computer readable storage medium storing computer programs / instructions, which are executed by a processor to implement the steps of the data processing method as described above.

[0276] The above is a schematic solution of the computer readable storage medium of the embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the data processing method belong to the same concept, and the details of the technical solution of the storage medium that are not described in detail can be referred to the description of the technical solution of the data processing method.

[0277] An embodiment of the present specification further provides a computer program product comprising computer programs / instructions, which are executed by a processor to implement the steps of the data processing method as described above.

[0278] The above is a schematic solution of the computer program product of the embodiment. It should be noted that the technical solution of the computer program product and the technical solution of the data processing method belong to the same concept, and the details of the technical solution of the computer program product that are not described in detail can be referred to the description of the technical solution of the data processing method.

[0279] The above-described embodiments of the application have several aspects, no single one of which is solely responsible for the application's desirable attributes. Without limiting the scope of the application as expressed by the claims which follow, some further embodiments make these aspects even more useful. Other embodiments can result in less desirable attributes.

[0280] The computer program / instructions can include a computer program code, which can be in a form of source code, object code, executable file, or some intermediate form etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution package, etc.

[0281] It should be noted that, for the foregoing method embodiments, the acts described can be performed in a different order from that described, and the methodology disclosed can be implemented by hardware, software, firmware, middleware or a combination of hardware and software and firmware or software and middleware etc. Further, it should be noted that, the acts described in the specification can be implemented by electronic hardware, computer software, or a combination of computer software and hardware, but the disclosure is not limited thereto. In some cases, the acts described in the claims are not necessarily performed in the order described in the specification.

[0282] In the above embodiments, the description of each embodiment is focused on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0283] The preferred embodiments of the present specification disclosed above are only used to help explain the present specification. The alternative embodiments do not describe all the details and do not limit the application to the specific embodiments described. Obviously, according to the content of the present specification, many modifications and changes can be made. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present specification, so that those skilled in the art can well understand and utilize the present specification. The present specification is limited only by the claims and their full scope and equivalents.

Claims

1. A data processing method, characterized in that: include: Determining device environment data and device attribute data of the first mobile device, wherein the device environment data is environment data of a target device environment in which the first mobile device is located, and the device attribute data is data generated when the first mobile device moves in the target device environment; Determine a first device correction parameter corresponding to the first mobile device using the device attribute data; determine associated device data associated with the device environment data from historical device data, and determine a second device correction parameter corresponding to the first mobile device based on the device environment data and the associated device data; determine device risk data corresponding to the first mobile device using the device environment data and the device attribute data; use the first device correction parameter, the second device correction parameter, and the device risk data as device correction parameters corresponding to the first mobile device, wherein the first device correction parameter is a friction correction parameter of the first mobile device in the target device environment, and the second device correction parameter is a predicted friction correction parameter of the first mobile device in the target device environment, and the device correction parameters are used to correct the movement state of the second mobile device; Mobile device control data is generated based on the device modification parameter, and the mobile device control data is used to control the second mobile device to move in the target device environment.

2. The data processing method according to claim 1, wherein: The determining of the device environment data and device attribute data of the first mobile device includes: Receive the device environment data and the device attribute data sent by the first mobile device, wherein the device environment data is obtained through the environment data acquisition device configured on the first mobile device, and the device attribute data is obtained by processing the attribute data to be processed collected by the attribute data acquisition device configured on the first mobile device using a data processing algorithm.

3. The data processing method according to claim 1, wherein: The first device correction parameter is a first friction correction coefficient, and the second device correction parameter is a second friction correction coefficient; The determining, by using the device attribute data, a first device correction parameter corresponding to the first mobile device includes: inputting the device attribute data into a trained friction correction coefficient prediction model to obtain the first friction correction coefficient corresponding to the first mobile device; or Calculating a device friction coefficient of the first mobile device based on the device attribute data, and calculating the first friction correction coefficient corresponding to the first mobile device based on the device friction coefficient and a reference friction coefficient; The determining, based on the device environment data and the associated device data, a second device correction parameter corresponding to the first mobile device includes: Inputting the device environment data and the associated device data into a device attribute prediction model to obtain predicted device attribute data corresponding to the first mobile device; The second friction correction coefficient corresponding to the first mobile device is determined using the predicted device attribute data.

4. The data processing method according to claim 1, wherein: The determining device risk data corresponding to the first mobile device by using the device environment data and the device attribute data includes: Using a simulation algorithm, performing device movement simulation on the first mobile device according to the device environment data and the device attribute data, to obtain device movement simulation data of the first mobile device; The device risk data corresponding to the first mobile device is determined based on the device movement simulation data.

5. The data processing method according to claim 1, wherein: Generating mobile device control data based on the device correction parameter, and controlling the second mobile device to move in the target device environment using the mobile device control data, includes: generating a mobile device control parameter for the second mobile device based on the device modification parameter; The mobile device control parameters are sent to the second mobile device, so that the second mobile device updates the local mobile device control policy using the mobile device control parameters, obtains the updated mobile device control policy, and moves in the target device environment according to the updated mobile device control policy.

6. The data processing method according to claim 1, wherein: Generating mobile device control data based on the device correction parameter, and controlling the second mobile device to move in the target device environment using the mobile device control data, includes: generating device movement prompt information for the second mobile device based on the device correction parameter; The device movement prompt information is sent to the second mobile device, so that the second mobile device moves in the target device environment according to the device movement prompt information.

7. The data processing method according to claim 1, wherein: Also includes: When the first mobile device moves to the device repair area, determining full device environment data and full device attribute data of the first mobile device, wherein the full device environment data is environment data corresponding to a preset movement path of the first mobile device, the full device attribute data is data generated when the first mobile device moves within the preset movement path, and the target device environment is a partial environment within the preset movement path; determining, based on the full device environment data and the full device attribute data, a full device correction parameter corresponding to the first mobile device; Mobile device control data is generated based on the full device correction parameter, and the mobile device control data is used to control the second mobile device to move in the target device environment.

8. The data processing method according to claim 7, characterized in that: After determining the full device environment data and full device attribute data of the first mobile device when the first mobile device moves to the device repair area, the method further includes: Based on the full amount of equipment environment data and the full amount of equipment attribute data, the friction correction coefficient prediction model, the equipment attribute prediction model and the abnormality recognition model are optimized to obtain an optimized friction correction coefficient prediction model, an optimized equipment attribute prediction model and an optimized abnormality recognition model.

9. The data processing method according to claim 7, characterized in that: After determining the full device environment data and full device attribute data of the first mobile device when the first mobile device moves to the device repair area, the method further includes: Using an anomaly recognition model to perform anomaly recognition on the full device environment data and the full device attribute data, to obtain an anomaly recognition result corresponding to the first mobile device; Based on the abnormality identification result, the first mobile device is repaired.

10. The data processing method according to any one of claims 1 to 9, characterized in that: The time it takes for the first mobile device to move to the target device environment is greater than the time it takes for the second mobile device to move to the target device environment. The first mobile device is a first train, and the second mobile device is a second train.

11. A device management system, characterized in that: The device comprises a first mobile device, a second mobile device and a device control unit, wherein: a first mobile device configured to determine device environment data and device attribute data corresponding to a target device environment, wherein the first mobile device moves in the target device environment and the device attribute data is data generated by the first mobile device during movement, and send the device environment data and the device attribute data to a device control unit; The device control unit is configured to determine device environment data and device attribute data of a first mobile device, wherein the device environment data is environment data of a target device environment in which the first mobile device is located, and the device attribute data is data generated by the first mobile device when moving in the target device environment; determine a first device correction parameter corresponding to the first mobile device using the device attribute data; determine associated device data associated with the device environment data from historical device data, and determine a second device correction parameter corresponding to the first mobile device based on the device environment data and the associated device data; determine device risk data corresponding to the first mobile device using the device environment data and the device attribute data; use the first device correction parameter, the second device correction parameter, and the device risk data as device correction parameters corresponding to the first mobile device, generate mobile device control data based on the device correction parameters, and use the mobile device control data to control the second mobile device to move in the target device environment, wherein the first device correction parameter is a friction correction parameter of the first mobile device in the target device environment, and the second device correction parameter is a predicted friction correction parameter of the first mobile device in the target device environment, and the device correction parameters are used to correct the movement state of the second mobile device.

12. The equipment management system according to claim 11, characterized in that: The first mobile device is further configured to Acquiring the device environment data using an environment data acquisition device, and acquiring the attribute data to be processed using an attribute data acquisition device, wherein the environment data acquisition device and the attribute data acquisition device are configured on the first mobile device; The attribute data to be processed is processed using a data processing algorithm to obtain the device attribute data, wherein the data processing algorithm includes a Kalman filter algorithm and a K-nearest neighbor algorithm.

13. A computing device, characterized in that include: memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions. When the computer program / instructions are executed by the processor, the steps of the data processing method according to any one of claims 1 to 10 are implemented.

14. A computer-readable storage medium storing a computer program / instruction, characterized in that: When the computer program / instruction is executed by a processor, the steps of the data processing method according to any one of claims 1 to 10 are implemented.

15. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the data processing method according to any one of claims 1 to 10 are implemented.

Images