A remote control vehicle emergency management method and system

Abstract

The present invention is applicable to the technical field of emergency management of remote-controlled vehicles, and provides an emergency management method and system for remote-controlled driving vehicles. When receiving a real-time emergency event alarm of a target remote-controlled passenger elevator car, the alarm time and the risk prediction value of the target remote-controlled passenger elevator car in responding to the real-time emergency event are obtained, and it is judged whether the real-time emergency event belongs to a predetermined emergency category. Accurate evaluation of tire performance is achieved, so that the initial risk prediction value generated by the emergency event is more accurately corrected. This process not only improves the system's sensitivity and response speed to changes in tire performance, but also ensures that under complex or harsh environmental conditions, the system can dynamically adjust the risk assessment results based on the latest tire grip data. The corrected risk prediction value can more realistically reflect the current operating status of the vehicle, making the automatic driving system more reliable when judging whether it is necessary to transfer control to the driver.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote control vehicle emergency management, and in particular relates to an emergency management method and system for a remote control driving vehicle. Background Art

[0002] As an important equipment for airport ground services, airport passenger elevators are responsible for guiding passengers from the terminal to the aircraft on the apron. With the development of automation and remote control technology, remote-controlled passenger elevators have gradually become an important part of modern airport operations. Remote control technology not only improves operational flexibility, but also reduces dependence on manual driving. Especially in complex environments and busy periods, remote control can effectively improve operational efficiency and reduce the risk of manual misoperation. However, the application of remote control also brings new challenges, especially when dealing with severe weather conditions (such as rain and snow), which puts higher requirements on the grip and overall performance of vehicle tires.

[0003] Existing remote control passenger elevator systems usually rely on preset risk assessment values ​​to make decisions when dealing with special emergency situations such as rainy and snowy weather. However, this assessment method often lacks real-time feedback on the recent status of the tire and cannot accurately reflect the dynamic performance of the tire in complex environments. Especially when the tire grip changes greatly, such as on slippery roads or snow-covered conditions, the existing system cannot adjust the original risk prediction value in time, resulting in possible misjudgment. This situation is extremely unfavorable in emergency management, because the inability to accurately evaluate tire performance may cause the system to react overly conservatively or too aggressively, which in turn affects the operational safety of the passenger elevator. Summary of the invention

[0004] The purpose of the present invention is to provide an emergency management method and system for remote control driving of a vehicle, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: a method for emergency management of a remotely controlled vehicle, the method comprising:

[0006] When receiving a real-time emergency event alarm of a target remote-controlled passenger elevator vehicle, obtaining the alarm time and the risk prediction value of the target remote-controlled passenger elevator vehicle in responding to the real-time emergency event, and determining whether the real-time emergency event belongs to a predetermined emergency category;

[0007] If it is determined that the real-time emergency event belongs to the predetermined emergency category, the real-time grip data, the real-time environmental condition data and the historical operation log of the target remote-controlled passenger elevator vehicle are obtained, and the historical operation records consistent with the real-time environmental condition data in the historical operation log are retrieved;

[0008] Obtain the historical operation time of each historical operation record and the historical tire grip data of the corresponding target remote-controlled passenger elevator vehicle, and generate a grip data analysis line graph based on the historical operation time and the historical tire grip data corresponding to the historical operation time and the real-time tire grip data corresponding to the alarm time;

[0009] Analyze each inflection point of the grip data analysis line graph, and determine whether the grip data analysis line graph meets the tire performance optimization determination standard. If it is determined that the tire performance optimization determination standard is met, generate a correction slope according to the grip data analysis line graph;

[0010] The risk prediction value is adjusted according to the correction slope to obtain a corrected risk prediction value, the safety threshold corresponding to the predetermined emergency category is obtained, and the corrected risk prediction value is compared with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the predetermined emergency category is a snowy day category or a rainy day category. When the predetermined emergency category is a snowy day category, the real-time environmental condition data should include snow thickness, humidity and road friction coefficient; when the predetermined emergency category is a rainy day category, the real-time environmental condition data should include ambient temperature and road slipperiness.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, if it is determined that the real-time emergency event belongs to a predetermined emergency category, the steps of obtaining the real-time grip data, real-time environmental condition data and historical operation log of the target remote-controlled passenger elevator vehicle, and retrieving the historical operation record consistent with the real-time environmental condition data in the historical operation log include:

[0013] After determining that the real-time emergency event belongs to a predetermined emergency category, obtaining real-time grip data, real-time environmental condition data, and historical operation logs of the target remote-controlled passenger elevator vehicle;

[0014] Retrieve the historical operation log of the target remote-controlled passenger elevator and filter out the historical operation records with the same category as the real-time emergency event;

[0015] The historical operation records screened out initially are screened a second time according to the real-time environmental condition data of the target remote-controlled passenger elevator car to obtain historical operation records that are consistent with the real-time environmental condition data.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the grip data analysis line graph is an XY axis line graph, wherein the X axis represents time, the Y axis represents tire grip data, and the time interval between each punctuation point on the X axis is the same.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, the step of analyzing each inflection point of the grip data analysis line graph and determining whether the grip data analysis line graph meets the tire performance optimization judgment standard, and if it is determined that the tire performance optimization judgment standard is met, then generating a correction slope according to the grip data analysis line graph includes:

[0018] Obtain the occurrence time of each inflection point of the grip data analysis line graph, and determine whether a tire-related maintenance event of the target remote-controlled passenger elevator vehicle has occurred within a preset period before each occurrence time based on historical operation records;

[0019] If it is determined that a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before the occurrence time exceeding the preset ratio, it is determined that the grip data analysis line graph meets the tire performance optimization determination standard;

[0020] After determining that the grip data analysis line graph meets the tire performance optimization determination criteria, a correction slope is generated based on the grip data analysis line graph.

[0021] As a further limitation of the technical solution of the embodiment of the present invention, after determining that the grip data analysis line graph meets the tire performance optimization determination standard, the step of generating a correction slope according to the grip data analysis line graph includes:

[0022] After determining that the grip data analysis line graph meets the tire performance optimization determination standard, obtaining a local line graph between the last two inflection points in the grip data analysis line graph;

[0023] Calculate the slope of the local line graph and set it as the correction slope.

[0024] As a further limitation of the technical solution of the embodiment of the present invention, the steps of adjusting the risk prediction value according to the correction slope to obtain a corrected risk prediction value, obtaining a safety threshold corresponding to a predetermined emergency category, and comparing the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right include:

[0025] Multiply the risk prediction value by the correction slope to obtain the corrected risk prediction value;

[0026] Obtaining a safety threshold corresponding to a predetermined emergency category, and comparing the modified risk prediction value with the safety threshold corresponding to the predetermined emergency category;

[0027] When the modified risk prediction value is greater than or equal to the safety threshold corresponding to the predetermined emergency category, it is determined that the autonomous driving right should be transferred to the driver;

[0028] When the corrected risk prediction value is less than the safety threshold, it is determined that there is no need to transfer the autonomous driving rights.

[0029] As a further limitation of the technical solution of the embodiment of the present invention, an emergency management system for remote control driving of a vehicle, the system comprises: a first data acquisition module, a second data acquisition module, a line graph generation module, a correction slope generation module and a risk prediction value adjustment module, wherein:

[0030] A first data acquisition module is used to obtain the alarm time and the risk prediction value of the target remote-controlled passenger elevator vehicle in responding to the real-time emergency event when receiving the real-time emergency event alarm of the target remote-controlled passenger elevator vehicle, and determine whether the real-time emergency event belongs to a predetermined emergency category;

[0031] The predetermined emergency category is a snowy day category or a rainy day category. When the predetermined emergency category is a snowy day category, the real-time environmental condition data should include snow thickness, humidity and road friction coefficient; when the predetermined emergency category is a rainy day category, the real-time environmental condition data should include ambient temperature and road slipperiness;

[0032] A second data acquisition module is used to acquire the real-time grip data, real-time environmental condition data and historical operation log of the target remote-controlled passenger elevator vehicle if it is determined that the real-time emergency event belongs to a predetermined emergency category, and retrieve the historical operation record consistent with the real-time environmental condition data in the historical operation log;

[0033] A line graph generation module is used to obtain the historical operation time of each historical operation record and the historical tire grip data of the corresponding target remote-controlled passenger elevator vehicle, and generate a grip data analysis line graph based on the historical operation time and the historical tire grip data corresponding to the historical operation time and the real-time tire grip data corresponding to the alarm time;

[0034] The grip data analysis line graph is an XY axis line graph, wherein the X axis represents time, the Y axis represents tire grip data, and the time interval between each punctuation point on the X axis is the same;

[0035] A correction slope generation module is used to analyze each inflection point of the grip data analysis line graph and determine whether the grip data analysis line graph meets the tire performance optimization judgment standard. If it is determined that it meets the tire performance optimization judgment standard, a correction slope is generated according to the grip data analysis line graph;

[0036] The risk prediction value adjustment module is used to adjust the risk prediction value according to the correction slope to obtain a corrected risk prediction value, obtain the safety threshold corresponding to the predetermined emergency category, and compare the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right.

[0037] As a further limitation of the technical solution of the embodiment of the present invention, the second data acquisition module specifically includes:

[0038] A data acquisition unit, for acquiring real-time grip data, real-time environmental condition data, and historical operation logs of a target remote-controlled passenger elevator vehicle after determining that the real-time emergency event belongs to a predetermined emergency category;

[0039] A first operation log retrieval unit is used to retrieve the historical operation log of the target remote-controlled passenger elevator vehicle and filter out the historical operation records with the same category as the real-time emergency event;

[0040] The second operation log retrieval unit is used to perform a secondary screening on the historical operation records screened out initially according to the real-time environmental condition data of the target remote-controlled passenger elevator vehicle, and obtain the historical operation records consistent with the real-time environmental condition data.

[0041] As a further limitation of the technical solution of the embodiment of the present invention, the correction slope generation module specifically includes:

[0042] A maintenance event judgment unit is used to obtain the occurrence time of each inflection point of the grip data analysis line graph, and judge whether a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before each occurrence time according to historical operation records;

[0043] a standard compliance judgment unit, for judging that the grip data analysis line graph meets the tire performance optimization judgment standard if it is determined that a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before the occurrence time exceeding the preset ratio;

[0044] The correction slope generating unit is used to generate a correction slope according to the grip data analysis line graph after determining that the grip data analysis line graph meets the tire performance optimization determination standard.

[0045] Compared with the prior art, the present invention obtains the alarm time and the risk prediction value of the target remote-controlled passenger elevator car in dealing with the real-time emergency event when receiving the real-time emergency event alarm of the target remote-controlled passenger elevator car, and determines whether the real-time emergency event belongs to a predetermined emergency category; if it is determined that the real-time emergency event belongs to the predetermined emergency category, the real-time grip data, real-time environmental condition data and historical operation log of the target remote-controlled passenger elevator car are obtained, and the historical operation records consistent with the real-time environmental condition data in the historical operation log are retrieved; the historical operation time of each historical operation record and its corresponding historical tire grip data of the target remote-controlled passenger elevator car are obtained, and the historical operation records consistent with the real-time environmental condition data are retrieved according to the historical operation log. The system generates a grip data analysis line graph based on historical operating time and its corresponding historical tire grip data, as well as real-time tire grip data corresponding to the alarm time; analyzes each inflection point of the grip data analysis line graph, and determines whether the grip data analysis line graph meets the tire performance optimization judgment standard; if it is determined to meet the tire performance optimization judgment standard, generates a correction slope based on the grip data analysis line graph; adjusts the risk prediction value based on the correction slope to obtain a corrected risk prediction value, obtains the safety threshold corresponding to the predetermined emergency category, and compares the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right.

[0046] The accurate evaluation of tire performance is achieved, so that the initial risk prediction value generated by the emergency event can be corrected more accurately. This process not only improves the system's sensitivity and response speed to changes in tire performance, but also ensures that under complex or harsh environmental conditions, the system can dynamically adjust the risk assessment results based on the latest tire grip data. The corrected risk prediction value can more realistically reflect the current operating status of the vehicle, making the autonomous driving system more reliable when judging whether to transfer control to the driver. By combining real-time data with historical operating data, the system can not only identify subtle changes in tire performance, but also accurately adjust risk assessment by generating a correction slope. The rationality of this method lies in that it comprehensively considers the current actual situation and the tire performance in similar situations in the past, forming a closed-loop feedback mechanism. In addition, this method not only improves the safety of the system, but also reduces misjudgments caused by overly conservative or overly aggressive risk assessments, and optimizes the decision-making process for the transfer of autonomous driving rights. This refined evaluation method enables the autonomous driving system to make more accurate judgments at critical moments, significantly improving the stability of the system and the safety of operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0048] Figure 2A flowchart of retrieving historical operation records consistent with real-time environmental condition data in a historical operation log in a method provided by an embodiment of the present invention;

[0049] Figure 3 A flow chart of analyzing the grip data analysis line graph in the method provided in an embodiment of the present invention to determine whether the line graph meets the tire performance optimization criteria;

[0050] Figure 4 A flow chart of generating a correction slope according to a line graph of grip data analysis in a method provided by an embodiment of the present invention;

[0051] Figure 5 A flow chart of determining whether to transfer the autonomous driving right in the method provided in an embodiment of the present invention;

[0052] Figure 6 An application architecture diagram of a system provided by an embodiment of the present invention;

[0053] Figure 7 A structural block diagram of a second data acquisition module in a system provided by an embodiment of the present invention;

[0054] Figure 8 This is a structural block diagram of a line graph generation module in a system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0057] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0058] Specifically, a remote control vehicle emergency management method comprises the following steps:

[0059] Step S100, when receiving a real-time emergency event alarm of a target remote-controlled passenger elevator, obtain the alarm time and the risk prediction value of the target remote-controlled passenger elevator in dealing with the real-time emergency event, and determine whether the real-time emergency event belongs to a predetermined emergency category.

[0060] The scheduled emergency category is the snowy day category or the rainy day category. When the scheduled emergency category is the snowy day category, the real-time environmental condition data should include snow thickness, humidity and road friction coefficient; when the scheduled emergency category is the rainy day category, the real-time environmental condition data should include ambient temperature and road slipperiness.

[0061] In the embodiment of the present invention, when the monitoring system of the target remote-controlled passenger elevator vehicle detects a potential risk, a real-time emergency event alarm will be automatically generated. These real-time emergency event alarms are generated by sensors and detection equipment installed on the vehicle. When key indicators such as tire grip, ambient temperature, humidity, etc. exceed the preset safety threshold, the system will immediately trigger an alarm to remind the operator or the autonomous driving system of possible danger.

[0062] The risk prediction value is generated by analyzing real-time data based on existing technologies. These data include the vehicle's grip, environmental conditions (such as temperature and humidity), and the vehicle's operating status. The algorithm combines these factors to calculate a quantitative risk value, which is used to evaluate the safety of passenger elevator operation in the current environment and as a basis for determining whether emergency measures need to be taken.

[0063] Once an alarm is triggered, the system automatically records and sends the alarm event to the control center or the operator's interface. The operator can obtain detailed alarm information through the interface, including the trigger time, risk prediction value and related environmental condition data, which helps to quickly judge the emergency situation and decide whether to transfer the autonomous driving right or take other measures.

[0064] Furthermore, the emergency management method for the remote control driving vehicle further comprises the following steps:

[0065] Step S200: If it is determined that the real-time emergency event belongs to a predetermined emergency category, the real-time grip data, real-time environmental condition data and historical operation log of the target remote-controlled passenger elevator are obtained, and the historical operation records consistent with the real-time environmental condition data in the historical operation log are retrieved.

[0066] Specifically, Figure 2 A flow chart for retrieving historical operation records in a historical operation log that are consistent with real-time environmental condition data is shown.

[0067] If it is determined that the real-time emergency event belongs to the predetermined emergency category, the real-time grip data, real-time environmental condition data and historical operation log of the target remote-controlled passenger elevator are obtained, and the historical operation records consistent with the real-time environmental condition data in the historical operation log are retrieved. Specifically, the following steps are included:

[0068] Step S201, after determining that the real-time emergency event belongs to a predetermined emergency category, obtaining real-time grip data, real-time environmental condition data, and historical operation logs of the target remote-controlled passenger elevator vehicle;

[0069] Step S202, searching the historical operation log of the target remote-controlled passenger elevator, and filtering out the historical operation records with the same category as the real-time emergency event;

[0070] Step S203, performing a secondary screening on the historical operation records screened out initially according to the real-time environmental condition data of the target remote-controlled passenger elevator vehicle, and obtaining historical operation records consistent with the real-time environmental condition data.

[0071] In the embodiment of the present invention, the real-time grip data is usually obtained by tire grip sensors installed on the tires of the target remote control passenger elevator vehicle. These sensors can monitor the friction between the tires and the ground in real time and transmit the data to the vehicle-mounted system so that the system can instantly evaluate the grip of the tires during driving.

[0072] Historical operation logs are record files automatically generated by the target remote control passenger elevator during operation. These logs capture and store various operation data in real time through the on-board recording system, usually including the vehicle's operating time, speed, path, tire grip data, environmental conditions (such as temperature, humidity, road friction coefficient), and any abnormal or maintenance events that occurred. Historical operation logs provide the system with a detailed archive of past operating status, which is convenient for backtracking and analysis when needed.

[0073] When obtaining historical operation records that are consistent with real-time environmental condition data, the system does not require that these data be exactly the same, but allows the data to fluctuate within a certain range. For example, taking the snowy day category as an example, assuming that the snow thickness in the current real-time environmental condition data is 15 cm, the system can filter out records in the historical records with a snow thickness between 12 and 18 cm. Similarly, if the current humidity is 85%, humidity between 80% and 90% in the historical records can be considered to meet the conditions. This permission for data fluctuations within a certain range is to better match historical operating conditions with current real-time conditions, thereby providing a more accurate risk assessment.

[0074] Furthermore, the emergency management method for the remote control driving vehicle further comprises the following steps:

[0075] Step S300, obtaining the historical operation time of each historical operation record and its corresponding historical tire grip data of the target remote-controlled passenger elevator, and generating a grip data analysis line graph based on these historical operation times and their corresponding historical tire grip data, as well as the real-time tire grip data corresponding to the alarm time.

[0076] The grip data analysis line graph is an XY axis line graph, wherein the X axis represents time, the Y axis represents tire grip data, and the time interval between each punctuation point on the X axis is the same.

[0077] In the embodiment of the present invention, by integrating historical and real-time data, the system plots the grip change trajectory on a line graph, thereby intuitively analyzing the trend of tire grip. This graph is used to evaluate the stability and safety of tire performance, providing a key basis for further risk assessment and driving decision-making.

[0078] Furthermore, the emergency management method for the remote control driving vehicle further comprises the following steps:

[0079] Step S400, analyzing each inflection point of the grip data analysis line graph, and determining whether the grip data analysis line graph meets the tire performance optimization judgment standard. If it is determined to meet the tire performance optimization judgment standard, a correction slope is generated according to the grip data analysis line graph.

[0080] Specifically, Figure 3 A flow chart showing the analysis of grip data and the line graph to determine whether the tire performance optimization is met.

[0081] The process of analyzing each inflection point of the grip data analysis line graph and determining whether the grip data analysis line graph meets the tire performance optimization judgment standard, and if it is determined that the tire performance optimization judgment standard is met, generating a correction slope according to the grip data analysis line graph specifically includes the following steps:

[0082] Step S401, obtaining the occurrence time of each inflection point of the grip data analysis line graph, and judging whether a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before each occurrence time according to the historical operation records;

[0083] Step S402, if it is determined that a tire maintenance event related to the target remote-controlled passenger elevator vehicle occurs within a preset period before the occurrence time exceeding the preset ratio, it is determined that the grip data analysis line graph meets the tire performance optimization determination standard;

[0084] Step S403, after determining that the grip data analysis line graph meets the tire performance optimization determination standard, generating a correction slope according to the grip data analysis line graph.

[0085] In the embodiment of the present invention, the preset ratio between the inflection point occurrence time and the tire-related maintenance event is set based on empirical data and statistical analysis. In the long-term operation process, by analyzing the historical operation data of the target remote-controlled passenger elevator, the correlation between tire performance optimization and maintenance events can be found. The occurrence of the inflection point usually indicates that the tire grip has changed significantly, which may be directly related to the previous tire maintenance or replacement and other related events.

[0086] The preset ratio is set based on the following reasons and basis:

[0087] a Statistical correlation: Historical data analysis shows that before a certain percentage of inflection points occur, there are usually tire-related maintenance events. This indicates that these maintenance events have a significant impact on tire performance, thereby affecting the change in grip data. Therefore, it is reasonable to use this correlation as a criterion for evaluating tire performance optimization.

[0088] b Maintenance effectiveness verification: By setting a preset ratio, the effectiveness of tire-related maintenance can be verified. If there are related maintenance events before most of the inflection points occur, it means that these maintenance are effective in improving tire performance. Therefore, setting a reasonable ratio can help the system identify effective maintenance operations and determine whether the grip data reflects good tire performance optimization.

[0089] Exceeding the preset ratio means that in most cases, the change in grip data (i.e. the occurrence of inflection points) is closely related to tire-related maintenance events. This close correlation shows that the effect of tire performance optimization is reflected in the grip data. If the inflection points detected by the system are highly consistent with maintenance events within the preset ratio, it means that tire maintenance has indeed improved tire performance and is clearly reflected in the data.

[0090] Therefore, when there is a tire-related maintenance event before the inflection point that exceeds the preset ratio, it can be reasonably determined that the grip data analysis line graph meets the tire performance optimization judgment standard. This means that the change in grip data is not only the result of random fluctuations, but is more likely to be a direct reflection of the performance improvement brought about by the maintenance event, thereby increasing confidence in tire performance optimization and judgment accuracy.

[0091] This verification solution can effectively identify and verify the actual impact of tire maintenance on performance optimization by combining the inflection points of the grip data line graph with tire-related maintenance events. By setting a preset ratio and judging the correlation between the inflection points and maintenance events, the system can more accurately evaluate the effectiveness of maintenance operations, thereby avoiding unnecessary maintenance, saving resources and improving operational efficiency.

[0092] In addition, this solution enhances the system's ability to judge tire performance optimization. When the inflection point that exceeds the preset ratio is associated with a maintenance event, the system can more confidently determine that the grip data reflects the actual performance improvement. This verification method helps to improve the accuracy and reliability of the entire technical solution, ensuring that the right decision is made at critical moments.

[0093] Overall, the verification scheme provides a data-driven evaluation mechanism for the technical solution, so that the grip data analysis is not limited to simple trend observation, but also can be effectively fed back and verified through actual maintenance records. This closed-loop feedback mechanism strengthens the system's self-correction ability and helps to continuously optimize the safety and performance of the autonomous driving system.

[0094] Specifically, Figure 4 A flow chart for generating a correction slope based on a line graph of grip data analysis is shown.

[0095] After determining that the grip data analysis line graph meets the tire performance optimization determination standard, generating a correction slope according to the grip data analysis line graph specifically includes the following steps:

[0096] Step S4031, after determining that the grip data analysis line graph meets the tire performance optimization determination standard, obtaining a local line graph between the last two inflection points in the grip data analysis line graph;

[0097] Step S4032, calculate the slope of the local line graph and set the slope as the correction slope.

[0098] In an embodiment of the present invention, the slope of the local broken line graph between the last two inflection points is selected as the correction slope based on the consideration of accurately capturing and feeding back the latest dynamic changes in tire performance. In grip data analysis, inflection points usually represent significant change points in grip data, reflecting the turning point or adjustment of tire performance. The local broken line graph between the last two inflection points can reflect the changing trend of tire performance in the recent period of time, especially the grip performance after maintenance or adjustment. Therefore, calculating the slope of this local broken line graph can provide a dynamic correction parameter that is closest to the current tire state, ensuring that the adjustment of the risk prediction value is more accurate.

[0099] This driving method is based on the "local sensitivity" analysis of grip data changes. Local sensitivity means that the system has a high response sensitivity to the latest changes in grip data, especially in emergency management, this sensitivity can help the system quickly adapt to the latest conditions of tire performance. By paying attention to the slope between the last two inflection points, the system can reflect the trend of grip data over the past period of time in the current risk prediction, thereby avoiding inaccurate decisions based on outdated data. In addition, the slope of the local line graph reflects the "instantaneous change rate" of tire grip to a certain extent, and this parameter is crucial for evaluating future grip change trends.

[0100] For example, in snowy conditions, if the last two inflection points appear after the vehicle passes through thick snow and slippery roads, the slope of the local broken line graph can reflect the improvement or deterioration of the tire's grip during this period. If the slope is large, it means that the tire's grip has been significantly enhanced. The system can reduce the risk prediction value by correcting the slope, thereby reducing unnecessary control intervention. If the slope is small or even negative, it means that the tire performance may be deteriorating. The system should increase the risk prediction value to take countermeasures in advance. This analysis method ensures that the risk prediction is highly consistent with the real-time status of the tire performance, improving the decision-making accuracy and safety of the autonomous driving system.

[0101] Furthermore, the emergency management method for the remote control driving vehicle further comprises the following steps:

[0102] Step S500, adjust the risk prediction value according to the correction slope to obtain a corrected risk prediction value, obtain the safety threshold corresponding to the predetermined emergency category, and compare the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right.

[0103] Specifically, Figure 5 A flowchart for determining whether to transfer the autonomous driving authority is shown.

[0104] Among them, adjusting the risk prediction value according to the correction slope to obtain a corrected risk prediction value, obtaining a safety threshold corresponding to a predetermined emergency category, and comparing the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right specifically includes the following steps:

[0105] Step S501, multiplying the risk prediction value by the correction slope to obtain a corrected risk prediction value;

[0106] Step S502, obtaining a safety threshold corresponding to a predetermined emergency category, and comparing the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category;

[0107] Step S503, when the corrected risk prediction value is greater than or equal to the safety threshold corresponding to the predetermined emergency category, it is determined that the automatic driving right should be transferred to the driver;

[0108] Step S504: when the corrected risk prediction value is less than the safety threshold, it is determined that there is no need to transfer the automatic driving right.

[0109] In the embodiment of the present invention, the method of multiplying the risk prediction value by the correction slope to obtain the corrected risk prediction value is based on the dynamic adjustment and precise calibration of the current risk state. The corrected slope reflects the latest trend of tire performance, so multiplying it with the original risk prediction value can more accurately reflect the current actual risk level. If the slope is large, it means that the tire grip has been greatly improved, and the system reduces the overall risk assessment value through the product. Conversely, if the slope is small or negative, it means that the grip performance has deteriorated, and the product increases the risk assessment value.

[0110] The logic behind this method is the concept of "proportional adjustment". The correction slope, as a dynamic adjustment factor, can effectively correct the deviation of the original risk prediction value, making the final corrected risk prediction value closer to the actual vehicle status. Through product operations, the system can keenly capture the slight changes in tire grip data and adjust the assessment of potential risks accordingly. This method ensures a high degree of consistency between risk prediction and the current status of the vehicle.

[0111] The basis for comparing the revised risk prediction value with the safety threshold corresponding to the predetermined emergency category is mainly to ensure the safety and stability of vehicle operation. The safety threshold is set based on a large amount of historical data, environmental conditions and vehicle performance characteristics. It represents the risk boundary at which the vehicle can operate safely under specific conditions. When the revised risk prediction value is greater than or equal to the safety threshold, it means that the current operating risk has approached or exceeded the safety range allowed by the system, so the right to autonomous driving needs to be transferred to the driver to ensure safety. When the revised risk prediction value is lower than the safety threshold, it indicates that the current operating conditions are still within the safety range, and the system can continue to maintain the autonomous driving mode without human intervention.

[0112] In summary, this technical solution achieves accurate evaluation of tire performance, thereby making more accurate corrections to the initial risk prediction value generated by emergency events. This process not only improves the system's sensitivity and response speed to changes in tire performance, but also ensures that under complex or harsh environmental conditions, the system can dynamically adjust the risk assessment results based on the latest tire grip data.

[0113] The beneficial effect is that the corrected risk prediction value can more realistically reflect the current operating status of the vehicle, making the autonomous driving system more reliable in determining whether to transfer control to the driver. By combining real-time data with historical operating data, the system can not only identify subtle changes in tire performance, but also accurately adjust risk assessment by generating a correction slope. The rationale of this method is that it comprehensively considers the current actual situation and the tire performance in similar situations in the past, forming a closed-loop feedback mechanism.

[0114] In addition, this approach not only improves the safety of the system, but also reduces misjudgments caused by overly conservative or overly aggressive risk assessments, and optimizes the decision-making process for the transfer of autonomous driving rights. This refined evaluation method enables the autonomous driving system to make more accurate judgments at critical moments, significantly improving the stability of the system and the safety of operation.

[0115] Furthermore, Figure 6 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0116] Among them, in another preferred embodiment provided by the present invention, an emergency management system for remote control driving vehicles includes:

[0117] The first data acquisition module 100 is used to obtain the alarm time and the risk prediction value of the target remote-controlled passenger elevator in dealing with the real-time emergency event when receiving the real-time emergency event alarm of the target remote-controlled passenger elevator, and determine whether the real-time emergency event belongs to a predetermined emergency category.

[0118] The predetermined emergency category is a snowy day category or a rainy day category. When the predetermined emergency category is a snowy day category, the real-time environmental condition data should include snow thickness, humidity and road friction coefficient; when the predetermined emergency category is a rainy day category, the real-time environmental condition data should include ambient temperature and road wetness.

[0119] In the embodiment of the present invention, when the monitoring system of the target remote-controlled passenger elevator vehicle detects a potential risk, a real-time emergency event alarm will be automatically generated. These real-time emergency event alarms are generated by sensors and detection equipment installed on the vehicle. When key indicators such as tire grip, ambient temperature, humidity, etc. exceed the preset safety threshold, the system will immediately trigger an alarm to remind the operator or the autonomous driving system of possible danger.

[0120] The risk prediction value is generated by analyzing real-time data based on existing technologies. These data include the vehicle's grip, environmental conditions (such as temperature and humidity), and the vehicle's operating status. The algorithm combines these factors to calculate a quantitative risk value, which is used to evaluate the safety of passenger elevator operation in the current environment and as a basis for determining whether emergency measures need to be taken.

[0121] Once an alarm is triggered, the system automatically records and sends the alarm event to the control center or the operator's interface. The operator can obtain detailed alarm information through the interface, including the trigger time, risk prediction value and related environmental condition data, which helps to quickly judge the emergency situation and decide whether to transfer the autonomous driving right or take other measures.

[0122] Furthermore, the emergency management system of the remote control vehicle also includes:

[0123] The second data acquisition module 200 is used to obtain the real-time grip data, real-time environmental condition data and historical operation log of the target remote-controlled passenger elevator car if it is determined that the real-time emergency event belongs to the predetermined emergency category, and retrieve the historical operation records in the historical operation log that are consistent with the real-time environmental condition data.

[0124] Specifically, Figure 7 It shows a structural block diagram of the second data acquisition module 200 in the system provided by the embodiment of the present invention.

[0125] Among them, in the preferred embodiment provided by the present invention, the second data acquisition module 200 specifically includes:

[0126] The data acquisition unit 201 is used to acquire the real-time gripping force data, real-time environmental condition data and historical operation log of the target remote-controlled passenger elevator vehicle after determining that the real-time emergency event belongs to a predetermined emergency category;

[0127] The first operation log retrieval unit 202 is used to retrieve the historical operation log of the target remote-controlled passenger elevator and filter out the historical operation records with the same category as the real-time emergency event;

[0128] The second operation log retrieval unit 203 is used to perform a secondary screening on the historical operation records screened out initially according to the real-time environmental condition data of the target remote-controlled passenger elevator vehicle, and obtain the historical operation records consistent with the real-time environmental condition data.

[0129] In the embodiment of the present invention, the real-time grip data is usually obtained by tire grip sensors installed on the tires of the target remote control passenger elevator vehicle. These sensors can monitor the friction between the tires and the ground in real time and transmit the data to the vehicle-mounted system so that the system can instantly evaluate the grip of the tires during driving.

[0130] Historical operation logs are record files automatically generated by the target remote control passenger elevator during operation. These logs capture and store various operation data in real time through the on-board recording system, usually including the vehicle's operating time, speed, path, tire grip data, environmental conditions (such as temperature, humidity, road friction coefficient), and any abnormal or maintenance events that occurred. Historical operation logs provide the system with a detailed archive of past operating status, which is convenient for backtracking and analysis when needed.

[0131] When obtaining historical operation records that are consistent with real-time environmental condition data, the system does not require that these data be exactly the same, but allows the data to fluctuate within a certain range. For example, taking the snowy day category as an example, assuming that the snow thickness in the current real-time environmental condition data is 15 cm, the system can filter out records in the historical records with a snow thickness between 12 and 18 cm. Similarly, if the current humidity is 85%, humidity between 80% and 90% in the historical records can be considered to meet the conditions. This permission for data fluctuations within a certain range is to better match historical operating conditions with current real-time conditions, thereby providing a more accurate risk assessment.

[0132] Furthermore, the emergency management system of the remote control vehicle also includes:

[0133] The line graph generation module 300 is used to obtain the historical operation time of each historical operation record and the historical tire grip data of the corresponding target remote-controlled passenger elevator, and generate a grip data analysis line graph based on these historical operation times and the corresponding historical tire grip data, as well as the real-time tire grip data corresponding to the alarm time.

[0134] The grip data analysis line graph is an XY axis line graph, wherein the X axis represents time, the Y axis represents tire grip data, and the time interval between each punctuation point on the X axis is the same.

[0135] In the embodiment of the present invention, by integrating historical and real-time data, the system plots the grip change trajectory on a line graph, thereby intuitively analyzing the trend of tire grip. This graph is used to evaluate the stability and safety of tire performance, providing a key basis for further risk assessment and driving decision-making.

[0136] Furthermore, the emergency management system of the remote control vehicle also includes:

[0137] The correction slope generation module 400 is used to analyze each inflection point of the grip data analysis line graph and determine whether the grip data analysis line graph meets the tire performance optimization judgment standard. If it is determined to meet the tire performance optimization judgment standard, a correction slope is generated according to the grip data analysis line graph.

[0138] Specifically, Figure 8 The structure block diagram of the correction slope generating module 400 in the system provided by the embodiment of the present invention is shown.

[0139] In a preferred embodiment of the present invention, the correction slope generating module 400 specifically includes:

[0140] The maintenance event judgment unit 401 is used to obtain the occurrence time of each inflection point of the grip data analysis line graph, and judge whether a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before each occurrence time according to the historical operation records;

[0141] The standard compliance judgment unit 402 is used to judge that the grip data analysis line graph meets the tire performance optimization judgment standard if it is determined that a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before the occurrence time exceeding the preset ratio, and then judge that the grip data analysis line graph meets the tire performance optimization judgment standard;

[0142] The correction slope generating unit 403 is used to generate a correction slope according to the grip data analysis line graph after determining that the grip data analysis line graph meets the tire performance optimization determination standard.

[0143] In the embodiment of the present invention, the preset ratio between the inflection point occurrence time and the tire-related maintenance event is set based on empirical data and statistical analysis. In the long-term operation process, by analyzing the historical operation data of the target remote-controlled passenger elevator, the correlation between tire performance optimization and maintenance events can be found. The occurrence of the inflection point usually indicates that the tire grip has changed significantly, which may be directly related to the previous tire maintenance or replacement and other related events.

[0144] The preset ratio is set based on the following reasons and basis:

[0145] a Statistical correlation: Historical data analysis shows that before a certain percentage of inflection points occur, there are usually tire-related maintenance events. This indicates that these maintenance events have a significant impact on tire performance, thereby affecting the change in grip data. Therefore, it is reasonable to use this correlation as a criterion for evaluating tire performance optimization.

[0146] b Maintenance effectiveness verification: By setting a preset ratio, the effectiveness of tire-related maintenance can be verified. If there are related maintenance events before most of the inflection points occur, it means that these maintenance are effective in improving tire performance. Therefore, setting a reasonable ratio can help the system identify effective maintenance operations and determine whether the grip data reflects good tire performance optimization.

[0147] Exceeding the preset ratio means that in most cases, the change in grip data (i.e. the occurrence of inflection points) is closely related to tire-related maintenance events. This close correlation shows that the effect of tire performance optimization is reflected in the grip data. If the inflection points detected by the system are highly consistent with maintenance events within the preset ratio, it means that tire maintenance has indeed improved tire performance and is clearly reflected in the data.

[0148] Therefore, when there is a tire-related maintenance event before the inflection point that exceeds the preset ratio, it can be reasonably determined that the grip data analysis line graph meets the tire performance optimization judgment standard. This means that the change in grip data is not only the result of random fluctuations, but is more likely to be a direct reflection of the performance improvement brought about by the maintenance event, thereby increasing confidence in tire performance optimization and judgment accuracy.

[0149] This verification solution can effectively identify and verify the actual impact of tire maintenance on performance optimization by combining the inflection points of the grip data line graph with tire-related maintenance events. By setting a preset ratio and judging the correlation between the inflection points and maintenance events, the system can more accurately evaluate the effectiveness of maintenance operations, thereby avoiding unnecessary maintenance, saving resources and improving operational efficiency.

[0150] In addition, this solution enhances the system's ability to judge tire performance optimization. When the inflection point that exceeds the preset ratio is associated with a maintenance event, the system can more confidently determine that the grip data reflects the actual performance improvement. This verification method helps to improve the accuracy and reliability of the entire technical solution, ensuring that the right decision is made at critical moments.

[0151] Overall, the verification scheme provides a data-driven evaluation mechanism for the technical solution, so that the grip data analysis is not limited to simple trend observation, but also can be effectively fed back and verified through actual maintenance records. This closed-loop feedback mechanism strengthens the system's self-correction ability and helps to continuously optimize the safety and performance of the autonomous driving system.

[0152] The slope of the local broken line graph between the last two inflection points is selected as the correction slope based on the consideration of accurately capturing and feeding back the latest dynamic changes in tire performance. In grip data analysis, inflection points usually represent significant change points in grip data, reflecting the turning point or adjustment of tire performance. The local broken line graph between the last two inflection points can reflect the changing trend of tire performance in the recent period of time, especially the grip performance after maintenance or adjustment. Therefore, calculating the slope of this local broken line graph can provide a dynamic correction parameter that is closest to the current tire status, ensuring that the adjustment of the risk prediction value is more accurate.

[0153] This driving method is based on the "local sensitivity" analysis of grip data changes. Local sensitivity means that the system has a high response sensitivity to the latest changes in grip data, especially in emergency management, this sensitivity can help the system quickly adapt to the latest conditions of tire performance. By paying attention to the slope between the last two inflection points, the system can reflect the trend of grip data over the past period of time in the current risk prediction, thereby avoiding inaccurate decisions based on outdated data. In addition, the slope of the local line graph reflects the "instantaneous change rate" of tire grip to a certain extent, and this parameter is crucial for evaluating future grip change trends.

[0154] For example, in snowy conditions, if the last two inflection points appear after the vehicle passes through thick snow and slippery roads, the slope of the local broken line graph can reflect the improvement or deterioration of the tire's grip during this period. If the slope is large, it means that the tire's grip has been significantly enhanced. The system can reduce the risk prediction value by correcting the slope, thereby reducing unnecessary control intervention. If the slope is small or even negative, it means that the tire performance may be deteriorating. The system should increase the risk prediction value to take countermeasures in advance. This analysis method ensures that the risk prediction is highly consistent with the real-time status of the tire performance, improving the decision-making accuracy and safety of the autonomous driving system.

[0155] Furthermore, the emergency management system of the remote control vehicle also includes:

[0156] The risk prediction value adjustment module 500 is used to adjust the risk prediction value according to the correction slope to obtain a corrected risk prediction value, obtain the safety threshold corresponding to the predetermined emergency category, and compare the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right.

[0157] In the embodiment of the present invention, the method of multiplying the risk prediction value by the correction slope to obtain the corrected risk prediction value is based on the dynamic adjustment and precise calibration of the current risk state. The corrected slope reflects the latest trend of tire performance, so multiplying it with the original risk prediction value can more accurately reflect the current actual risk level. If the slope is large, it means that the tire grip has been greatly improved, and the system reduces the overall risk assessment value through the product. Conversely, if the slope is small or negative, it means that the grip performance has deteriorated, and the product increases the risk assessment value.

[0158] The logic behind this method is the concept of "proportional adjustment". The correction slope, as a dynamic adjustment factor, can effectively correct the deviation of the original risk prediction value, making the final corrected risk prediction value closer to the actual vehicle status. Through product operations, the system can keenly capture the slight changes in tire grip data and adjust the assessment of potential risks accordingly. This method ensures a high degree of consistency between risk prediction and the current status of the vehicle.

[0159] The basis for comparing the revised risk prediction value with the safety threshold corresponding to the predetermined emergency category is mainly to ensure the safety and stability of vehicle operation. The safety threshold is set based on a large amount of historical data, environmental conditions and vehicle performance characteristics. It represents the risk boundary at which the vehicle can operate safely under specific conditions. When the revised risk prediction value is greater than or equal to the safety threshold, it means that the current operating risk has approached or exceeded the safety range allowed by the system, so the right to autonomous driving needs to be transferred to the driver to ensure safety. When the revised risk prediction value is lower than the safety threshold, it indicates that the current operating conditions are still within the safety range, and the system can continue to maintain the autonomous driving mode without human intervention.

[0160] In summary, this technical solution achieves accurate evaluation of tire performance, thereby making more accurate corrections to the initial risk prediction value generated by emergency events. This process not only improves the system's sensitivity and response speed to changes in tire performance, but also ensures that under complex or harsh environmental conditions, the system can dynamically adjust the risk assessment results based on the latest tire grip data.

[0161] The beneficial effect is that the corrected risk prediction value can more realistically reflect the current operating status of the vehicle, making the autonomous driving system more reliable in determining whether to transfer control to the driver. By combining real-time data with historical operating data, the system can not only identify subtle changes in tire performance, but also accurately adjust risk assessment by generating a correction slope. The rationale of this method is that it comprehensively considers the current actual situation and the tire performance in similar situations in the past, forming a closed-loop feedback mechanism.

[0162] In addition, this approach not only improves the safety of the system, but also reduces misjudgments caused by overly conservative or overly aggressive risk assessments, and optimizes the decision-making process for the transfer of autonomous driving rights. This refined evaluation method enables the autonomous driving system to make more accurate judgments at critical moments, significantly improving the stability of the system and the safety of operation.

[0163] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0165] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0167] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A remote control vehicle emergency management method, characterized in that: The method comprises: When receiving a real-time emergency event alarm of a target remote-controlled passenger elevator vehicle, obtaining the alarm time and the risk prediction value of the target remote-controlled passenger elevator vehicle in responding to the real-time emergency event, and determining whether the real-time emergency event belongs to a predetermined emergency category; If it is determined that the real-time emergency event belongs to the predetermined emergency category, the real-time grip data, the real-time environmental condition data and the historical operation log of the target remote-controlled passenger elevator vehicle are obtained, and the historical operation records consistent with the real-time environmental condition data in the historical operation log are retrieved; Obtain the historical operation time of each historical operation record and the historical tire grip data of the corresponding target remote-controlled passenger elevator vehicle, and generate a grip data analysis line graph based on the historical operation time and the historical tire grip data corresponding to the historical operation time and the real-time tire grip data corresponding to the alarm time; Analyze each inflection point of the grip data analysis line graph, and determine whether the grip data analysis line graph meets the tire performance optimization determination standard. If it is determined that the tire performance optimization determination standard is met, generate a correction slope according to the grip data analysis line graph; The risk prediction value is adjusted according to the correction slope to obtain a corrected risk prediction value, the safety threshold corresponding to the predetermined emergency category is obtained, and the corrected risk prediction value is compared with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right.

2. The emergency management method for remote-controlled driving vehicles according to claim 1, characterized in that: The predetermined emergency category is a snowy day category or a rainy day category. When the predetermined emergency category is a snowy day category, the real-time environmental condition data should include snow thickness, humidity and road friction coefficient; when the predetermined emergency category is a rainy day category, the real-time environmental condition data should include ambient temperature and road wetness.

3. The emergency management method for remote-controlled driving vehicles according to claim 2, characterized in that: If it is determined that the real-time emergency event belongs to the predetermined emergency category, the steps of obtaining the real-time grip data, the real-time environmental condition data and the historical operation log of the target remote-controlled passenger elevator vehicle, and retrieving the historical operation record consistent with the real-time environmental condition data in the historical operation log include: After determining that the real-time emergency event belongs to a predetermined emergency category, obtaining real-time grip data, real-time environmental condition data, and historical operation logs of the target remote-controlled passenger elevator vehicle; Retrieve the historical operation log of the target remote-controlled passenger elevator and filter out the historical operation records with the same category as the real-time emergency event; The historical operation records screened out initially are screened a second time according to the real-time environmental condition data of the target remote-controlled passenger elevator car to obtain historical operation records that are consistent with the real-time environmental condition data.

4. The emergency management method for remote control driving vehicles according to claim 1, characterized in that: The grip data analysis line graph is an XY axis line graph, wherein the X axis represents time, the Y axis represents tire grip data, and the time interval between each punctuation point on the X axis is the same.

5. The emergency management method for remote-controlled driving vehicles according to claim 1, characterized in that: The steps of analyzing each inflection point of the grip data analysis line graph and determining whether the grip data analysis line graph meets the tire performance optimization determination standard, and if it is determined that the tire performance optimization determination standard is met, generating a correction slope according to the grip data analysis line graph includes: Obtain the occurrence time of each inflection point of the grip data analysis line graph, and determine whether a tire-related maintenance event of the target remote-controlled passenger elevator vehicle has occurred within a preset period before each occurrence time based on historical operation records; If it is determined that a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before the occurrence time exceeding the preset ratio, it is determined that the grip data analysis line graph meets the tire performance optimization determination standard; After determining that the grip data analysis line graph meets the tire performance optimization determination criteria, a correction slope is generated based on the grip data analysis line graph.

6. The emergency management method for remote-controlled driving vehicles according to claim 5, characterized in that: After determining that the grip data analysis line graph meets the tire performance optimization determination standard, the steps of generating a correction slope according to the grip data analysis line graph include: After determining that the grip data analysis line graph meets the tire performance optimization determination standard, obtaining a local line graph between the last two inflection points in the grip data analysis line graph; Calculate the slope of the local line graph and set it as the correction slope.

7. The emergency management method for remote-controlled driving vehicles according to claim 1, characterized in that: The steps of adjusting the risk prediction value according to the correction slope to obtain a corrected risk prediction value, obtaining a safety threshold corresponding to a predetermined emergency category, and comparing the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the autonomous driving right include: Multiply the risk prediction value by the correction slope to obtain the corrected risk prediction value; Obtaining a safety threshold corresponding to a predetermined emergency category, and comparing the modified risk prediction value with the safety threshold corresponding to the predetermined emergency category; When the modified risk prediction value is greater than or equal to the safety threshold corresponding to the predetermined emergency category, it is determined that the autonomous driving right should be transferred to the driver; When the corrected risk prediction value is less than the safety threshold, it is determined that there is no need to transfer the autonomous driving rights.

8. An emergency management system for remotely controlled vehicles, characterized in that: The system comprises: a first data acquisition module, a second data acquisition module, a line graph generation module, a correction slope generation module and a risk prediction value adjustment module, wherein: A first data acquisition module is used to obtain the alarm time and the risk prediction value of the target remote-controlled passenger elevator vehicle in responding to the real-time emergency event when receiving the real-time emergency event alarm of the target remote-controlled passenger elevator vehicle, and determine whether the real-time emergency event belongs to a predetermined emergency category; The predetermined emergency category is a snowy day category or a rainy day category. When the predetermined emergency category is a snowy day category, the real-time environmental condition data should include snow thickness, humidity and road friction coefficient; when the predetermined emergency category is a rainy day category, the real-time environmental condition data should include ambient temperature and road slipperiness; A second data acquisition module is used to acquire the real-time grip data, real-time environmental condition data and historical operation log of the target remote-controlled passenger elevator vehicle if it is determined that the real-time emergency event belongs to a predetermined emergency category, and retrieve the historical operation record consistent with the real-time environmental condition data in the historical operation log; A line graph generation module is used to obtain the historical operation time of each historical operation record and the historical tire grip data of the corresponding target remote-controlled passenger elevator vehicle, and generate a grip data analysis line graph based on the historical operation time and the historical tire grip data corresponding to the historical operation time and the real-time tire grip data corresponding to the alarm time; The grip data analysis line graph is an XY axis line graph, wherein the X axis represents time, the Y axis represents tire grip data, and the time interval between each punctuation point on the X axis is the same; A correction slope generation module is used to analyze each inflection point of the grip data analysis line graph and determine whether the grip data analysis line graph meets the tire performance optimization judgment standard. If it is determined that the tire performance optimization judgment standard is met, a correction slope is generated according to the grip data analysis line graph; The risk prediction value adjustment module is used to adjust the risk prediction value according to the correction slope to obtain a corrected risk prediction value, obtain the safety threshold corresponding to the predetermined emergency category, and compare the corrected risk prediction value with the safety threshold corresponding to the predetermined emergency category to determine whether to transfer the automatic driving right.

9. The emergency management system for remote-controlled vehicles according to claim 8, characterized in that: The second data acquisition module specifically includes: A data acquisition unit, for acquiring real-time grip data, real-time environmental condition data, and historical operation logs of a target remote-controlled passenger elevator vehicle after determining that the real-time emergency event belongs to a predetermined emergency category; A first operation log retrieval unit is used to retrieve the historical operation log of the target remote-controlled passenger elevator vehicle and filter out the historical operation records with the same category as the real-time emergency event; The second operation log retrieval unit is used to perform a secondary screening on the historical operation records screened out initially according to the real-time environmental condition data of the target remote-controlled passenger elevator vehicle, and obtain the historical operation records consistent with the real-time environmental condition data.

10. The emergency management system for remote-controlled vehicles according to claim 9, characterized in that: The correction slope generation module specifically includes: A maintenance event judgment unit is used to obtain the occurrence time of each inflection point of the grip data analysis line graph, and judge whether a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before each occurrence time according to historical operation records; a standard compliance judgment unit, for judging that the grip data analysis line graph meets the tire performance optimization judgment standard if it is determined that a tire-related maintenance event of the target remote-controlled passenger elevator vehicle occurs within a preset period before the occurrence time exceeding the preset ratio; The correction slope generating unit is used to generate a correction slope according to the grip data analysis line graph after determining that the grip data analysis line graph meets the tire performance optimization determination standard.

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