CN117494981A

CN117494981A - Safety-based intelligent vehicle scheduling method and device

Info

Publication number: CN117494981A
Application number: CN202311374131.1A
Authority: CN
Inventors: 李强; 尹庆
Original assignee: Shenzhen Dexing Intelligent Technology Co ltd
Current assignee: Shenzhen Dexing Intelligent Technology Co ltd
Priority date: 2023-10-23
Filing date: 2023-10-23
Publication date: 2024-02-02
Anticipated expiration: 2043-10-23
Also published as: CN117494981B

Abstract

The invention is applicable to the technical field of artificial intelligence, and provides a vehicle intelligent scheduling method, device and terminal equipment based on safety, wherein the information safety control method comprises the following steps: and acquiring various vehicle information, transportation task information and driving characters of the driver, obtaining performance types and driving character types based on the vehicle information, and primarily screening a plurality of initial drivers. And calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information. And screening the target drivers according to the safety scores, and performing ranking recommendation based on the target drivers and the vehicle positions. Because the matching degree among drivers, vehicles and transportation tasks is comprehensively evaluated based on the plurality of dimension information, the safety scores are obtained, drivers corresponding to different vehicles are matched based on the safety scores, and the matching precision is high. Reasonable assessment of the driver and reasonable allocation of suitable drivers for different vehicles are achieved.

Description

Safety-based intelligent vehicle scheduling method and device

Technical Field

The invention belongs to the technical field of artificial intelligence, and particularly relates to a vehicle intelligent scheduling method and device based on safety.

Background

In some specific scenarios, it may be desirable to assign different drivers to different vehicles, such as: drivers in passenger or freight traffic scenarios match demand. At present, driver matching often needs to be manually selected according to information such as driving age, driving records, related experience and the like of a driver.

However, there are problems of non-uniformity of standards, non-comprehensive information, and the like due to the manual distribution process. And the requirements of different transportation tasks on drivers are different, so that how to reasonably evaluate the drivers and how to reasonably distribute proper drivers for different vehicles becomes a technical problem to be solved urgently.

Disclosure of Invention

In view of the above, the embodiments of the present invention provide a method, an apparatus, a terminal device, and a computer readable storage medium for intelligent vehicle dispatching based on security, so as to solve the technical problems of how to reasonably evaluate drivers and how to reasonably allocate suitable drivers to different vehicles.

A first aspect of an embodiment of the present invention provides a vehicle intelligent scheduling method based on security, where the information security management and control method includes:

acquiring vehicle information, and calculating the performance type and the performance score of the vehicle according to the vehicle information; the vehicle information includes power, handling, braking performance, comfort and vehicle safety level;

Obtaining driving character type and driving preference score corresponding to each of a plurality of drivers based on driving character test results;

screening a plurality of initial drivers according to the performance type, the driving personality type, the driving preference score and the performance score;

acquiring transportation task information, vehicle types and vehicle ages; the transportation task information comprises a transportation main body type, a travel distance, a value grade, a congestion degree and road complexity;

acquiring historical driving information corresponding to each of a plurality of initial drivers, wherein the historical driving information comprises driving age, driving safety level, driving duration of the vehicle type and average vehicle speed;

calculating a safety coefficient according to the historical driving information, the vehicle type and the vehicle age;

calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information;

screening a target driver among a plurality of initial drivers according to the safety score;

obtaining distances between a plurality of target drivers and the positions of the vehicles, ranking the target drivers according to the distances, and outputting ranking results; the ranking result is used for scheduling the corresponding driver to execute the transportation task of the current vehicle.

Further, the step of acquiring vehicle information and calculating the performance type and the performance preference score of the vehicle according to the vehicle information includes:

constructing a multidimensional vector corresponding to the vehicle information, and acquiring a plurality of performance type matrixes;

multiplying the multidimensional vector with the performance type matrix respectively to obtain target vectors corresponding to the performance type matrixes respectively;

respectively calculating the modes of a plurality of target vectors;

matching a target performance type matrix corresponding to the maximum modulus, and taking a preset type mapped by the target performance type matrix as the performance type;

the maximum modulus is taken as the performance preference score.

Further, the step of screening a plurality of initial drivers according to the performance type, the driving personality type, the driving preference score, and the performance score includes:

acquiring a preset driving character type mapped by the performance type;

screening the preset driving personality types from the driving personality types corresponding to the drivers respectively to obtain a plurality of drivers to be selected corresponding to the preset driving personality types;

respectively carrying out weighted summation on the driving preference scores and the performance scores corresponding to the multiple drivers to be selected to obtain a matching degree;

And taking the candidate driver with the matching degree larger than a threshold value as the initial driver.

Further, the step of calculating a safety factor according to the historical driving information, the vehicle model and the vehicle age includes:

constructing a plurality of historical driving information as a feature matrix;

multiplying the characteristic features by preset preference matrixes corresponding to the evaluation dimensions respectively to obtain a plurality of characteristic values;

and calculating the safety coefficient according to the characteristic values, the vehicle model and the vehicle age.

Further, the step of calculating the safety coefficient according to a plurality of the characteristic values, the vehicle model and the vehicle age includes:

acquiring weight coefficients corresponding to the characteristic values respectively;

substituting the weight coefficients, the characteristic values, the vehicle model and the vehicle age into a formula I to obtain the safety coefficient;

equation one:

wherein S represents the safety factor, x _i Representing the i-th said characteristic value,represents the average value, w, of a plurality of said characteristic values _i Represents the ith weight coefficient, n represents the eigenvalue or the number of weight coefficients, ++>Parameter values representing the vehicle model Beta represents the parameter value of the vehicle age.

Further, the step of calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information includes:

calculating a basic score corresponding to the initial driver according to the driving age and the driving safety level;

calculating a model coefficient corresponding to the initial driver according to the driving duration of the model;

calculating a risk coefficient of a transportation task according to the average vehicle speed, the transportation main body type, the travel distance, the congestion degree, the road complexity and the value grade;

and carrying out weighted summation on the basic score, the vehicle model coefficient and the risk coefficient to obtain the safety score.

Further, the step of calculating the risk factor of the transportation task according to the average vehicle speed, the transportation subject type, the travel distance, the congestion degree, the road complexity and the value level includes:

matching corresponding clustering clusters according to the transportation main body type; wherein, a plurality of clusters are trained by different samples, each cluster comprises a plurality of cluster branches;

Calculating the distance between a clustering vector formed by the transportation main body type and the value level and a distance vector corresponding to a clustering branch;

taking the clustering branch corresponding to the maximum distance as a target clustering branch, and acquiring an adjustment factor corresponding to the target clustering branch;

and calculating a risk coefficient of the transportation task according to the adjustment factor, the average vehicle speed, the travel distance, the congestion degree and the road complexity.

Further, the step of calculating a risk factor of a transportation mission according to the adjustment factor, the average vehicle speed, the travel distance, the congestion degree and the road complexity includes:

substituting the adjustment factor, the average vehicle speed, the travel distance, the congestion degree and the road complexity into a formula II to obtain a risk coefficient of the transportation task;

formula II:

wherein R represents a risk factor of the transportation mission, mu represents the adjustment factor, tau represents the average vehicle speed, D represents the travel distance,representing the congestion degree, and P represents the road complexity.

A second aspect of an embodiment of the present invention provides a safety-based intelligent vehicle scheduling apparatus, including:

The first acquisition unit is used for acquiring vehicle information and calculating the performance type and the performance score of the vehicle according to the vehicle information; the vehicle information includes power, handling, braking performance, comfort and vehicle safety level;

the second acquisition unit is used for acquiring driving character types and driving preference scores corresponding to a plurality of drivers based on the driving character test result;

a first screening unit configured to screen a plurality of initial drivers according to the performance type, the driving personality type, the driving preference score, and the performance score;

the third acquisition unit is used for acquiring transportation task information, vehicle types and vehicle ages; the transportation task information comprises a transportation main body type, a travel distance, a value grade, a congestion degree and road complexity;

a fourth obtaining unit, configured to obtain historical driving information corresponding to each of the plurality of initial drivers, where the historical driving information includes a driving age, a driving safety level, a driving duration of the vehicle model, and an average vehicle speed;

the first calculation unit is used for calculating a safety coefficient according to the historical driving information, the vehicle type and the vehicle age;

a second calculation unit for calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information;

A second screening unit configured to screen a target driver among a plurality of the initial drivers according to the safety score;

the output unit is used for acquiring the distances between the plurality of target drivers and the vehicle position, ranking the target drivers according to the distances and outputting ranking results; the ranking result is used for scheduling the corresponding driver to execute the transportation task of the current vehicle.

A third aspect of an embodiment of the present invention provides a terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method of the first aspect when executing the computer program.

A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method of the first aspect.

Compared with the prior art, the embodiment of the invention has the beneficial effects that: calculating the performance type and the performance score of the vehicle according to the vehicle information by acquiring the vehicle information; obtaining driving character type and driving preference score corresponding to each of a plurality of drivers based on driving character test results; screening a plurality of initial drivers according to the performance type, the driving personality type, the driving preference score and the performance score; acquiring transportation task information, vehicle types and vehicle ages; the transportation task information comprises a transportation main body type, a travel distance, a value grade, a congestion degree and road complexity; acquiring historical driving information corresponding to each of a plurality of initial drivers, wherein the historical driving information comprises driving age, driving safety level, driving duration of the vehicle type and average vehicle speed; calculating a safety coefficient according to the historical driving information, the vehicle type and the vehicle age; calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information; screening a target driver among a plurality of initial drivers according to the safety score; obtaining distances between a plurality of target drivers and the positions of the vehicles, ranking the target drivers according to the distances, and outputting ranking results; the ranking result is used for scheduling the corresponding driver to execute the transportation task of the current vehicle. In the above scheme, in order to improve the matching accuracy, a plurality of vehicle information, transportation task information and driving characteristics of the driver are respectively acquired, and a performance type and a driving characteristics type are obtained based on the vehicle information, so that a plurality of initial drivers are primarily screened. And further, calculating the safety coefficient according to the historical driving information, the vehicle type and the vehicle age of the initial driver. And calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information. And screening the target drivers according to the safety scores, and performing ranking recommendation based on the target drivers and the vehicle positions. Because the matching degree among drivers, vehicles and transportation tasks is comprehensively evaluated based on the plurality of dimension information, the safety scores are obtained, drivers corresponding to different vehicles are matched based on the safety scores, and the matching precision is high. Reasonable assessment of the driver and reasonable allocation of suitable drivers for different vehicles are achieved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.

FIG. 1 shows a schematic flow chart of a safety-based intelligent vehicle scheduling method provided by the invention;

FIG. 2 is a schematic diagram of a safety-based intelligent vehicle dispatching apparatus according to an embodiment of the present invention;

fig. 3 shows a schematic diagram of a terminal device according to an embodiment of the present invention.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

The embodiment of the invention provides a vehicle intelligent dispatching method, device, terminal equipment and computer readable storage medium based on safety, which are used for solving the technical problems of reasonably evaluating drivers and reasonably distributing proper drivers for different vehicles.

Firstly, the invention provides a vehicle intelligent scheduling method based on safety. Referring to fig. 1, fig. 1 shows a schematic flow chart of a vehicle intelligent scheduling method based on safety. As shown in fig. 1, the intelligent vehicle dispatching method based on safety can comprise the following steps:

step 101: acquiring vehicle information, and calculating the performance type and the performance score of the vehicle according to the vehicle information; the vehicle information includes power, handling, braking performance, comfort and vehicle safety level;

in order to sufficiently evaluate the vehicle characteristics, the present application needs to acquire various pieces of vehicle information for analysis. Among other things, vehicle information includes, but is not limited to, one or more of power, handling, braking performance, comfort, and vehicle safety rating.

Where power refers to the amount of propulsion power generated by the vehicle engine, measured in horsepower. Steering refers to steering and athletic performance of a vehicle under driver control, with the steering of different vehicles being evaluated at different steering levels. Braking performance describes the effect and response capability of a vehicle when braked, measured in braking distance per unit distance. Comfort describes the comfort felt by the driver of the vehicle interior. Different vehicles are distinguished for comfort at different comfort levels. The vehicle safety level is a level of vehicle safety that is assessed at different preset levels according to various safety tests and criteria to assess the ability of the vehicle to protect the driver and passengers during an accident.

Specifically, step 101 specifically includes steps 1011 to 1015:

step 1011: constructing a multidimensional vector corresponding to the vehicle information, and acquiring a plurality of performance type matrixes;

and assigning and normalizing the plurality of vehicle information, and constructing a multidimensional vector formed by the plurality of vehicle information.

Illustratively, the maneuver uses a score of 0-10, where 10 represents the optimal maneuver performance. The braking performance uses a score of 0-10, where 10 represents the best braking performance. Comfort uses a score of 0-10, where 10 represents the highest comfort. The vehicle safety levels are numbered to indicate different safety levels, e.g. 1 indicates low, 2 indicates medium, and 3 indicates high.

Assume that the vehicle information is: the power was 300 horsepower, the handling was 9.5, the braking performance was 9.2, comfort: 7.8, the vehicle safety rating is 5 (rating of 1-10).

Normalizing the above raw data:

power: (300-100)/(500-100) =0.625

And (3) control: 9.5/10=0.95

Braking performance: 9.2/10=0.92

Comfort level: 7.8/10=0.78

Vehicle security level: 5/10=0.5

Finally, normalized values are obtained: the power is 0.625, the control is 0.95, the braking performance is 0.92, the comfort level is 0.78, and the vehicle safety level is 0.5.

The plurality of performance type matrices each correspond to a different performance type including, but not limited to, X-type, Y-type, and Z-type. Wherein, X type represents the vehicle performance that power is strong and control in a flexible way, Y type represents the vehicle performance that comfort level is high and security is good, and Z type represents the vehicle performance of performance balance. The values in the performance type matrix are characteristic values corresponding to the performance types.

Step 1012: multiplying the multidimensional vector with the performance type matrix respectively to obtain target vectors corresponding to the performance type matrixes respectively;

the target vector is used to characterize a distance between the vehicle information of the multi-dimensional vector and the performance type of the performance type matrix.

Step 1013: respectively calculating the modes of a plurality of target vectors;

step 1014: matching a target performance type matrix corresponding to the maximum modulus, and taking a preset type mapped by the target performance type matrix as the performance type;

step 1015: the maximum modulus is taken as the performance preference score.

In this embodiment, by constructing multidimensional vectors corresponding to vehicle information, each dimension representing a specific attribute or performance type, the characteristics and performance of the vehicle can be described more precisely. A plurality of performance type matrices are constructed from the multi-dimensional vector, each matrix representing a weight distribution for a particular performance type. This allows different weights to be defined for different performance types. Multiplying the multidimensional vector with the corresponding performance type matrix to obtain the target vector corresponding to each of the plurality of performance type matrices. The target vector reflects the scoring of the vehicle at different performance types. For each target vector, its modulus (also called norm) is calculated. The modulus represents the size or intensity of the target vector. The target vector with the largest modulus, i.e. the performance type matrix with the highest performance score, is found. This allows a determination to be made as to which performance type the vehicle is performing optimally. And taking the preset type of the target performance type matrix mapping corresponding to the maximum model as the performance type of the vehicle. The preset type is a set of performance types defined in advance. The maximum modulus is taken as the performance preference score for the vehicle. The higher the score, the better the performance of the vehicle under the corresponding performance type. In summary, the present embodiment can improve accuracy of performance type matching by using the calculation of the multidimensional vector and the performance type matrix. By performing modular computation and maximum modular matching on a plurality of target vectors, the performance of the vehicle under different performance types can be accurately identified, and the vehicle is allocated with the most suitable preset performance type and the corresponding performance preference score.

Step 102: obtaining driving character type and driving preference score corresponding to each of a plurality of drivers based on driving character test results;

the driving character test result is a test result obtained by selecting a driver according to a preset question. The driving style represents the driving style of the driver, and includes, but is not limited to, one or more of adventure, robustness, caution, and balance. The driving preference score indicates the degree of preference that the driver belongs to that personality type.

Step 103: screening a plurality of initial drivers according to the performance type, the driving personality type, the driving preference score and the performance score;

specifically, step 103 specifically includes steps 1031 to 1034:

step 1031: acquiring a preset driving character type mapped by the performance type;

different performance types and different driving character types have preset mapping relations.

Step 1032: screening the preset driving personality types from the driving personality types corresponding to the drivers respectively to obtain a plurality of drivers to be selected corresponding to the preset driving personality types;

step 1033: respectively carrying out weighted summation on the driving preference scores and the performance scores corresponding to the multiple drivers to be selected to obtain a matching degree;

Step 1034: and taking the candidate driver with the matching degree larger than a threshold value as the initial driver.

In this embodiment, by acquiring the preset driving personality type of the performance type map, the personality and the preference of the driver may be classified and generalized. This provides the basis data for the subsequent driver matching process. And screening the candidate drivers which accord with the preset driving personality types from the plurality of drivers according to the driving personality types corresponding to each driver. Therefore, the matching range can be reduced, and the matching accuracy and the target conformity are improved. And carrying out weighted summation on the driving preference score and the performance score of each candidate driver to obtain a comprehensive matching degree score. This scoring takes into account the personality traits and skill levels of the driver, as well as the performance requirements of the vehicle. By means of this weighting calculation, it is possible to better quantify and evaluate whether the driver is suitable for a particular driving task. And determining a driver to be selected with the matching degree larger than the threshold value according to the matching degree score, and selecting the driver to be selected as an initial driver. Thus, the reliability and the similarity of the preliminary matching result can be ensured, and the degree of fit between a driver and a task is improved. In general, by combining preset driving personality types, driving preference scores, and performance scores, and setting thresholds, more accurate driver matching is achieved. By comprehensively considering a plurality of factors and based on numerical evaluation, the rationality of driver matching can be improved.

Step 104: acquiring transportation task information, vehicle types and vehicle ages; the transportation task information comprises a transportation main body type, a travel distance, a value grade, a congestion degree and road complexity;

different vehicle types generally have different appearance designs, sizes, functions and characteristics, and are suitable for different transportation tasks. Common vehicle types include, but are not limited to, passenger vehicles and trucks, and the like, trucks including, but not limited to, flatbed trucks, van trucks, high and low deck trucks, refrigerated trucks, dump trucks, vans, tank trucks, and the like.

The service life of an automobile refers to the service life of the automobile, namely the past years of automobile production. The higher the vehicle age, the longer the mass production time representing the vehicle, which may mean that the technology, performance and reliability of the vehicle are old or degraded. The age of the vehicle can also reflect the extent of use and maintenance of the vehicle. In general, smaller vehicles are generally considered newer, more valuable and more reliable.

Transportation mission information includes, but is not limited to, one or more of transportation body type, travel distance, value level, congestion level, and road complexity.

The transportation body type refers to a classification for transporting goods or persons, etc. Travel distance refers to the distance that is required to actually travel or transport during transport. Value grade refers to the economic value or importance level of the goods or personnel being transported. The value ratings of the goods or personnel may be low, medium, high or other categories. The degree of congestion refers to a situation where traffic flow on a road or traffic network exceeds normal capacity, resulting in a slowing or stagnation of the vehicle running speed. The congestion degree can be judged according to the traffic flow, the road capacity, the traffic condition and other factors. Road complexity refers to the complexity of the shape, environment, and road surface conditions of the road. Complex roads may include mountain roads, narrow roads, curved roads, complex intersections, and the like. Road complexity can have an impact on the choice of transport body and the driving process. Some transportation bodies may be more suitable for driving under simple road conditions, while requiring higher skills and skills on complex roads. The type of transportation main body, the travel distance, the value grade, the congestion degree and the road complexity are important factors influencing the transportation process, and the transportation main body type, the travel distance, the value grade, the congestion degree and the road complexity need to be comprehensively considered in actual situations and make corresponding decisions and arrangements.

For better calculation later, the transport body type, travel distance, value class, congestion level and road complexity need to be assigned and normalized:

the transport body type assigns a representative numerical code to each type. The travel distance is a specific unit (e.g., kilometers). Value grades the value grade is divided into several discrete grades, e.g. three high, medium, low or more. Each level is then assigned a corresponding numerical code as a representation of the attribute. The congestion level is similar to the value level, the congestion level is divided into a plurality of discrete levels, and each level is assigned a corresponding numerical code. The road complexity divides the road complexity into several discrete levels and assigns a corresponding numerical code for each level. And normalizing the numerical codes of the attributes to ensure that the numerical codes are in the same numerical range. And carrying out normalization processing according to the minimum value and the maximum value of the attribute by using the same normalization formula.

Step 105: acquiring historical driving information corresponding to each of a plurality of initial drivers, wherein the historical driving information comprises driving age, driving safety level, driving duration of the vehicle type and average vehicle speed;

In order to evaluate driving preferences more accurately, the present application acquires historical driving information of the driver to further perform security scoring based on the historical driving information. The historical driving information includes, but is not limited to, a combination of one information and a plurality of information such as driving age, driving safety level, driving duration of a vehicle type, and average vehicle speed.

The driving age refers to the number of years of experience of a person from the time when driving a car is started. Longer driving ages generally indicate that a person has more experience and skill in driving. The age of driving can be associated with driving safety because experienced drivers tend to be more familiar with road regulations and traffic conditions and can better address various driving challenges and hazards. The driving safety level is a level that is rated based on information such as driving behavior of the driver and credit record. This level may reflect a driver's driving habits, compliance with traffic regulations, whether traffic violations are involved, etc. Generally, the higher the driving safety level, the safer and more reliable the driving behavior of the driver. The driving duration of a vehicle model refers to the length of time that the vehicle is driven on a particular vehicle model. Different vehicle types have different performance characteristics and driving modes, so that a certain adaptation period is needed to be familiar with and master the operation skills of the vehicle types. The longer the driving duration, the more the driver may have a higher degree of driving skill and awareness of the particular vehicle type. The average vehicle speed reflects the average vehicle speed of the driver over the historical driving experience. The average vehicle speed may be affected by a variety of factors including road conditions, traffic flow, driver driving style, and the like. The length of travel does not necessarily represent the speed of the vehicle, as traffic congestion, speed limiting measures or other factors may be encountered while driving, resulting in a change in speed of the vehicle. The average vehicle speed may provide a reference but may not fully reflect the safety of the driving behavior. In short, the driving age, the driving safety level, the driving duration of the vehicle type and the average vehicle speed are all indexes for evaluating the driving experience and skill of the driver and the influence of the driving experience and skill on traffic safety. The driving experience and skill level of the driver and familiarity with certain vehicle types can have an important impact on driving safety, while maintaining proper speed during driving is also one of the important factors to ensure traffic safety.

Driving age is an integer in years. Assume a minimum driving age of 1 year and a maximum driving age of 30 years. The driving safety level represents the driving safety level using an integer from 1 to 10, where 1 represents the lowest safety level and 10 represents the highest safety level. The vehicle model carries out One-Hot Encoding (One-Hot Encoding) on the vehicle model. For example, if there are three types of vehicles, namely a refrigerated truck, a dump truck and a tank truck, it is possible to use [1, 0] for the refrigerated truck, [0,1,0] for the dump truck and [0, 1] for the tank truck. The driving duration is a real value in hours representing the total time of driving. The real value of the average vehicle speed in km/h represents the average vehicle speed during driving.

Normalization processing is carried out by using minimum-maximum normalization, and the specific steps are as follows:

age of driving: assume a minimum driving age of 1 year and a maximum driving age of 30 years. The driving age is normalized to a range of 0 to 1 by the following formula:

normalized driving age= (driving age-minimum driving age)/(maximum driving age-minimum driving age);

driving safety level: assume that the lowest security level is 1 and the highest security level is 10. The driving safety level is normalized to the range of 0 to 1 by the following formula:

Normalized security level= (driving security level-lowest security level)/(highest security level-lowest security level);

duration of driving: the minimum and maximum driving durations are determined according to the specific situation. The driving duration may then be normalized to a range of 0 to 1 using the following equation:

normalized driving duration= (driving duration-minimum driving duration)/(maximum driving duration-minimum driving duration);

average vehicle speed: the minimum and maximum average vehicle speeds are determined according to the specific conditions. The average vehicle speed may then be normalized to a range of 0 to 1 using the following equation:

normalized average vehicle speed= (average vehicle speed-minimum average vehicle speed)/(maximum average vehicle speed-minimum average vehicle speed).

Step 106: calculating a safety coefficient according to the historical driving information, the vehicle type and the vehicle age;

specifically, step 106 includes steps 1061 to 1063:

step 1061: constructing a plurality of historical driving information as a feature matrix;

and assigning a plurality of historical driving information, and combining to obtain a feature matrix.

Step 1062: multiplying the characteristic features by preset preference matrixes corresponding to the evaluation dimensions respectively to obtain a plurality of characteristic values;

the preset preference matrix is used for representing characteristic values corresponding to different driving preferences.

Step 1063: and calculating the safety coefficient according to the characteristic values, the vehicle model and the vehicle age.

In the present embodiment, a plurality of pieces of history driving information are taken as inputs, and are constructed as one feature matrix. The matrix contains information of various aspects such as driving behavior, vehicle performance and the like, and provides a data basis for the subsequent calculation of the safety coefficient. And multiplying the characteristic matrix by a preset preference matrix corresponding to each of the plurality of evaluation dimensions to obtain a plurality of characteristic values. Each characteristic value represents a score or a degree of importance of the driving behavior in the corresponding evaluation dimension. Calculating a safety coefficient according to the characteristic value, the vehicle type and the vehicle age: and combining and calculating by utilizing the plurality of characteristic values, the vehicle model and the vehicle age to obtain a final safety coefficient. This combination takes into account various aspects of driving behavior, vehicle type and age, etc., providing a more accurate assessment of the safety factor. In general, the embodiment realizes accurate calculation of the safety coefficient by combining factors such as historical driving information, a preset preference matrix, vehicle types and vehicle ages. By calculating and integrating the feature values of the plurality of evaluation dimensions, the safety of the driving behavior can be evaluated more accurately.

Specifically, step 1063 specifically includes: obtaining weight coefficients corresponding to the characteristic values respectively, substituting the weight coefficients, the characteristic values, the vehicle model and the vehicle age into a formula I to obtain the safety coefficient;

equation one:

wherein S represents the safety factor, x _i Representing the i-th said characteristic value,represents the average value, w, of a plurality of said characteristic values _i Represents the ith weight coefficient, n represents the eigenvalue or the number of weight coefficients, ++>And the parameter value of the vehicle model is represented, and the parameter value of the vehicle age is represented by beta.

According to the method and the device, the influence of various factors is comprehensively considered, and the weight coefficient, the characteristic value, the vehicle type and the vehicle age have certain influence on the safety coefficient, so that the safety coefficient is calculated based on the weight coefficient, the characteristic value, the vehicle type and the vehicle age, and the calculation accuracy is improved. The first formula is based on a large amount of experimental data and verification, but is not limited to the mathematical expression.

Step 107: calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information;

specifically, step 107 specifically includes steps 1071 to 1074:

Step 1071: calculating a basic score corresponding to the initial driver according to the driving age and the driving safety level;

and carrying out weighted summation on the driving age and the driving safety level to obtain a basic score.

Step 1072: calculating a model coefficient corresponding to the initial driver according to the driving duration of the model;

and multiplying the driving duration by the correction factor to obtain the model coefficient.

Step 1073: calculating a risk coefficient of a transportation task according to the average vehicle speed, the transportation main body type, the travel distance, the congestion degree, the road complexity and the value grade;

step 1073 specifically includes steps A1 to A4:

step A1: matching corresponding clustering clusters according to the transportation main body type; wherein, a plurality of clusters are trained by different samples, each cluster comprises a plurality of cluster branches;

the cluster is obtained in advance according to a plurality of sample training. Different transport body types correspond to different clusters.

Step A2: calculating the distance between a clustering vector formed by the transportation main body type and the value level and a distance vector corresponding to a clustering branch;

and constructing a clustering vector according to the type and the value grade of the transportation main body and a preset rule. The distance vectors are used to measure the distance (i.e., similarity) of different cluster vectors to the cluster branches.

Step A3: taking the clustering branch corresponding to the maximum distance as a target clustering branch, and acquiring an adjustment factor corresponding to the target clustering branch;

step A4: and calculating a risk coefficient of the transportation task according to the adjustment factor, the average vehicle speed, the travel distance, the congestion degree and the road complexity.

Specifically, step A4 specifically includes: substituting the adjustment factor, the average vehicle speed, the travel distance, the congestion degree and the road complexity into a formula II to obtain a risk coefficient of the transportation task;

formula II:

According to the method and the device, the influence of various factors is comprehensively considered, and as the adjustment factors, the average speed, the travel distance, the congestion degree and the road complexity have certain influence on the risk coefficient, the risk coefficient is calculated based on the adjustment factors, the average speed, the travel distance, the congestion degree and the road complexity, so that the calculation accuracy is improved. The second formula is based on a large amount of experimental data and verification, but is not limited to the mathematical expression.

In this embodiment, it is matched to the corresponding cluster according to the transport subject type. Each cluster is trained from different samples, representing the commonalities and characteristics of a particular type of transport subject. And carrying out distance calculation on the distance vectors corresponding to the clustering branches according to the clustering vectors formed by the transportation main body types and the value grades. This allows for the measurement of the similarity or difference between the transport task and each cluster branch. And selecting the clustering branch with the largest distance as a target clustering branch, and acquiring a corresponding adjustment factor of the target clustering branch. This adjustment factor affects the risk factor of the transportation task, being adapted according to the characteristics of the transportation body. And calculating the risk coefficient of the transportation task by combining parameters such as an adjustment factor, an average vehicle speed, a travel distance, a congestion degree, road complexity and the like. Thus, the contribution of a plurality of factors to the risk can be comprehensively considered, and a more accurate risk assessment result can be generated. By the technical scheme, the accuracy of calculating the risk coefficient can be improved. By matching appropriate clusters, calculating distances, and using adjustment factors, the risk level of a transportation task can be more accurately assessed.

Step 1074: and carrying out weighted summation on the basic score, the vehicle model coefficient and the risk coefficient to obtain the safety score.

In the present embodiment, by taking into consideration the driving age and the driving safety level of the driver, the base score corresponding to the initial driver can be calculated. This may reflect the safety behavior level accumulated by the driver in past driving experience. Calculating a model coefficient according to the driving duration of the model: according to the driving duration of the vehicle model, the vehicle model coefficient corresponding to the initial driver can be calculated. The coefficient considers the service time and the control difficulty of different vehicle types, so that the safety performance of the vehicle is accurately estimated. Calculating risk factors of transportation tasks: the risk factor for the transportation mission may be calculated based on a number of factors including average vehicle speed, transportation body type, travel distance, degree of congestion, road complexity, and value level. These factors reflect the characteristics of the particular driving environment and enable a more accurate assessment of potential risks and hazards. And weighting and summing the basic score, the model coefficient and the risk coefficient according to a certain weight to obtain a final safety score. This weighted calculation takes into account the relative importance between the different factors, thereby improving the accuracy and objectivity of the scoring. In summary, according to the embodiment, by comprehensively considering the driving experience of the driver, the characteristics of the vehicle type and the related factors of the transportation task, the accuracy of calculating the safety score is improved. By quantifying and measuring the contribution degree of a plurality of factors and carrying out reasonable weighted calculation, the driving risk and the safety can be more accurately estimated.

Step 108: screening a target driver among a plurality of initial drivers according to the safety score;

and taking the initial driver with the safety score exceeding the threshold value as a target driver.

Step 109: obtaining distances between a plurality of target drivers and the positions of the vehicles, ranking the target drivers according to the distances, and outputting ranking results; the ranking result is used for scheduling the corresponding driver to execute the transportation task of the current vehicle.

Since each driver is located differently from the vehicle, in order to allocate drivers nearby, the present application ranks the target drivers according to distance and outputs the ranking result. And schedule the top ranked driver to perform the transportation task of the current vehicle.

In the embodiment, by acquiring vehicle information, calculating the performance type and the performance score of the vehicle according to the vehicle information; obtaining driving character type and driving preference score corresponding to each of a plurality of drivers based on driving character test results; screening a plurality of initial drivers according to the performance type, the driving personality type, the driving preference score and the performance score; acquiring transportation task information, vehicle types and vehicle ages; the transportation task information comprises a transportation main body type, a travel distance, a value grade, a congestion degree and road complexity; acquiring historical driving information corresponding to each of a plurality of initial drivers, wherein the historical driving information comprises driving age, driving safety level, driving duration of the vehicle type and average vehicle speed; calculating a safety coefficient according to the historical driving information, the vehicle type and the vehicle age; calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information; screening a target driver among a plurality of initial drivers according to the safety score; obtaining distances between a plurality of target drivers and the positions of the vehicles, ranking the target drivers according to the distances, and outputting ranking results; the ranking result is used for scheduling the corresponding driver to execute the transportation task of the current vehicle. In the above scheme, in order to improve the matching accuracy, a plurality of vehicle information, transportation task information and driving characteristics of the driver are respectively acquired, and a performance type and a driving characteristics type are obtained based on the vehicle information, so that a plurality of initial drivers are primarily screened. And further, calculating the safety coefficient according to the historical driving information, the vehicle type and the vehicle age of the initial driver. And calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information. And screening the target drivers according to the safety scores, and performing ranking recommendation based on the target drivers and the vehicle positions. Because the matching degree among drivers, vehicles and transportation tasks is comprehensively evaluated based on the plurality of dimension information, the safety scores are obtained, drivers corresponding to different vehicles are matched based on the safety scores, and the matching precision is high. Reasonable assessment of the driver and reasonable allocation of suitable drivers for different vehicles are achieved.

Referring to fig. 2, fig. 2 shows a schematic diagram of a safety-based intelligent vehicle dispatching device according to the present invention, and fig. 2 shows a safety-based intelligent vehicle dispatching device according to the present invention, which includes:

a first acquisition unit 21 for acquiring vehicle information from which a performance type and a performance score of the vehicle are calculated; the vehicle information includes power, handling, braking performance, comfort and vehicle safety level;

a second obtaining unit 22, configured to obtain driving personality types and driving preference scores corresponding to the multiple drivers based on the driving personality test results;

a first screening unit 23 for screening a plurality of initial drivers according to the performance type, the driving style type, the driving preference score, and the performance score;

a third acquiring unit 24 for acquiring transportation task information, a vehicle model, and a vehicle age; the transportation task information comprises a transportation main body type, a travel distance, a value grade, a congestion degree and road complexity;

a fourth obtaining unit 25, configured to obtain historical driving information corresponding to each of the plurality of initial drivers, where the historical driving information includes a driving age, a driving safety level, a driving duration of the vehicle type, and an average vehicle speed;

A first calculation unit 26 for calculating a safety factor based on the historical driving information, the vehicle model, and the vehicle age;

a second calculation unit 27 for calculating a safety score based on the historical driving information, the safety coefficient, and the transportation task information;

a second screening unit 28 for screening a target driver among a plurality of the initial drivers according to the safety score;

an output unit 29 for acquiring distances between a plurality of the target drivers and the vehicle position, ranking the target drivers according to the distances, and outputting a ranking result; the ranking result is used for scheduling the corresponding driver to execute the transportation task of the current vehicle.

According to the intelligent vehicle scheduling device based on safety, the performance type and the performance score of the vehicle are calculated according to the vehicle information by acquiring the vehicle information; obtaining driving character type and driving preference score corresponding to each of a plurality of drivers based on driving character test results; screening a plurality of initial drivers according to the performance type, the driving personality type, the driving preference score and the performance score; acquiring transportation task information, vehicle types and vehicle ages; the transportation task information comprises a transportation main body type, a travel distance, a value grade, a congestion degree and road complexity; acquiring historical driving information corresponding to each of a plurality of initial drivers, wherein the historical driving information comprises driving age, driving safety level, driving duration of the vehicle type and average vehicle speed; calculating a safety coefficient according to the historical driving information, the vehicle type and the vehicle age; calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information; screening a target driver among a plurality of initial drivers according to the safety score; obtaining distances between a plurality of target drivers and the positions of the vehicles, ranking the target drivers according to the distances, and outputting ranking results; the ranking result is used for scheduling the corresponding driver to execute the transportation task of the current vehicle. In the above scheme, in order to improve the matching accuracy, a plurality of vehicle information, transportation task information and driving characteristics of the driver are respectively acquired, and a performance type and a driving characteristics type are obtained based on the vehicle information, so that a plurality of initial drivers are primarily screened. And further, calculating the safety coefficient according to the historical driving information, the vehicle type and the vehicle age of the initial driver. And calculating a safety score according to the historical driving information, the safety coefficient and the transportation task information. And screening the target drivers according to the safety scores, and performing ranking recommendation based on the target drivers and the vehicle positions. Because the matching degree among drivers, vehicles and transportation tasks is comprehensively evaluated based on the plurality of dimension information, the safety scores are obtained, drivers corresponding to different vehicles are matched based on the safety scores, and the matching precision is high. Reasonable assessment of the driver and reasonable allocation of suitable drivers for different vehicles are achieved.

Fig. 3 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 3, a terminal device 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32 stored in said memory 31 and executable on said processor 30, for example a safety-based intelligent scheduler for a vehicle. The processor 30, when executing the computer program 32, implements the steps of each of the above-described embodiments of the intelligent vehicle scheduling method based on security, such as steps 101 through 109 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 32, performs the functions of the units in the above-described device embodiments, such as the functions of the units 21 to 29 shown in fig. 2.

By way of example, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units may be a series of computer program instruction segments capable of performing a specific function describing the execution of the computer program 32 in the one terminal device 3. For example, the computer program 32 may be partitioned into units having the following specific functions:

Including but not limited to a processor 30 and a memory 31. It will be appreciated by those skilled in the art that fig. 3 is merely an example of one type of terminal device 3 and is not meant to be limiting as to one type of terminal device 3, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the one type of terminal device may also include input and output devices, network access devices, buses, etc.

The processor 30 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the one terminal device 3. The memory 31 is used for storing the computer program and other programs and data required for the one roaming control device. The memory 31 may also be used for temporarily storing data that has been output or is to be output.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present invention, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

Embodiments of the present invention also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps for implementing the various method embodiments described above.

Embodiments of the present invention provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that enable the implementation of the method embodiments described above.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to a detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is monitored" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon monitoring a [ described condition or event ]" or "in response to monitoring a [ described condition or event ]".

Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the invention. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.

Claims

1. The intelligent vehicle dispatching method based on safety is characterized by comprising the following steps of:

2. The intelligent scheduling method for a vehicle based on safety according to claim 1, wherein the step of acquiring vehicle information and calculating a performance type and a performance preference score of the vehicle based on the vehicle information comprises:

respectively calculating the modes of a plurality of target vectors;

the maximum modulus is taken as the performance preference score.

3. The safety-based intelligent scheduling method of a vehicle according to claim 1, wherein the step of screening a plurality of initial drivers according to the performance type, the driving personality type, the driving preference score, and the performance score comprises:

Acquiring a preset driving character type mapped by the performance type;

4. The intelligent scheduling method for a vehicle based on safety according to claim 1, wherein the step of calculating a safety factor based on the historical driving information, the vehicle model, and the vehicle age comprises:

constructing a plurality of historical driving information as a feature matrix;

5. The intelligent scheduling method for a vehicle based on safety according to claim 4, wherein the step of calculating the safety factor based on a plurality of the characteristic values, the vehicle model, and the vehicle age comprises:

Obtaining weight coefficients corresponding to the characteristic values respectively, substituting the weight coefficients, the characteristic values, the vehicle model and the vehicle age into a formula I to obtain the safety coefficient;

equation one:

wherein S represents the safety factor, x _i Represents the ith characteristic value, x represents the average value of a plurality of characteristic values, w _i Represents the ith said weight coefficient, n represents the number of said feature values or said weight coefficients,and the parameter value of the vehicle model is represented, and the parameter value of the vehicle age is represented by beta.

6. The intelligent scheduling method for a vehicle based on safety according to claim 1, wherein the step of calculating a safety score based on the historical driving information, the safety factor, and the transportation task information comprises:

7. The intelligent scheduling method for vehicles based on safety according to claim 6, wherein the step of calculating a risk factor of a transportation task according to the average vehicle speed, the transportation subject type, the travel distance, the congestion degree, the road complexity, and the value class comprises:

8. The intelligent scheduling method for a vehicle based on safety according to claim 7, wherein the step of calculating a risk factor for a transportation mission based on the adjustment factor, the average vehicle speed, the trip distance, the congestion degree, and the road complexity comprises:

formula II:

9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 8 when the computer program is executed.

10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 8.