CN117709832A

CN117709832A - Transportation information generation method, device, equipment and computer readable medium

Info

Publication number: CN117709832A
Application number: CN202311628278.9A
Authority: CN
Inventors: 谢枫; 戎袁杰; 侯平; 王国伟; 李志新; 陈曦; 王丽君; 隋志巍; 王志波; 闫小浩; 陈恩光; 刘建爽; 张方明; 任科洁; 郜珩; 古恒锐
Original assignee: State Grid Information and Telecommunication Co Ltd; Beijing Guodiantong Network Technology Co Ltd; State Grid Materials Co Ltd
Current assignee: State Grid Information and Telecommunication Co Ltd; Beijing Guodiantong Network Technology Co Ltd; State Grid Materials Co Ltd
Priority date: 2023-11-30
Filing date: 2023-11-30
Publication date: 2024-03-15

Abstract

Embodiments of the present disclosure disclose transportation information generation methods, apparatuses, devices, and computer-readable media. One embodiment of the method comprises the following steps: responding to the acquired transport data information of the target transport vehicle updated by the Internet of things data updating platform, and carrying out data filtering on the transport data information; data integration is carried out on the target transportation data information to obtain a vehicle transportation data information set; generating transportation route information, transportation early warning information and early warning processing information according to the vehicle transportation data information set; carrying out data visualization on the transportation route information, the transportation early warning information and the early warning processing information; and generating transportation information according to the visualized transportation route information, the visualized transportation early warning information and the visualized early warning processing information. According to the method and the system, the accuracy and the comprehensiveness of the generated transportation information can be improved, the experience of a user is improved, the waste of article resources caused by damage of goods is reduced, and the waste of hardware resources is reduced.

Description

Transportation information generation method, device, equipment and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a method, an apparatus, a device, and a computer readable medium for generating transportation information.

Background

The method comprises the steps of acquiring data of a transport vehicle, wherein the acquired data can be used for generating transport information, and the generated transport information can be used for carrying out early warning and recommending a driving route for the transport vehicle. At present, when acquiring data information of a transport vehicle, a method may be generally adopted in which a large number of sensors are installed on the vehicle to acquire data of the transport vehicle, and then transport information of the vehicle is generated according to the acquired data, and the acquired transport information may include a recommended route for the vehicle to travel and early warning information for the vehicle, wherein the method of determining the recommended route for the vehicle may be to determine the route of the transport vehicle according to an end point and a start point of the transport vehicle.

However, the inventors found that when the transportation information of the transportation vehicle is determined in the above manner, there are often the following technical problems:

firstly, directly acquiring data of a vehicle through a sensor arranged on the vehicle, wherein the data detected by the arranged sensor is single, the detected data range is smaller, the acquired data quantity is smaller, the generated transportation information is further poorer in comprehensiveness and lower in accuracy, the running safety of the vehicle and the safety of the carried goods are lower, the experience of a user is poorer, and the waste of article resources is more caused by the damage of the goods; and a large number of sensors are additionally arranged on the vehicle to acquire the data of the vehicle, so that the waste of hardware resources is caused.

Secondly, determining a route of vehicle transportation only according to the end point and the start point of the transportation vehicle, determining a recommended driving route without considering the driving habit of the vehicle driver, and individually recommending the driving route for the driver, so that the comprehensiveness of the recommended driving route is poor, and the experience of the user is poor.

The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.

Disclosure of Invention

The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose transportation information generation methods, apparatuses, electronic devices, and computer-readable media to solve one or more of the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide a transportation information generation method, the method including: in response to obtaining transport data information of a target transport vehicle updated by an internet of things data updating platform, carrying out data filtering processing on the transport data information to obtain transport data information after the data filtering processing as target transport data information, wherein the target transport data information and the transport data information comprise vehicle distance information, vehicle environment information, vehicle running state information, vehicle driver information, transport track information and carriage detection information, and the vehicle running state information comprises vehicle speed information, vehicle running shake information and vehicle impact acceleration information; carrying out data integration processing on the target transportation data information to obtain a vehicle transportation data information set; generating transportation route information corresponding to the target transportation vehicle according to the vehicle transportation data information set; generating transportation early warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set; responding to the fact that the transportation early-warning information meets preset early-warning conditions, and generating early-warning processing information corresponding to the target transportation vehicle according to the transportation early-warning information; carrying out data visualization processing on the transportation route information, the transportation early warning information and the early warning processing information; and generating transportation information corresponding to the target transportation vehicle according to the visualized transportation route information, the visualized transportation early warning information and the visualized early warning information.

In a second aspect, some embodiments of the present disclosure provide a transportation information generating apparatus, the apparatus including: the data filtering processing unit is configured to respond to the acquired transport data information of the target transport vehicle updated by the Internet of things data updating platform, perform data filtering processing on the transport data information, and obtain transport data information after the data filtering processing as target transport data information, wherein the target transport data information and the transport data information comprise vehicle distance information, vehicle surrounding environment information, vehicle running state information, vehicle driver information, transport track information and carriage detection information, and the vehicle running state information comprises vehicle speed information, vehicle running jitter information and vehicle impact acceleration information; the data integration processing unit is configured to perform data integration processing on the target transportation data information to obtain a vehicle transportation data information set; a first generation unit configured to generate transportation route information corresponding to the target transportation vehicle based on the vehicle transportation data information set; a second generation unit configured to generate transportation warning information corresponding to the target transportation vehicle based on the vehicle transportation data information set; a third generation unit configured to generate, in response to determining that the transportation warning information satisfies a preset warning condition, warning processing information corresponding to the target transportation vehicle according to the transportation warning information; the data visualization processing unit is configured to perform data visualization processing on the transportation route information, the transportation early warning information and the early warning processing information; and a fourth generation unit configured to generate transportation information corresponding to the target transportation vehicle based on the visualized transportation route information, the visualized transportation warning information, and the visualized warning information.

In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.

In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

The above embodiments of the present disclosure have the following advantageous effects: the transportation information obtained by the transportation information generation method of some embodiments of the present disclosure can improve the driving safety of the vehicle and the safety of the carried goods, improve the experience of the user and reduce the waste of the article resources caused by the damage of the goods; and reduces the waste of hardware resources. Specifically, the driving safety of the vehicle and the safety of the carried goods are low, the experience of the user is poor, and the damage of the goods causes more waste of the goods resources, and the reason for the waste of the hardware resources is as follows: the method comprises the steps that data of a vehicle are directly obtained through a sensor arranged on the vehicle, the data detected by the sensor are single, the range of the detected data is smaller, the obtained data are smaller, the generated transportation information is poorer in comprehensiveness and lower in accuracy, the running safety of the vehicle and the safety of carried goods are lower, the experience of a user is poorer, and the waste of goods resources is more due to the damage of the goods; and a large number of sensors are additionally arranged on the vehicle to acquire the data of the vehicle, so that the waste of hardware resources is caused. Based on this, in the transportation information generating method according to some embodiments of the present disclosure, first, in response to obtaining the transportation data information of the target transportation vehicle updated by the internet of things data updating platform, data filtering processing is performed on the transportation data information, and the transportation data information after the data filtering processing is obtained as the target transportation data information. The target transportation data information and the transportation data information comprise vehicle distance information, vehicle whole environment information, vehicle driving state information, vehicle driver information, transportation track information and carriage detection information, and the vehicle driving state information comprises vehicle speed information, vehicle driving shake information and vehicle impact acceleration information. Therefore, the transportation data information of the target transportation vehicle can be obtained from the internet of things data updating platform, the transportation data information of the target transportation vehicle can be generated, and the transportation data information after filtering processing can be obtained. And then, carrying out data integration processing on the target transportation data information to obtain a vehicle transportation data information set. Thus, a data information set corresponding to the target transport vehicle can be obtained, and the acquired transport data information can be integrated. And then, generating the transportation route information corresponding to the target transportation vehicle according to the vehicle transportation data information set. Thus, the route information recommended for the vehicle can be obtained. And secondly, generating transportation early warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set. Thus, risk early warning of the transport vehicle can be obtained. And then, in response to determining that the transportation early-warning information meets a preset early-warning condition, generating early-warning processing information corresponding to the target transportation vehicle according to the transportation early-warning information. Thus, a mode of handling the target transport vehicle can be obtained. And secondly, carrying out data visualization processing on the transportation route information, the transportation early warning information and the early warning processing information. Thus, the obtained transportation route information, the transportation early warning information and the early warning processing information can be subjected to visual processing, and can be used for displaying the obtained transportation route information, the transportation early warning information and the early warning processing information. And finally, generating transportation information corresponding to the target transportation vehicle according to the visualized transportation route information, the visualized transportation early warning information and the visualized early warning information. Therefore, the method can be used for recommending routes for the target transport vehicle and carrying out risk early warning. Also, because the transportation data of the vehicle is not obtained by installing a large number of sensors on the vehicle, the vehicle data in the internet of things data updating platform can be directly obtained, wherein the internet of things data updating platform can be an internet of things management platform, a platform for storing the data of the vehicle can be a platform for storing the data of the vehicle, and the internet of things data updating platform can be used for obtaining a large amount of vehicle data from the intelligent device for monitoring the special electric equipment. The data detected by the developed intelligent equipment is more comprehensive than the data detected by the common sensor, and the intelligent equipment further comprises detected vehicle running shake information, vehicle impact acceleration information and the like, so that the comprehensiveness of the detected data is improved, the range of the detected data is enlarged, and the number of acquired data is increased. The transportation information is generated according to the data acquired from the Internet of things data updating platform, so that the comprehensiveness and accuracy of the generated transportation information can be improved, the driving safety of vehicles and the safety of carried goods are improved, the experience of users is improved, and the condition that the goods are damaged to cause waste of article resources is reduced. And the data can be directly obtained from the Internet of things data updating platform, so that the number of additional sensors mounted on the vehicle is reduced. Therefore, the condition that goods are damaged to cause waste of article resources is reduced, and waste of hardware resources is reduced.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a flow chart of some embodiments of a transportation information generation method according to the present disclosure;

FIG. 2 is a schematic structural view of some embodiments of a transportation information generating device according to the present disclosure;

fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates a flow 100 of some embodiments of a transportation information generation method according to the present disclosure. The transportation information generation method comprises the following steps:

Step 101, in response to obtaining the transportation data information of the target transportation vehicle updated by the internet of things data updating platform, performing data filtering processing on the transportation data information to obtain transportation data information after the data filtering processing as target transportation data information.

In some embodiments, in response to acquiring the transportation data information of the target transportation vehicle updated by the internet of things data updating platform, an execution subject (e.g., a vehicle-mounted terminal) of the transportation information generating method performs data filtering processing on the transportation data information, and obtains the transportation data information after the data filtering processing as the target transportation data information. Wherein, the target transportation data information and the transportation data information may each include vehicle distance information, vehicle environment information, vehicle driving state information, vehicle driver information, transportation track information, and car detection information. The vehicle running state information may include vehicle speed information, vehicle running shake information, and vehicle jerk information. The above-described vehicle running state information may characterize a state in which the vehicle is running. The vehicle speed information may characterize a travel speed of the target transport vehicle. The vehicle running shake information may characterize the number of times the target transport vehicle shakes while running. The above-described vehicle jerk information may be indicative of a value of acceleration or deceleration experienced by the vehicle at an instant during a collision of the vehicle. The transportation data information may characterize data of the target transportation vehicle during transportation. The target transportation data information may characterize the filtered transportation data information. The internet of things data updating platform can be an internet of things management platform. The internet of things data updating platform can store the transportation data information of the vehicle. The data in the internet of things data updating platform can be data acquired from intelligent equipment for monitoring the special power equipment. The data interaction mode between the execution body and the internet of things data update platform can be an MQTT data interaction mode. The vehicle distance information may characterize a distance between the target transport vehicle and each of the vehicles corresponding to the four orientations of the target transport vehicle. The four orientations may be forward, rearward, left and right. The vehicle-wide environment information may characterize traffic conditions within a preset range centered on the target transport vehicle. The specific range of the preset range is not particularly limited herein. The vehicle driver information may characterize a driving characteristic of a driver driving the target transport vehicle. For example, the vehicle driver information may be: "driver information: the driver 1 likes to drive on an overhead route, likes to drive on a route with few traffic lights, and likes to drive on a route with supermarkets on two sides of the route. The driver information may represent a number corresponding to the driver. The transportation trajectory information may characterize a trajectory characteristic of the travel of the target transportation vehicle. The car detection information may characterize detected temperature data and smell index data within the target transport vehicle car. The temperature data may be indicative of a temperature within a cabin of the target transportation vehicle. The odor index data may be indicative of an odor index within a passenger compartment of the target transportation vehicle. The transportation data information may include first transportation data information. The first transportation data information in the above-described respective first transportation data information may be vehicle distance information, vehicle surrounding environment information, vehicle running state information, vehicle driver information, transportation track information, or cabin detection information. In practice, firstly, the executing body can acquire the transportation data information of the target transportation vehicle information from the internet of things data updating platform through an MQTT data interaction mode. Then, the above transportation data information may be subjected to data filtering processing in various ways, and the transportation data information after the data filtering processing is obtained as target transportation data information.

In some optional implementations of some embodiments, in response to obtaining the transportation data information of the target transportation vehicle updated by the internet of things data update platform, the executing body may perform data filtering processing on the transportation data information to obtain the transportation data information after the data filtering processing as the target transportation data information:

and a first step of performing null value detection processing on the transportation data information to determine whether first transportation data information meeting a preset null value condition exists in the transportation data information. The preset null value condition may be that the first transportation data information is null. In practice, the executing body may detect each first transportation data information included in the transportation data information through an isnan function.

And a second step of deleting the first transportation data information meeting the preset null value condition from the transportation data information to obtain changed transportation data information in response to determining that the first transportation data information meeting the preset null value condition is included in the transportation data information. The change transportation data information may represent transportation data information after deleting the first transportation data information satisfying the preset null value condition. The modified transportation data information may include each of the second transportation data information. The respective second transportation data information may be vehicle distance information, vehicle environment information, vehicle running state information, vehicle driver information, transportation trajectory information, or cabin detection information. In practice, the executing body may delete each first transportation data information satisfying the preset null condition in the transportation data information.

And a third step of selecting arbitrary second transportation data information from the changed transportation data information.

And a fourth step of determining each second transportation data information different from the selected second transportation data information in the changed transportation data information as a second transportation data information set. Wherein the second transportation data information included in the second transportation data information set is different from the selected second transportation data information. In practice, first, the executing entity may determine the similarity between the selected second transportation data information and each second transportation data information that is not selected in the modified transportation data information through the euclidean distance, so as to obtain the respective similarities. And then, determining each piece of second transportation data information corresponding to each similarity meeting the preset similarity condition as a second transportation data information set. The predetermined similarity condition may be that the similarity is smaller than the predetermined similarity. Specific values of the above preset similarity are not specifically limited herein.

And fifthly, determining the relevance between the selected second transportation data information and each second transportation data information in the second transportation data information set to obtain each relevance. Wherein the correlation of the respective correlations may characterize a correlation coefficient of the selected second transportation data information with the second transportation data information of the second transportation data information set. In practice, the executing body may determine the relevance between the selected second transportation data information and each second transportation data information in the second transportation data information set by using a pearson correlation coefficient method, so as to obtain each relevance.

And sixthly, deleting the second transportation data information meeting the preset deleting condition in the second transportation data information set in response to the fact that each obtained relevance meets the preset relevance condition, and obtaining an updated second transportation data information set. The preset correlation condition may be that the number of the respective correlations greater than the preset correlation is greater than a preset number. The specific value of the preset correlation is not specifically limited herein. The preset amount may be eighty percent of the amount of each correlation obtained. The predetermined deletion condition may be that a correlation between the second transportation data information in the second transportation data information set and the selected second transportation data information is smaller than the predetermined correlation. In practice, the executing body may delete the second transportation data information in the second transportation data information set, where the correlation between the second transportation data information and the selected second transportation data information is smaller than the preset correlation, to obtain an updated second transportation data information set.

Seventh, the obtained updated second transportation data information set and the selected second transportation data information are determined as target transportation data information.

And 102, carrying out data integration processing on the target transportation data information to obtain a vehicle transportation data information set.

In some embodiments, the executing body may perform data integration processing on the target transportation data information to obtain a vehicle transportation data information set. The vehicle transportation data information set can represent the integrated and processed target transportation data information. The vehicle transportation data information set may include individual vehicle transportation data information. The vehicle transportation data information in the respective vehicle transportation data information may be the vehicle distance information, the vehicle surrounding environment information, the vehicle running state information, the vehicle driver information, the transportation track information, or the vehicle cabin detection information. In practice, the executing body may determine, as the vehicle transportation data information, second transportation data information representing the vehicle distance information, second transportation data information representing the vehicle surrounding environment information, second transportation data information representing the vehicle driving state information, second transportation data information representing the vehicle driver information, second transportation data information representing the transportation track information, and second transportation data information representing the vehicle cabin detection information in the target transportation data information, respectively, to obtain a vehicle transportation data information set.

And step 103, generating transportation route information corresponding to the target transportation vehicle according to the vehicle transportation data information set.

In some embodiments, the executing body may generate the transportation route information corresponding to the target transportation vehicle according to the vehicle transportation data information set. Wherein, the transportation route information can represent a recommended driving route. In practice, the executing body may generate the transportation route information corresponding to the target transportation vehicle from the vehicle transportation data information set in various ways.

In some optional implementations of some embodiments, the executing entity may generate the transportation route information corresponding to the target transportation vehicle according to the vehicle transportation data information set by:

first, vehicle driver portrait information corresponding to the target transport vehicle is constructed based on the vehicle driver information included in the transport data information set. Wherein the vehicle driver portrayal information may characterize the driving characteristics of a driver driving the target transportation vehicle. In practice, the executing entity may construct vehicle driver portrayal information corresponding to the target transport vehicle through a decision tree algorithm.

And a second step of acquiring preset vehicle driver evaluation information corresponding to the vehicle driver information from a preset vehicle driver evaluation information set. The preset vehicle driver evaluation information in the preset vehicle driver evaluation information set may be an evaluation statement of a driver driving the target transport vehicle by a driver monitoring user. The driver monitoring user may be a user who monitors the driver. The preset vehicle driver evaluation information in the preset vehicle driver evaluation information set may represent a correspondence between driver information and evaluation information. The preset vehicle driver evaluation information in the above-described set of preset vehicle driver evaluation information may include driver information and evaluation information. The evaluation information may characterize the driver monitoring user's evaluation statement for the driver. For example, the preset vehicle driver evaluation information may be "driver information: driver No. 1, evaluation information: like driving on routes with few traffic lights). In practice, the execution subject may acquire, from a set of preset vehicle driver evaluation information, preset vehicle driver evaluation information including the same driver information as that included in the vehicle driver information as preset vehicle driver evaluation information corresponding to the vehicle driver information.

And thirdly, acquiring each route pattern information and each target route detail information corresponding to the target transportation vehicle information according to the transportation end point information of the target transportation vehicle information. The destination information may be a destination of the target transport vehicle. The individual roadmap information described above may characterize an image of the drawn route. Each of the respective target route detail information corresponds to one of the respective route pattern information. The target route detail information in the respective target route detail information may characterize a route. For example, the destination route detail information may be: "there are 10 traffic lights, which need to go up 2 times overhead". In practice, first, the executing body may acquire each preset route information from the preset route information set. Then, each route pattern information and each target route detail information in each of the acquired preset route information are determined as each route pattern information and each target route detail information corresponding to the above-mentioned target transport vehicle information. The preset route information in the preset route information set may include route pattern information and route detail information of the target transport vehicle reaching a destination represented by the transportation end information.

And a fourth step of constructing each route image information corresponding to each route image information based on each route image information. Wherein the route pattern information in the route pattern information corresponds to the route pattern information in the route pattern information. The route image information in each route image information can characterize the route. In practice, the executing body may construct route image information corresponding to each of the route image information through a decision tree algorithm, so as to obtain each route image information.

And fifthly, converting each piece of route image information in the route image information to obtain each piece of numerical vector information. Wherein the numerical vector information in the respective numerical vector information may characterize a numerical vector. In practice, the execution body may convert each of the route drawing information into numerical vector information according to One-Hot encoding, to obtain each of the numerical vector information.

Sixth, for each of the above-described individual numerical vector information, the following steps are performed:

a first sub-step of performing feature conversion processing on the numerical vector information and the vehicle driver image information to obtain first feature vector information corresponding to the numerical vector information and the vehicle driver image information. Wherein the first feature vector information may represent a real vector corresponding to the numerical vector information and the vehicle driver portrait information. In practice, the execution body may perform feature conversion processing on the numerical vector information and the vehicle driver portrait information according to an Embedding layer.

And a second sub-step of performing feature extraction processing on the target route detail information corresponding to the preset vehicle driver evaluation information and the numerical vector information to obtain second feature vector information. The second feature vector information may represent text features of preset vehicle driver evaluation information and the numerical value vector information. The execution subject may perform feature extraction processing on the target route detail information corresponding to the preset vehicle driver evaluation information and the numerical vector information by using an attention model based on an encodable-decode framework.

And a third sub-step of performing feature fusion processing on the first feature vector information and the second feature vector information to obtain fusion feature information. The fused feature information may represent a fused feature of the first feature vector information and the second feature vector information. In practice, the execution subject may perform feature fusion processing on the first feature vector information and the second feature vector information through an information fusion technique. For example, the information Fusion technique may be a Fusion layer.

And a fourth sub-step of inputting the fusion characteristic information into a pre-trained route score information generation model to obtain predictive score information corresponding to the fusion characteristic information. The pre-trained route score information generation model may be a model of component value information. The predictive value information may be a probability value between the route representation information and the vehicle driver representation information. For example, the pre-trained route score information generation model may be a Deep FM model.

And fifth, performing score conversion processing on the obtained predictive score information to obtain route score information corresponding to the numerical vector information. The route score information may be a score of a route corresponding to the numerical vector information. In practice, the executing body may perform score conversion processing on the obtained predicted score information according to the Sigmoid function, so as to obtain route score information.

Seventh, determining route pattern information meeting a preset route score condition from the route pattern information according to the obtained route score information. The preset route score condition may be that the corresponding route score information is the maximum route score information in the obtained route score information. In practice, first, the execution subject may determine the maximum route score information among the respective route score information as target route score information. Then, route pattern information corresponding to the target route score information among the respective route pattern information is determined as route pattern information satisfying the preset route score condition.

Eighth, the determined route pattern information and the destination route detail information corresponding to the determined route pattern information are used as the transportation route information corresponding to the destination transportation vehicle.

The above technical solution is an invention point of the embodiments of the present disclosure, and solves the second technical problem mentioned in the background art, namely, determining a route of vehicle transportation only according to an end point and a start point of a transportation vehicle, determining a recommended driving route without considering a driving habit of a driver of the vehicle, and individually recommending the driving route for the driver, which results in poor comprehensiveness of the recommended driving route and poor experience of the user. Factors that cause poor comprehensiveness of the recommended travel route and poor experience for the user are often as follows: the route of the vehicle transportation is determined only according to the end point and the start point of the transportation vehicle, the recommended driving route is not considered in combination with the driving habit of the driver of the vehicle, and the personalized recommended driving route is not performed for the driver. If the factors are solved, the effect of improving the comprehensiveness of the recommended driving route and improving the experience of the user can be achieved. In order to achieve the effect, when a route is recommended to a driver, the recommended driving route is combined with the driving habit of the driver of the vehicle according to the image of the driver and the image of each route, the score of each route is determined, and the route with the largest score is determined as the recommended route, and the recommended driving route is not recommended only according to the terminal point and the starting point, but is personalized for the driver, so that the comprehensiveness of the recommended driving route is improved, and the experience of a user is improved.

And 104, generating transportation early warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set.

In some embodiments, the executing entity may generate the transportation early warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set. The transportation early warning information may be early warning of the target transportation vehicle. In practice, the executing body may generate the transportation early warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set in various manners.

In some optional implementations of some embodiments, the executing entity may generate the transportation early warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set by:

first, for each vehicle transportation data information in the vehicle transportation data information set, the following steps are performed:

and a first sub-step of generating first early warning information corresponding to the target transport vehicle as early warning information according to the vehicle distance information in response to determining that the vehicle transport data information characterizes the vehicle distance information. The vehicle distance information may include information on each vehicle distance. The distance information in each distance information may represent a distance between the target transport vehicle and a vehicle corresponding to any of four directions. The first warning information may represent warning of a vehicle distance between the target transport vehicle and other vehicles. The first early warning information may be: "too close to left vehicle, please adjust in time". In practice, first, the executing body may determine whether there is any vehicle distance information satisfying a preset vehicle distance early warning condition among the respective vehicle distance information included in the vehicle distance information. The preset vehicle distance early warning condition may be that the vehicle distance represented by the vehicle distance information is smaller than the preset vehicle distance. Here, specific values of the preset vehicle distance are not particularly limited. Then, in response to determining that the vehicle distance information meeting the preset vehicle distance early warning condition exists in the vehicle distance information, determining a first preset statement as first early warning information corresponding to the target transport vehicle as early warning information. The first preset sentence may be an early warning sentence for a vehicle distance between the target transport vehicle and other vehicles. For example, the first preset sentence may be: "too close to left vehicle, please adjust in time". And finally, determining a preset empty value as first early warning information corresponding to the target transport vehicle as early warning information in response to determining that no vehicle distance information meeting the preset vehicle distance early warning condition exists in the vehicle distance information.

And a second sub-step of generating second early warning information corresponding to the target transport vehicle as early warning information according to the vehicle body environment information in response to determining that the vehicle transport data information characterizes the vehicle body environment information. The vehicle environment information may include traffic accident event information within a preset range centering on the target transport vehicle. The traffic accident event information may characterize the traffic accident occurring. The second warning information may represent a warning sentence regarding vehicle-wide environmental information of the target transport vehicle. For example, the traffic accident event information may be "five hundred meters ahead has a rear-end collision event". In practice, first, the execution subject may determine whether traffic accident event information included in the vehicle-surrounding environment information is empty. Then, in response to determining that the traffic accident event information included in the vehicle-wide environment information is empty, a preset empty value is determined as second warning information corresponding to the target transport vehicle as warning information. And finally, in response to determining that the traffic accident event information included in the vehicle whole environment information is not empty, determining a second preset statement as second early warning information corresponding to the target transport vehicle as early warning information. Wherein the second preset sentence may represent an early warning sentence in terms of vehicle-body environment information about the target transport vehicle. For example, the second preset sentence may be "five hundred meters ahead has a rear-end traffic accident, please drive carefully.

And a third sub-step of generating third early warning information corresponding to the target transport vehicle as early warning information according to the vehicle running state information in response to determining that the vehicle running state information is characterized by the vehicle transport data information. The third early warning information may represent an early warning statement regarding vehicle running state information of the target transport vehicle. In practice, first, the executing body may determine whether the speed represented by the vehicle speed information included in the vehicle running state information is greater than a first preset speed value, whether the number of times of shake represented by the vehicle running shake information included is greater than a preset number of times, and whether the shock acceleration represented by the vehicle shock acceleration information included is greater than a second preset speed value. Then, in response to determining that the speed represented by the vehicle speed information is greater than the first preset speed value, determining the third preset sentence as a sub-warning sentence. And in response to determining that the speed represented by the vehicle speed information is less than or equal to the first preset speed value, determining a preset null value as a sub-early warning statement. And then, in response to determining that the shaking frequency represented by the vehicle driving shaking information is larger than the preset frequency, determining the fourth preset statement as a sub-early warning statement. And determining a preset null value as a sub-early warning sentence in response to determining that the number of times of the vehicle driving shake information representation is smaller than or equal to the preset number of times. And secondly, in response to determining that the impact acceleration represented by the vehicle impact acceleration information is larger than the second preset speed value, determining a fifth preset statement as a sub-early warning statement. And determining a preset null value as a sub-early warning statement in response to determining that the impact acceleration represented by the vehicle impact acceleration information is less than or equal to the second preset speed value. And finally, determining each obtained sub early warning statement as third early warning information corresponding to the target transport vehicle as early warning information. The specific values of the first preset speed value, the second preset speed value, and the preset times are not specifically limited herein. The third preset sentence, the fourth preset sentence, and the fifth preset sentence may each represent an early warning sentence in terms of vehicle running state information about the target transport vehicle. For example, the third preset sentence may be "please run at a reduced speed", the fourth preset sentence may be "shake too many times, please check the vehicle, and the fifth preset sentence may be" please check the vehicle off ".

And a fourth sub-step of generating fourth early warning information corresponding to the target transportation vehicle as early warning information according to the vehicle driver information in response to determining that the vehicle transportation data information characterizes the vehicle driver information. The vehicle driver information may include driver driving habit information and driving time information. The travel time information may characterize a driving time of a driver driving the target transport vehicle. The fourth warning information may be a warning statement regarding vehicle driver information of the target transport vehicle. In practice, first, the execution subject may determine whether or not the time characterized by the travel time information included in the vehicle driver information is greater than a preset time. For example, the preset time may be 3 hours. Then, in response to determining that the time characterized by the travel time information included in the vehicle driver information is greater than the preset time, a sixth preset sentence is determined as fourth warning information corresponding to the target transport vehicle as warning information. And then, in response to determining that the time represented by the travel time information included in the vehicle driver information is less than or equal to the preset time, determining a preset empty value as fourth early warning information corresponding to the target transport vehicle as early warning information. The sixth preset sentence may be an early warning sentence regarding vehicle driver information of the target transport vehicle. For example, the sixth preset sentence may be: "drive time is too long, please pause for rest".

And a fifth sub-step of generating fifth warning information corresponding to the target transport vehicle as warning information according to the car detection information in response to determining that the vehicle transport data information characterizes the car detection information, wherein the car detection information may include car temperature information and car smell index information. The car smell index information may characterize a smell index in a car of the target transport vehicle. The fifth warning information may represent a warning sentence regarding the cabin detection information of the target transport vehicle. In practice, first, the execution subject may determine whether the cabin temperature represented by the cabin temperature information included in the cabin detection information is greater than a preset temperature value. Then, in response to determining that the cabin temperature represented by the cabin temperature information included in the cabin detection information is greater than the preset temperature value, a seventh preset sentence is determined as fifth warning information that should be the target transport vehicle as warning information. And in response to determining that the cabin temperature represented by the cabin temperature information included in the cabin detection information is less than or equal to the preset temperature value, determining a preset empty value as fifth early warning information of the target transport vehicle as early warning information. Next, it may be determined whether or not the car smell index represented by the car smell index information included in the above-described car detection information is greater than a preset smell index. Then, in response to determining that the car smell index represented by the car smell index information included in the car detection information is greater than the preset smell index, an eighth preset sentence is determined as fifth warning information corresponding to the target transport vehicle as warning information. And in response to determining that the car smell index represented by the car smell index information included in the car detection information is equal to or less than the preset smell index, determining a preset null value as fifth warning information corresponding to the target transport vehicle as warning information. The specific values of the preset temperature value and the preset odor index are not specifically limited herein. The seventh preset sentence and the eighth preset sentence may each represent an early warning sentence in terms of the cabin detection information about the target transport vehicle. For example, the seventh preset sentence may be "the temperature in the cabin is too high, please check by parking". The eighth preset sentence may be "there is a pungent smell in the car, please check the car.

And a sixth sub-step of generating sixth early warning information corresponding to the target transport vehicle as early warning information according to the transport track information in response to determining that the vehicle transport data information characterizes the transport track information. The transportation track information may include transportation destination information and transportation direction information. The transportation direction information may characterize a direction of a destination of the target transportation vehicle and a traveling direction of the target transportation vehicle. For example, the transportation direction information may be forward east, and the traveling direction may be forward south. The sixth warning information may be a warning statement regarding transportation trajectory information of the target transportation vehicle. In practice, the execution subject may first determine whether the direction of the destination of the target transport vehicle, which is characterized by the transport direction information in the transport trajectory information, and the traveling direction of the target transport vehicle are the same. Then, in response to determining that the direction of the destination of the target transport vehicle, which is characterized by the transport direction information in the transport trajectory information, is the same as the traveling direction of the target transport vehicle, a preset null value is determined as sixth warning information corresponding to the target transport vehicle as warning information. In response to determining that the direction of the destination of the target transport vehicle represented by the transport direction information in the transport trajectory information is different from the traveling direction of the target transport vehicle, determining an eighth preset sentence as sixth early warning information corresponding to the target transport vehicle as early warning information. The eighth preset sentence may be an early warning sentence regarding transportation trajectory information of the target transportation vehicle. For example, the eighth preset sentence may be "deviation direction, please adjust in time".

And secondly, adding the early warning information meeting the preset non-empty condition in the generated early warning information to an early warning information set. The preset non-empty condition may be that the early warning information is non-empty. The early warning information in the early warning information set may be non-empty early warning information.

Thirdly, acquiring preset early warning level information corresponding to each early warning information in the early warning information set from the preset early warning level information set as target preset early warning level information to obtain a target preset early warning level information set. The preset early warning level information in the preset early warning level information set can represent the early warning level of the early warning information. The target preset early warning level information in the target preset early warning level information set may represent an early warning level of early warning information in the early warning information set. The preset pre-warning level information in the preset pre-warning level information set may include pre-warning information and a pre-warning level. The early warning level may represent the level of early warning information. For example, the preset pre-warning level information in the preset pre-warning level information set may be "pre-warning information: deviation direction, please adjust in time; early warning level: first-level early warning. In practice, first, the executing body may use, as the target preset early-warning level information set, each preset early-warning level information, which is the same as each early-warning information in the preset early-warning level information set, as early-warning information included in the preset early-warning level information set.

And fourthly, determining the early warning information set and the target preset early warning level information set as transportation early warning information.

And 105, generating early warning processing information corresponding to the target transport vehicle according to the transport early warning information in response to determining that the transport early warning information meets the preset early warning condition.

In some embodiments, in response to determining that the transportation alert information meets a preset alert condition, the executing entity may generate alert processing information corresponding to the target transportation vehicle according to the transportation alert information. The preset early warning condition may be that the transportation early warning information is not empty. The early warning processing information can be processing information of an early warning event characterized by transportation early warning information. In practice, in response to determining that the transportation early-warning information meets a preset early-warning condition, the executing body may generate early-warning processing information corresponding to the target transportation vehicle according to the transportation early-warning information in various manners.

In some optional implementations of some embodiments, in response to determining that the transportation early-warning information meets a preset early-warning condition, the executing entity may generate early-warning processing information corresponding to the target transportation vehicle according to the transportation early-warning information by:

The method comprises the first step of obtaining type information of the target transport vehicle and a preset early warning processing information set corresponding to the target transport vehicle. The preset early warning processing information in the preset early warning processing information set can represent a processing mode of the vehicle type represented by the type information. The type information may characterize a class of the target transport vehicle. For example, the type information may be a truck. For example, each preset early warning processing information in the preset early warning processing information set may be a processing manner of the truck. In practice, the executing body may acquire a preset pre-warning processing information set, where the type of the vehicle represented by the pre-warning processing information set is the same as the type of the vehicle represented by the type information, from the preset pre-warning processing information set group.

And secondly, acquiring preset early warning processing information corresponding to each early warning information in the transportation early warning information from the preset early warning processing information set as sub early warning processing information, and acquiring each sub early warning processing information as early warning processing information. The preset early warning processing information in the preset early warning processing information set may include early warning information and processing information. The processing information may be a manner of processing the early warning information. For example, the preset early warning processing information may be "early warning information: the interior of the carriage has pungent smell, and the vehicle is stopped for checking; processing information: the car ventilation is turned on. In practice, the executing body may acquire, from the preset early warning processing information set, each preset early warning processing information that includes early warning information identical to each early warning information included in the transportation early warning information as early warning processing information.

And 106, carrying out data visualization processing on the transportation route information, the transportation early warning information and the early warning processing information.

In some embodiments, the executing body may perform data visualization processing on the transportation route information, the transportation pre-warning information, and the pre-warning processing information. In practice, the execution subject may perform data visualization processing on the transportation route information, the transportation early warning information, and the early warning processing information in various manners.

In some optional implementations of some embodiments, the executing entity may perform data visualization processing on the transportation route information, the transportation pre-warning information, and the pre-warning processing information by:

the method comprises the steps of obtaining preset display template information representing route information from a preset display template information set to serve as first preset display template information. The preset display template information in the preset display template information set may be preset display template information representing route information or preset display template information representing an early warning display diagram. The preset display template information of the characterization route information may characterize a display template of a corresponding driving route. The preset display template information of the characterization early warning display diagram can be templates for characterizing corresponding early warning information and early warning processing information.

And a second step of filling the route image information and the target route detail information into the first preset display template information so as to perform visualization processing on the transportation route information. The first preset display template information may include a first filling area and a second filling area. The first filled region may characterize a filled region of the route image information. The second filled-in region may characterize a filled-in region of the target route detail information. In practice, first, the execution subject may add the route image information to the first filling area. Then, the above-mentioned target route detail information is added to the above-mentioned second filling area.

And thirdly, acquiring preset display template information representing the early warning display diagram from the preset display template information set to serve as second preset display template information. The second preset display template information comprises a preset level display area information set. Each preset level display area information in the preset level display area information set can represent a display area corresponding to the target preset early warning level information. For example, the preset level display area information in the preset level display area information set may represent a display area corresponding to the first level early warning.

And step four, acquiring each preset level color information corresponding to the target preset early warning level information set in the transportation early warning information from the preset level color information set. Wherein, the preset level color information in the preset level color information corresponds to the early warning information in the transportation early warning information. The preset level color information in the preset level color information set may represent a color corresponding to the early warning level. For example, the preset level color information may be "early warning information: early warning at a first stage; level color: red color).

And fifthly, acquiring each preset level display area information corresponding to the target preset early warning level information set in the transportation early warning information from the preset level display area information set, wherein the preset level display area information in each preset level display area information corresponds to the early warning information in the transportation early warning information.

And sixthly, rendering the preset level display area information according to the preset level color information and the transportation early warning information to obtain the rendered preset level display area information serving as first preset level display area information. The first preset level display area information in the first preset level display area information may represent preset level display area information after rendering processing. In practice, first, for each preset level display area information in the respective preset level display area information, the execution subject may use the pre-warning information corresponding to the preset level display area information as the target pre-warning information. And then, determining preset level color information corresponding to the target early warning information as preset level color information corresponding to the preset level display area information as target preset level color information. And finally, rendering the preset level display area information according to the target preset level color information by an image processor.

And seventhly, filling the early warning processing information into the first preset level display area information to obtain second preset level display area information so as to perform data visualization processing on the transportation early warning information and the early warning processing information. The second preset level display area information in the second preset level display area information may represent first preset level display area information filled with early warning processing information. In practice, first, the executing body may determine the pre-warning information corresponding to each sub-pre-warning information in the pre-warning information, so as to obtain each pre-warning information. And then adding each piece of sub-early warning processing information to the corresponding first preset level display area information to obtain each piece of second preset level display area information. The pre-warning information corresponding to the corresponding first preset level display area information is the same as the pre-warning information corresponding to the sub-pre-warning processing information.

And 107, generating transportation information of the corresponding target transportation vehicle according to the visualized transportation route information, the visualized transportation early warning information and the visualized early warning information.

In some embodiments, the executing body may generate the transportation information corresponding to the target transportation vehicle according to the visualized transportation route information, the visualized transportation early-warning information, and the visualized early-warning information. Wherein, the transportation information may include voice information and transportation image information. The transportation image information may represent first preset display template information after filling processing and second preset display template information after filling.

In some optional implementations of some embodiments, the executing body may generate the transportation information corresponding to the target transportation vehicle according to the visualized transportation route information, the visualized transportation early warning information, and the visualized early warning information by:

and determining the filled first preset display template information and the filled second preset display template information as the transport image information corresponding to the target transport vehicle.

And a second step of determining the target route detail information as first text information. The first text information may be text of the target route detail information.

And thirdly, determining the transportation early warning information and the early warning processing information as second text information. The second text information may represent the text of the transportation warning information and the warning processing information.

Fourth, generating voice information corresponding to the first text information and the second text information according to the first text information and the second text information. The speech information may characterize speech of the first text information and the second text information. First, the execution subject may determine the first text information and the second text information as target text information. The target text information may represent sentences of the first text information and the second text information. Then, text in the target text information may be converted into voice information corresponding to the first text information and the second text information by a voice synthesis technique. For example, the speech synthesis technique may be waveform splicing.

And fifth, determining the voice information and the transportation image information as transportation information corresponding to the target transportation vehicle.

Optionally, after step 107, first, the executing body may further display the transportation image information on an associated display device. Wherein, the above-mentioned associated display device may be a vehicle-mounted display screen.

The voice information may then be played at an associated voice playback device. The above-mentioned associated voice playing device may be a vehicle-mounted broadcaster.

With further reference to fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of a transportation information generation method, and these apparatus embodiments correspond to those method embodiments shown in fig. 1, and the apparatus is particularly applicable to various electronic devices.

As shown in fig. 2, the flow site processing information generating apparatus 200 of some embodiments includes: a data filtering processing unit 201, a data integration processing unit 202, a first generation unit 203, a second generation unit 204, a third generation unit 205, a data visualization processing unit 206, and a fourth generation unit 207. The data filtering processing unit 201 is configured to perform data filtering processing on the transport data information in response to acquiring the transport data information of the target transport vehicle updated by the internet of things data updating platform, so as to obtain transport data information after the data filtering processing as target transport data information, wherein the target transport data information and the transport data information each comprise vehicle distance information, vehicle surrounding environment information, vehicle running state information, vehicle driver information, transport track information and carriage detection information, and the vehicle running state information comprises vehicle speed information, vehicle running shake information and vehicle impact acceleration information; a data integration processing unit 202 configured to perform data integration processing on the target transportation data information to obtain a vehicle transportation data information set; a first generation unit 203 configured to generate transportation route information corresponding to the target transportation vehicle based on the vehicle transportation data information set; a second generation unit 204 configured to generate transportation warning information corresponding to the target transportation vehicle based on the vehicle transportation data information set; a third generating unit 205 configured to generate, in response to determining that the transportation warning information satisfies a preset warning condition, warning processing information corresponding to the target transportation vehicle according to the transportation warning information; a visualization processing unit 206 configured to perform data visualization processing on the transportation route information, the transportation warning information, and the warning processing information; fourth generation section 207 is configured to generate transportation information corresponding to the target transportation vehicle based on the visualized transportation route information, the visualized transportation warning information, and the visualized warning information.

It will be appreciated that the elements described in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting benefits described above for the method are equally applicable to the apparatus 200 and the units contained therein, and are not described in detail herein.

Referring now to fig. 3, a schematic diagram of an electronic device 300 (e.g., an in-vehicle terminal) suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.

As shown in fig. 3, the electronic device 300 may include a processing means 301 (e.g., a central processing unit, a graphics processor, etc.) that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.

It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: in response to obtaining transport data information of a target transport vehicle updated by an internet of things data updating platform, carrying out data filtering processing on the transport data information to obtain transport data information after the data filtering processing as target transport data information, wherein the target transport data information and the transport data information comprise vehicle distance information, vehicle environment information, vehicle running state information, vehicle driver information, transport track information and carriage detection information, and the vehicle running state information comprises vehicle speed information, vehicle running shake information and vehicle impact acceleration information; carrying out data integration processing on the target transportation data information to obtain a vehicle transportation data information set; generating transportation route information corresponding to the target transportation vehicle according to the vehicle transportation data information set; generating transportation early warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set; responding to the fact that the transportation early-warning information meets preset early-warning conditions, and generating early-warning processing information corresponding to the target transportation vehicle according to the transportation early-warning information; carrying out data visualization processing on the transportation route information, the transportation early warning information and the early warning processing information; and generating transportation information corresponding to the target transportation vehicle according to the visualized transportation route information, the visualized transportation early warning information and the visualized early warning information.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: the system comprises a data filtering processing unit, a data integration processing unit, a first generating unit, a second generating unit, a third generating unit, a data visualization processing unit and a fourth generating unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the first generation unit may also be described as "a unit that generates transportation route information corresponding to the target transportation vehicle from the above-described vehicle transportation data information set".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A transportation information generation method, comprising:

in response to obtaining transport data information of a target transport vehicle updated by an internet of things data updating platform, carrying out data filtering processing on the transport data information to obtain transport data information after the data filtering processing as target transport data information, wherein the target transport data information and the transport data information comprise vehicle distance information, vehicle surrounding environment information, vehicle running state information, vehicle driver information, transport track information and carriage detection information, and the vehicle running state information comprises vehicle speed information, vehicle running shake information and vehicle impact acceleration information;

carrying out data integration processing on the target transportation data information to obtain a vehicle transportation data information set;

generating transportation route information corresponding to the target transportation vehicle according to the vehicle transportation data information set;

generating transportation early warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set;

generating early warning processing information corresponding to the target transport vehicle according to the transport early warning information in response to determining that the transport early warning information meets a preset early warning condition;

Carrying out data visualization processing on the transportation route information, the transportation early warning information and the early warning processing information;

and generating transportation information corresponding to the target transportation vehicle according to the transportation route information after visualization, the transportation early warning information after visualization and the early warning information after visualization.

2. The method of claim 1, wherein the transportation data information includes first transportation data information, and the change transportation data information includes second transportation data information; and

the step of performing data filtering processing on the transportation data information to obtain transportation data information after the data filtering processing as target transportation data information includes:

performing null value detection processing on the transportation data information to determine whether first transportation data information meeting a preset null value condition exists in the transportation data information;

in response to determining that the transportation data information comprises first transportation data information meeting the preset null value condition, deleting the first transportation data information meeting the preset null value condition from the transportation data information to obtain changed transportation data information;

Selecting any second transportation data information from the changed transportation data information;

determining each of the modified transportation data information as a second transportation data information set;

determining the relevance of the selected second transportation data information and each second transportation data information in the second transportation data information set to obtain each relevance;

deleting the second transportation data information meeting the preset deleting condition in the second transportation data information set to obtain an updated second transportation data information set in response to the fact that each obtained relevance meets the preset relevance condition;

and determining the obtained updated second transportation data information set and the selected second transportation data information as target transportation data information.

3. The method of claim 1, wherein the performing data integration processing on the target transportation data information to obtain a vehicle transportation data information set includes:

and respectively determining the vehicle distance information, the vehicle surrounding environment information, the vehicle running state information, the vehicle driver information, the transportation track information and the carriage detection information in the target transportation data information as vehicle transportation data information to obtain a vehicle transportation data information set.

4. The method of claim 3, wherein the generating transportation warning information corresponding to the target transportation vehicle from the vehicle transportation data information set comprises:

for each vehicle transportation data information in the vehicle transportation data information set, performing the steps of:

generating first early warning information corresponding to the target transport vehicle as early warning information according to the vehicle distance information in response to determining that the vehicle transport data information characterizes the vehicle distance information, wherein the vehicle distance information comprises various vehicle distance information;

generating second early warning information corresponding to the target transport vehicle as early warning information according to the vehicle surrounding environment information in response to determining that the vehicle transportation data information characterizes the vehicle surrounding environment information, wherein the vehicle surrounding environment information comprises traffic accident event information within a preset range centering on the target transport vehicle;

generating third early warning information corresponding to the target transport vehicle as early warning information according to the vehicle running state information in response to determining that the vehicle transportation data information characterizes the vehicle running state information;

Generating fourth early warning information corresponding to the target transport vehicle as early warning information according to the vehicle driver information in response to determining that the vehicle transport data information characterizes the vehicle driver information, wherein the vehicle driver information comprises driver driving habit information and driving time information;

generating fifth early warning information corresponding to the target transport vehicle as early warning information according to the carriage detection information in response to determining that the vehicle transport data information characterizes the carriage detection information, wherein the carriage detection information comprises carriage temperature information and carriage smell index information;

in response to determining that the vehicle transportation data information characterizes the transportation track information, generating sixth early warning information corresponding to the target transportation vehicle as early warning information according to the transportation track information, wherein the transportation track information comprises transportation end information and transportation direction information; adding the early warning information meeting the preset non-empty condition in the generated early warning information to an early warning information set;

acquiring preset early warning level information corresponding to each early warning information in the early warning information set from the preset early warning level information set as target preset early warning level information to obtain a target preset early warning level information set;

And determining the early warning information set and the target preset early warning level information set as transportation early warning information.

5. The method of claim 4, wherein the generating pre-warning processing information corresponding to the target transportation vehicle from the transportation pre-warning information in response to determining that the transportation pre-warning information meets a preset pre-warning condition comprises:

acquiring type information of the target transport vehicle and a preset early warning processing information set corresponding to the target transport vehicle;

and acquiring preset early warning processing information corresponding to each early warning information in the transportation early warning information from the preset early warning processing information set as sub early warning processing information, and acquiring each sub early warning processing information as early warning processing information.

6. The method of claim 5, wherein the transportation route information includes route pattern information and destination route detail information; and

the data visualization processing for the transportation route information, the transportation early warning information and the early warning processing information comprises the following steps:

acquiring preset display template information representing route information from a preset display template information set as first preset display template information;

Filling the route image information and the target route detail information into the first preset display template information so as to carry out visual processing on the transportation route information;

acquiring preset display template information representing an early warning display diagram from the preset display template information set as second preset display template information, wherein the second preset display template information comprises a preset level display area information set;

acquiring each preset level color information corresponding to a target preset early warning level information set in the transportation early warning information from a preset level color information set, wherein the preset level color information in each preset level color information corresponds to early warning information in the transportation early warning information;

acquiring each preset level display area information corresponding to a target preset early warning level information set in the transportation early warning information from the preset level display area information set, wherein the preset level display area information in each preset level display area information corresponds to early warning information in the transportation early warning information;

rendering the display area information of each preset level according to the color information of each preset level and the transportation early warning information, and obtaining the display area information of each preset level after rendering as the display area information of each first preset level;

And filling the early warning processing information into the first preset level display area information to obtain the second preset level display area information so as to perform data visualization processing on the transportation early warning information and the early warning processing information.

7. The method of claim 6, wherein the generating transportation information corresponding to the target transportation vehicle based on the visualized transportation route information, the visualized transportation warning information, and the visualized warning information comprises:

determining the filled first preset display template information and the filled second preset display template information as transport image information corresponding to the target transport vehicle;

determining the destination route detail information as first text information;

determining the transportation early warning information and the early warning processing information as second text information;

generating voice information corresponding to the first text information and the second text information according to the first text information and the second text information;

and determining the voice information and the transportation image information as transportation information corresponding to the target transportation vehicle.

8. A transportation information generating apparatus comprising:

the data filtering processing unit is configured to respond to the acquired transport data information of the target transport vehicle updated by the Internet of things data updating platform, perform data filtering processing on the transport data information, and obtain transport data information after the data filtering processing as target transport data information, wherein the target transport data information and the transport data information both comprise vehicle distance information, vehicle surrounding environment information, vehicle running state information, vehicle driver information, transport track information and carriage detection information, and the vehicle running state information comprises vehicle speed information, vehicle running jitter information and vehicle impact acceleration information;

the data integration processing unit is configured to perform data integration processing on the target transportation data information to obtain a vehicle transportation data information set;

a first generation unit configured to generate transportation route information corresponding to the target transportation vehicle from the vehicle transportation data information set;

a second generation unit configured to generate transportation warning information corresponding to the target transportation vehicle according to the vehicle transportation data information set;

A third generation unit configured to generate early warning processing information corresponding to the target transport vehicle according to the transport early warning information in response to determining that the transport early warning information satisfies a preset early warning condition;

the data visualization processing unit is configured to perform data visualization processing on the transportation route information, the transportation early warning information and the early warning processing information;

and a fourth generation unit configured to generate transportation information corresponding to the target transportation vehicle according to the transportation route information after the visualization process, the transportation early warning information after the visualization process, and the early warning process information after the visualization process.

9. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.

10. A computer readable medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any of claims 1-7.