CN111062742A - Information recommendation method and device, storage medium and server - Google Patents
Information recommendation method and device, storage medium and server Download PDFInfo
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- CN111062742A CN111062742A CN201911168471.2A CN201911168471A CN111062742A CN 111062742 A CN111062742 A CN 111062742A CN 201911168471 A CN201911168471 A CN 201911168471A CN 111062742 A CN111062742 A CN 111062742A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0224—Discounts or incentives, e.g. coupons or rebates based on user history
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
The application discloses an information recommendation method, an information recommendation device, a storage medium and a server, and belongs to the technical field of Internet of vehicles. The method comprises the following steps: receiving a travel navigation request sent by a vehicle, wherein the travel navigation request comprises a destination; determining at least one key passing point on a driving route of a vehicle driving to a destination; acquiring environment data of each key passing point; determining a target insurance type matched with the driving route according to the acquired environment data; policy data corresponding to the target insurance category is recommended to the vehicle. According to the method and the device, insurance type recommendation is performed on the user according to the environmental data in the travel to be carried out, namely, the insurance type recommendation is performed on the user in a personalized mode according to the environmental data in the travel under the vehicle-mounted environment, the user can be helped to generate a more adaptive car insurance policy, the user expectation is better met, the driving safety of the user is more comprehensively guaranteed, the applicability is strong, the intelligence is realized, and the generation mode of the policy is enriched.
Description
Technical Field
The application relates to the technical field of vehicle networking, in particular to an information recommendation method, an information recommendation device, a storage medium and a server.
Background
With the increasing living standard, the motor vehicle has become one of the important tools for people to go out daily. Accordingly, the insurance for motor vehicles (hereinafter referred to as vehicle insurance) has been widely developed, i.e., vehicle insurance has gradually developed into an important commercial insurance.
Currently, the industry generally adopts the current vehicle condition as an element to generate a corresponding vehicle insurance policy. Among these, vehicle conditions include, but are not limited to: the body parts, the appearance, the interior decoration, the performance and the like.
The vehicle insurance policy is solidified in a generating mode and has a single mode. In order to guarantee driving safety of the user more comprehensively and consider continuous improvement of personalized requirements of the user, how to recommend information to the user in a vehicle-mounted environment to generate a more adaptive vehicle insurance policy becomes a problem to be solved by technical personnel in the field at present.
Disclosure of Invention
The embodiment of the application provides an information recommendation method, an information recommendation device, a storage medium and a server, which realize the personalized insurance type recommendation for a user according to environment data in a journey in a vehicle-mounted environment, can help the user to generate a more adaptive vehicle insurance policy, better meets the user expectation, more comprehensively guarantees the driving safety of the user, and is high in applicability and intelligent. The technical scheme is as follows:
in one aspect, an information recommendation method is provided, and the method includes:
receiving a travel navigation request sent by a vehicle, wherein the travel navigation request comprises a destination;
determining at least one key via point on a driving route of the vehicle to the destination;
acquiring environment data of each key passing point;
determining a target insurance type matched with the driving route according to the acquired environment data;
recommending policy data corresponding to the target insurance category to the vehicle.
In a possible implementation manner, the determining, according to the acquired environment data, a target insurance category matched with the driving route includes:
matching insurance influence factors in the first list according to the environment data of each key passing point, and inquiring insurance types and weights corresponding to the matched insurance influence factors in the second list;
determining the insurance weight value of each insurance category according to the matched insurance influence factors and the corresponding insurance categories and weights;
determining a target insurance type matched with the driving route according to the obtained insurance application weight value;
wherein the first list gives the correspondence between the environment and the application influencing factors, and the second list gives the correspondence between the insurance type, the application influencing factors and the weight.
In a possible implementation manner, the determining an insurance weight value of each insurance category according to the matched insurance influencing factors and the corresponding insurance categories and weights includes:
and accumulating the weights of the insurance influence factors belonging to the same insurance type in the matched insurance influence factors to obtain the insurance weight values of each insurance type.
In one possible implementation manner, the determining a target insurance category matching the driving route according to the obtained insurance weight value includes:
and sequencing all insurance types according to the sequence of the insurance weight values from large to small, and determining the front preset number of insurance types as target insurance types matched with the driving route.
In one possible implementation, the determining at least one key via point on the driving route of the vehicle to the destination includes:
determining a plurality of trip pass points on a driving route of the vehicle to the destination;
converting the longitude and latitude data of the route points of the plurality of routes to obtain a plurality of geographical partitions;
and selecting a route point of the trip from each geographical partition to obtain the at least one key route point.
In one possible implementation, after receiving a travel guidance request sent by a vehicle, the method further includes:
if the fact that an abnormal driving environment exists on a driving route of the vehicle to the destination is determined, the information recommendation process is executed; or the like, or, alternatively,
and if the length of the driving route of the vehicle towards the destination is determined to exceed the distance threshold, executing the information recommendation process.
In another aspect, an information recommendation apparatus is provided, the apparatus including:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is configured to receive a travel navigation request sent by a vehicle, and the travel navigation request comprises a destination;
a first determination module configured to determine at least one key via point on a driving route of the vehicle toward the destination;
the acquisition module is configured to acquire environmental data at each key passing point;
the second determination module is configured to determine a target insurance category matched with the driving route according to the acquired environment data;
a recommendation module configured to recommend policy data corresponding to the target insurance category to the vehicle.
In a possible implementation manner, the second determining module is further configured to match the insurance application influencing factors in the first list according to the environment data at each key passing point, and query insurance types and weights corresponding to the matched insurance application influencing factors in the second list; determining the insurance weight value of each insurance category according to the matched insurance influence factors and the corresponding insurance categories and weights; determining a target insurance type matched with the driving route according to the obtained insurance application weight value; wherein the first list gives the correspondence between the environment and the application influencing factors, and the second list gives the correspondence between the insurance type, the application influencing factors and the weight.
In a possible implementation manner, the second determining module is further configured to accumulate weights of the insurance influencing factors belonging to the same insurance category in the matched insurance influencing factors to obtain an insurance weight value of each insurance category;
in a possible implementation manner, the second determining module is further configured to perform sorting processing on the insurance categories according to a descending order of the insurance weight values, and determine a preset number of insurance categories as target insurance categories matched with the driving route.
In one possible implementation, the first determining module is further configured to determine a plurality of trip route points on a driving route of the vehicle to the destination; converting the longitude and latitude data of the route points of the plurality of routes to obtain a plurality of geographical partitions; and selecting a route point of the trip from each geographical partition to obtain the at least one key route point.
In a possible implementation manner, the device is further configured to execute the information recommendation process if it is determined that an abnormal driving environment exists on a driving route of the vehicle to the destination after receiving a travel navigation request sent by the vehicle; or if the length of the driving route of the vehicle towards the destination is determined to exceed the distance threshold, executing the information recommendation process.
In another aspect, a storage medium is provided, where at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement the information recommendation method.
In another aspect, a server is provided, which includes a processor and a memory, where the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the information recommendation method described above.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
after receiving a travel navigation request with a destination sent by a vehicle, the embodiment of the application determines at least one key passing point on a driving route of the vehicle to the destination, further obtains environment data of each key passing point, determines a target insurance type matched with the driving route according to the obtained environment data, and then recommends policy data corresponding to the target insurance type to the vehicle. Based on the above description, the embodiment of the application realizes insurance type recommendation for the user according to the environmental data in the travel to be carried out, namely realizes the personalized insurance type recommendation for the user according to the environmental data in the travel in the vehicle-mounted environment, can help the user generate a more adaptive vehicle insurance policy, better meets the user expectation, more comprehensively guarantees the driving safety of the user, has strong applicability and is intelligent, and enriches the generation mode of the vehicle insurance policy.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an implementation environment related to an information recommendation method provided in an embodiment of the present application;
fig. 2 is a flowchart of an information recommendation method provided in an embodiment of the present application;
fig. 3 is a flowchart of an information recommendation method provided in an embodiment of the present application;
fig. 4 is a schematic diagram of an overall execution flow of an information recommendation method provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of an information recommendation device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Before explaining embodiments of the present application in detail, some noun terms or abbreviations that may be referred to by the embodiments of the present application are explained.
Environmental data: in the embodiment of the present application, the environmental data includes, but is not limited to, road condition data and weather data. In one possible implementation, the data for the road condition includes, but is not limited to: road congestion conditions, transit time, traffic accidents, ponding road segments, electronic eyes and the like. For weather data, including but not limited to: season, temperature, humidity, wind, precipitation, etc.
The following describes an implementation environment related to a message pushing method provided by an embodiment of the present application.
Referring to fig. 1, the implementation environment includes a vehicle 101 and a server 102. The server 102 may also be referred to herein as a vehicle networking platform or a backend server or a cloud server.
For example, the server 102 and the vehicle 101 may communicate with each other through a mobile network, or may communicate with each other through information relay of a road side unit, which is not specifically limited in this embodiment of the present application.
Among these, mobile networks include, but are not limited to: third generation mobile communication technology (3G), fourth generation mobile communication technology (4G), and fourth generation mobile communication technology (5G), and so on.
The information recommendation method provided by the embodiment of the application relates to vehicle insurance personalized recommendation for a vehicle-mounted user, namely, vehicle insurance matching is carried out for the user according to environment data in a trip to be carried out by the vehicle, and personalized insurance recommendation is carried out by taking environmental factors in the trip to be carried out as insurance matching elements, so that the user can be helped to generate a more adaptive vehicle insurance policy, the generated insurance is more in line with the expectation of the user, and the driving safety of the user can be more comprehensively guaranteed.
The information recommendation method provided in the embodiments of the present application is explained in detail below.
Fig. 2 is a flowchart of an information recommendation method according to an embodiment of the present application. Referring to fig. 2, a method flow provided by the embodiment of the present application includes:
201. and receiving a travel navigation request sent by the vehicle, wherein the travel navigation request comprises a destination.
202. At least one key waypoint on a driving route of the vehicle toward the destination is determined.
203. And acquiring environment data at each key passing point.
204. And determining a target insurance type matched with the driving route according to the acquired environment data.
205. Policy data corresponding to the target insurance category is recommended to the vehicle.
According to the method provided by the embodiment of the application, after a travel navigation request which is sent by a vehicle and carries a destination is received, at least one key passing point on a driving route of the vehicle to the destination is determined, so that the environment data of each key passing point is obtained, a target insurance type matched with the driving route is determined according to the obtained environment data, and then policy data corresponding to the target insurance type is recommended to the vehicle. Based on the above description, the embodiment of the application realizes insurance type recommendation for the user according to the environmental data in the travel to be carried out, namely realizes the personalized insurance type recommendation for the user according to the environmental data in the travel in the vehicle-mounted environment, can help the user generate a more adaptive vehicle insurance policy, better meets the user expectation, more comprehensively guarantees the driving safety of the user, has strong applicability and is intelligent, and enriches the generation mode of the vehicle insurance policy.
In a possible implementation manner, the determining, according to the acquired environment data, a target insurance category matched with the driving route includes:
matching insurance influence factors in the first list according to the environment data of each key passing point, and inquiring insurance types and weights corresponding to the matched insurance influence factors in the second list;
determining the insurance weight value of each insurance category according to the matched insurance influence factors and the corresponding insurance categories and weights;
determining a target insurance type matched with the driving route according to the obtained insurance application weight value;
wherein the first list gives the correspondence between the environment and the application influencing factors, and the second list gives the correspondence between the insurance type, the application influencing factors and the weight.
In a possible implementation manner, the determining an insurance weight value of each insurance category according to the matched insurance influencing factors and the corresponding insurance categories and weights includes:
and accumulating the weights of the insurance influence factors belonging to the same insurance type in the matched insurance influence factors to obtain the insurance weight values of each insurance type.
In one possible implementation manner, the determining a target insurance category matching the driving route according to the obtained insurance weight value includes:
and sequencing all insurance types according to the sequence of the insurance weight values from large to small, and determining the front preset number of insurance types as target insurance types matched with the driving route.
In one possible implementation, the determining at least one key via point on the driving route of the vehicle to the destination includes:
determining a plurality of trip pass points on a driving route of the vehicle to the destination;
converting the longitude and latitude data of the route points of the plurality of routes to obtain a plurality of geographical partitions;
and selecting a route point of the trip from each geographical partition to obtain the at least one key route point.
In one possible implementation, after receiving a travel guidance request sent by a vehicle, the method further includes:
if the fact that an abnormal driving environment exists on a driving route of the vehicle to the destination is determined, the information recommendation process is executed; or the like, or, alternatively,
and if the length of the driving route of the vehicle towards the destination is determined to exceed the distance threshold, executing the information recommendation process.
All the above optional technical solutions may be combined arbitrarily to form the optional embodiments of the present disclosure, and are not described herein again.
Fig. 3 is a flowchart of an information recommendation method according to an embodiment of the present application. The interaction subject of the method is a vehicle and a cloud server. Referring to fig. 3, a method flow provided by the embodiment of the present application includes:
301. the vehicle sends a journey navigation request to the cloud server, wherein the journey navigation request comprises a destination.
In the embodiment of the application, after a user triggers a navigation operation with a destination on the vehicle side, the vehicle can initiate a journey navigation request to the cloud server.
For example, the information recommendation method provided in the embodiment of the present application may be executed once after the vehicle initiates a trip navigation request each time, or may be executed under a limited condition, which is not specifically limited in the embodiment of the present application. In one possible implementation, the constraints include, but are not limited to, the following two:
first, after receiving a route guidance request transmitted from a vehicle, if it is determined that an abnormal driving environment exists on a driving route of the vehicle to a destination, the following information recommendation process is performed.
As an example, the abnormal driving environment may be bad weather or a bad road condition on the driving route. That is, when bad weather or bad road conditions occur, insurance can be recommended to the user in time.
Second, after receiving a route guidance request transmitted from the vehicle, if it is determined that the length of the travel route of the vehicle to the destination exceeds the distance threshold, the following information recommendation process is performed.
In this case, when the user travels a long distance in the future, insurance can be recommended to the user in a timely manner in consideration of driving safety on the travel route. For example, the distance threshold may be a value of 500 km or 1000 km, which is not specifically limited in the embodiment of the present application.
302. The cloud server determines a plurality of travel passing points on a travel route of the vehicle to the destination.
In one possible implementation, the trip waypoints on the travel route include, but are not limited to: administrative areas such as scenic spots, landmark buildings, landmark roads, urban districts, counties, towns and the like are not specifically limited in this embodiment of the present application. In addition, the cloud server can store the driving passing points after obtaining the driving passing points on the driving route.
303. The cloud server converts the longitude and latitude data of the route points of the multiple routes to obtain multiple geographical partitions; and selecting a travel route point in each geographical partition to obtain at least one key route point.
In the embodiment of the application, in order to reduce the calculation amount and improve the recommendation speed, the cloud server screens key passing points from all the obtained passing points of the journey. As an example, the cloud server may convert the longitude and latitude data of the route point of the trip by using a GeoHash algorithm, so as to obtain a plurality of geographical partitions; and selecting a route point of a trip in each geographical partition to obtain a key route point on the driving route.
The GeoHash algorithm converts the longitude and latitude data, and may encode the two-dimensional longitude and latitude data into a character string to convert the two-dimensional data into one-dimensional data, that is, the GeoHash algorithm is an algorithm for partitioning geographical locations.
304. And the cloud server acquires the environment data of each key passing point.
In the embodiment of the application, in order to accurately recommend insurance to a user, the cloud server can acquire the environment data of each key passing point. The cloud server can pull the environment data from the third-party environment service mechanism through the environment data interface, and the embodiment of the application is not specifically limited to this.
Exemplary environmental data acquired at each key via point includes, but is not limited to: road condition data and weather data. In one possible implementation, the data for the road condition includes, but is not limited to: road congestion conditions, transit time, traffic accidents, ponding road sections, electronic eyes and the like, which are not specifically limited in the embodiment of the present application. For weather data, including but not limited to: season, temperature, humidity, wind power, precipitation, etc., which are not specifically limited in the embodiments of the present application.
In addition, in order to realize the personalized insurance recommendation, the cloud server can also obtain insurance-related resources from a third-party insurance company, namely, the third-party insurance company needs to provide insurance materials for realizing the embodiment of the application. In the embodiment of the present application, the cloud server may generate an association table between the insurance category, the application influence factor, and the weight, which is referred to as a second list herein, by using a text source such as a TF-IDF (term Frequency-Inverse text Frequency index) algorithm in combination with the insurance category, the insurance category description, and the offer case. Wherein the second list gives the correspondence between insurance type, insurable impact factor and weight. Illustratively, one possible form of the second list is given in table 1 below.
TABLE 1
| Kind of insurance | Influence factor of application | Weight of |
| Wading danger | Light rain-heavy rain | 0.2-0.7 |
| Wading danger | Summer season | 0.3 |
| Wading danger | Accumulated water | 0.2 |
| Risk of spontaneous combustion | Summer season | 0.3 |
| Risk of spontaneous combustion | Air temperature is 30-50 | 0.4-0.7 |
| Risk of spontaneous combustion | Relative humidity 20 | 0.4 |
| …… | …… | …… |
The first point to be noted is that table 1 above is only an example given for the second list. In practical applications, the second list may include more insurance types, insurance influence factors, and weights, which are not specifically limited in the embodiment of the present application.
In addition to the second list, the embodiment of the present application further provides an association table between the environment and the application influencing factors, which is referred to as the first list herein. Wherein the first list gives the correspondence between the environment and the insurable influencing factors. Illustratively, one possible form of the first list is given in table 2 below.
TABLE 2
| Environment(s) | Influence factor of application |
| Season | Summer season |
| Temperature of | Air temperature is 30-50 |
| Precipitation | Light rain-heavy rain |
| Road conditions | Ponding highway section |
| Humidity | Relative humidity 20 |
| …… | …… |
A second point of the description is that table 2 above is only an example given for the first list. In practical applications, the first list may include more environmental information and application influencing factors, which are not specifically limited in this application.
305. And the cloud server determines a target insurance type matched with the driving route according to the environment data of each key passing point.
In one possible implementation, the target insurance category matched with the driving route is determined according to the acquired environment data, including but not limited to:
3051. and matching the insurance influence factors in the first list according to the environment data of each key passing point, and inquiring the insurance type and the weight corresponding to the matched insurance influence factors in the second list.
Suppose that there are 3 key route points on the driving route, which are respectively a key route point a, a key route point B, and a key route point C, wherein the environment data at the key route point a is rainstorm, the environment data at the key route point B is air temperature 33 degrees, and the environment data at the key route point C is air temperature 36 degrees.
Then, an insurance influence factor of 'light rain-heavy rain' can be matched in the first list aiming at the key passing point a, the insurance influence factor exists in the second list, and the corresponding insurance type is 'water risk', and the weight is 0.2-0.7. The insurance influence factor of 'air temperature 30-50' can be matched in the first list aiming at the key passing point B, the insurance influence factor exists in the second list, the corresponding insurance type is 'self-ignition insurance', and the weight is 0.4-0.7. The insurance contribution factor "air temperature 30-50" is also matched in the first list for the key waypoint C, the corresponding insurance category and weight having been given above.
3052. And determining the insurance weight value of each insurance category according to the matched insurance influence factors and the corresponding insurance categories and weights.
In one possible implementation manner, the insurance weight value of each insurance category is determined according to the matched insurance influencing factors and the corresponding insurance categories and weights, including but not limited to: and accumulating the weights of the insurance influence factors belonging to the same insurance type in the matched insurance influence factors to obtain the insurance weight values of each insurance type.
Continuing to take the above example as an example, the insurance influencing factors matched according to the environment data at each key point are respectively light rain-rainstorm, air temperature 30-50 and air temperature 30-50, wherein the insurance type corresponding to the light rain-rainstorm insurance influencing factor is wading risk, and the weight is 0.7; the air temperature 30-50, which is the insurance influence factor, appears twice and respectively corresponds to the environments at the key passing point B and the key passing point C, so that corresponding weights can be selected to be accumulated, and taking the weight value corresponding to the air temperature 30-50, which is the insurance influence factor, as 0.4 as an example, the insurance weight value of the self-ignition risk is 0.4+0.4 to 0.8. That is, in this example, the insurance policy weight value for the water risk is 0.7 and the insurance policy weight value for the self-ignition risk is 0.8.
It should be noted that the insurance category may include other insurance categories such as glass insurance besides the above-mentioned water insurance and self-ignition insurance, and the embodiments of the present application are only exemplified by these two insurance categories.
In addition, if a certain dangerous seed cannot be matched with the environmental data in the driving route, the insurance weight value of the dangerous seed is 0.
3053. Determining the target insurance type matched with the driving route according to the obtained insurable weight value
In one possible implementation, the target insurance category matched with the driving route is determined according to the obtained insurance weight value, including but not limited to: sorting each insurance type according to the descending order of the insurance weight values to obtain an insurance list; and determining the front preset number of insurance types as target insurance types matched with the driving route.
For example, the preset number may take a value of 1, that is, one insurance category ranked at the top is determined as the insurance most suitable for the travel route to be traveled. The preset number may also be greater than 1, that is, multiple suitable insurance categories may also be recommended to the user, which is not specifically limited in this embodiment of the present application.
306. And the cloud server recommends policy data corresponding to the target insurance type to the vehicle.
In this embodiment of the present application, the cloud server may push policy data corresponding to the target insurance category to the vehicle in the form of a service message, which is not specifically limited in this embodiment of the present application.
307. And the vehicle draws an information recommendation page according to the received policy data and displays the drawn information recommendation page.
After receiving the policy data pushed by the cloud server, the vehicle machine side can draw an information recommendation page according to the received policy data and display the drawn information recommendation page to the user for the user to browse. In a possible implementation manner, the information recommendation page may be in the form of a service card, which is not specifically limited in this embodiment of the application.
308. The vehicle acquires policy confirmation operation triggered by a user and sends a confirmation notification message to the cloud server, wherein the confirmation notification message is used for indicating the cloud server to set the corresponding policy to be in an effective state.
In the embodiment of the application, if the user at the vehicle side confirms the policy, the vehicle sends a notification message to the cloud server, and the cloud server confirms that the policy is in effect after receiving the notification message, so that the recommendation process of one round is finished. Note that the warranty's validity period is at least greater than the duration of the journey.
According to the method provided by the embodiment of the application, after a travel navigation request which is sent by a vehicle and carries a destination is received, at least one key passing point on a driving route of the vehicle to the destination is determined, so that the environment data of each key passing point is obtained, a target insurance type matched with the driving route is determined according to the obtained environment data, and then policy data corresponding to the target insurance type is recommended to the vehicle. Based on the above description, the embodiment of the application realizes insurance type recommendation for the user according to the environmental data in the travel to be carried out, namely realizes the personalized insurance type recommendation for the user according to the environmental data in the travel in the vehicle-mounted environment, can help the user generate a more adaptive vehicle insurance policy, better meets the user expectation, more comprehensively guarantees the driving safety of the user, has strong applicability and is intelligent, and enriches the generation mode of the vehicle insurance policy.
Fig. 4 is a schematic diagram of an overall execution flow of an information recommendation method according to an embodiment of the present application. Referring to fig. 4, the overall execution flow includes:
a. and the vehicle sends a journey navigation request to the cloud server.
b. And the cloud server acquires the destination of the travel navigation request and plans a driving route.
c. And the cloud server determines a travel passing point on the driving route.
d. And the cloud server determines key passing points on the driving route from the travel passing points.
e. And the cloud server acquires the environment data of the key passing point.
The environment data includes, but is not limited to, weather data and road condition data, which is not specifically limited in this embodiment of the present application.
f. The cloud server obtains insurance materials from a third-party insurance company.
g. And the cloud server generates a list A among insurance types, insurance influence factors and weights by combining text sources such as insurance types, insurance type descriptions and insurance cases included in the insurance materials.
h. The cloud server generates a list B between the environment and the application influencing factors.
i. And the cloud server determines the insurance weight value of each dangerous seed according to the environment data at the key passing point, the list A and the list B.
j. And the cloud server sorts the dangerous varieties according to the obtained insurance application weight values to obtain a sorted insurance list.
k. And the cloud server recommends insurance policy data of the dangerous species which are most suitable for the travel route in the insurance list to the vehicle.
And l, drawing an information recommendation page by the vehicle according to the received policy data, and displaying the drawn information recommendation page.
And m, the vehicle acquires policy confirmation operation triggered by the user and sends a confirmation notification message to the cloud server.
And n, the cloud server sets the corresponding policy to be in an effective state.
According to the method provided by the embodiment of the application, after a travel navigation request which is sent by a vehicle and carries a destination is received, at least one key passing point on a driving route of the vehicle to the destination is determined, so that the environment data of each key passing point is obtained, a target insurance type matched with the driving route is determined according to the obtained environment data, and then policy data corresponding to the target insurance type is recommended to the vehicle. Based on the above description, the embodiment of the application realizes insurance type recommendation for the user according to the environmental data in the travel to be carried out, namely realizes the personalized insurance type recommendation for the user according to the environmental data in the travel in the vehicle-mounted environment, can help the user generate a more adaptive vehicle insurance policy, better meets the user expectation, more comprehensively guarantees the driving safety of the user, has strong applicability and is intelligent, and enriches the generation mode of the vehicle insurance policy.
Fig. 5 is a schematic structural diagram of an information recommendation device according to an embodiment of the present application. Referring to fig. 5, the apparatus includes:
the system comprises a receiving module 501, configured to receive a travel navigation request sent by a vehicle, wherein the travel navigation request comprises a destination;
a first determination module 502 configured to determine at least one key via point on a driving route of the vehicle to the destination;
an obtaining module 503 configured to obtain environment data at each of the key passing points;
a second determination module 504 configured to determine a target insurance category matching the travel route according to the acquired environment data;
a recommendation module 505 configured to recommend policy data corresponding to the target insurance category to the vehicle.
According to the device provided by the embodiment of the application, after a travel navigation request which is sent by a vehicle and carries a destination is received, at least one key passing point on a driving route of the vehicle to the destination is determined, so that the environment data of each key passing point is obtained, a target insurance type matched with the driving route is determined according to the obtained environment data, and then policy data corresponding to the target insurance type is recommended to the vehicle. Based on the above description, the embodiment of the application realizes insurance type recommendation for the user according to the environmental data in the travel to be carried out, namely realizes the personalized insurance type recommendation for the user according to the environmental data in the travel in the vehicle-mounted environment, can help the user generate a more adaptive vehicle insurance policy, better meets the user expectation, more comprehensively guarantees the driving safety of the user, has strong applicability and is intelligent, and enriches the generation mode of the vehicle insurance policy.
In a possible implementation manner, the second determining module 504 is further configured to match the insurance application influencing factors in the first list according to the environment data at each key passing point, and query the insurance type and the weight corresponding to the matched insurance application influencing factors in the second list; determining the insurance weight value of each insurance category according to the matched insurance influence factors and the corresponding insurance categories and weights; determining a target insurance type matched with the driving route according to the obtained insurance application weight value; wherein the first list gives the correspondence between the environment and the application influencing factors, and the second list gives the correspondence between the insurance type, the application influencing factors and the weight.
In a possible implementation manner, the second determining module 504 is further configured to accumulate the weights of the insurance influencing factors belonging to the same insurance category in the matched insurance influencing factors to obtain the insurance weight values of the insurance categories;
in a possible implementation manner, the second determining module 504 is further configured to perform sorting processing on the insurance categories according to a descending order of the insurance weight values, and determine a preset number of insurance categories as target insurance categories matched with the driving route.
In one possible implementation, the first determining module 502 is further configured to determine a plurality of trip route points on a driving route of the vehicle to the destination; converting the longitude and latitude data of the route points of the plurality of routes to obtain a plurality of geographical partitions; and selecting a route point of the trip from each geographical partition to obtain the at least one key route point.
In a possible implementation manner, the device is further configured to execute the information recommendation process if it is determined that an abnormal driving environment exists on a driving route of the vehicle to the destination after receiving a travel navigation request sent by the vehicle; or if the length of the driving route of the vehicle towards the destination is determined to exceed the distance threshold, executing the information recommendation process.
All the above optional technical solutions may be combined arbitrarily to form the optional embodiments of the present disclosure, and are not described herein again.
It should be noted that: in the information recommendation apparatus provided in the above embodiment, only the division of the above functional modules is illustrated when performing information recommendation, and in practical applications, the above function allocation may be completed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to complete all or part of the above described functions. In addition, the information recommendation device and the information recommendation method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
Fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application, where the server 600 may generate a relatively large difference due to a difference in configuration or performance, and may include one or more processors (CPUs) 601 and one or more memories 602, where at least one instruction is stored in the memory 602, and the at least one instruction is loaded and executed by the processor 601 to implement the information recommendation method provided by each method embodiment. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input/output, and the server may also include other components for implementing the functions of the device, which are not described herein again.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, including instructions executable by a processor in a terminal to perform the information recommendation method in the above embodiments is also provided. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (14)
1. An information recommendation method, characterized in that the method comprises:
receiving a travel navigation request sent by a vehicle, wherein the travel navigation request comprises a destination;
determining at least one key via point on a driving route of the vehicle to the destination;
acquiring environment data of each key passing point;
determining a target insurance type matched with the driving route according to the acquired environment data;
recommending policy data corresponding to the target insurance category to the vehicle.
2. The method according to claim 1, wherein the determining a target insurance category matching the travel route from the acquired environmental data comprises:
matching insurance influence factors in the first list according to the environment data of each key passing point, and inquiring insurance types and weights corresponding to the matched insurance influence factors in the second list;
determining the insurance weight value of each insurance category according to the matched insurance influence factors and the corresponding insurance categories and weights;
determining a target insurance type matched with the driving route according to the obtained insurance application weight value;
wherein the first list gives the correspondence between the environment and the application influencing factors, and the second list gives the correspondence between the insurance type, the application influencing factors and the weight.
3. The method of claim 2, wherein determining an insurance weight value for each insurance category based on the matched insurance impact factors and the corresponding insurance categories and weights comprises:
and accumulating the weights of the insurance influence factors belonging to the same insurance type in the matched insurance influence factors to obtain the insurance weight values of each insurance type.
4. The method of claim 2, wherein determining a target insurance category matching the travel route based on the derived underwriting weight value comprises:
and sequencing all insurance types according to the sequence of the insurance weight values from large to small, and determining the front preset number of insurance types as target insurance types matched with the driving route.
5. The method of claim 1, wherein the determining at least one key waypoint on a route traveled by the vehicle toward the destination comprises:
determining a plurality of trip pass points on a driving route of the vehicle to the destination;
converting the longitude and latitude data of the route points of the plurality of routes to obtain a plurality of geographical partitions;
and selecting a route point of the trip from each geographical partition to obtain the at least one key route point.
6. The method according to any one of claims 1 to 5, wherein upon receiving a trip navigation request sent by a vehicle, the method further comprises:
if the fact that an abnormal driving environment exists on a driving route of the vehicle to the destination is determined, the information recommendation process is executed; or the like, or, alternatively,
and if the length of the driving route of the vehicle towards the destination is determined to exceed the distance threshold, executing the information recommendation process.
7. An information recommendation apparatus, characterized in that the apparatus comprises:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is configured to receive a travel navigation request sent by a vehicle, and the travel navigation request comprises a destination;
a first determination module configured to determine at least one key via point on a driving route of the vehicle toward the destination;
the acquisition module is configured to acquire environmental data at each key passing point;
the second determination module is configured to determine a target insurance category matched with the driving route according to the acquired environment data;
a recommendation module configured to recommend policy data corresponding to the target insurance category to the vehicle.
8. The apparatus according to claim 7, wherein the second determining module is further configured to match the insurance influencing factors in the first list according to the environment data at each key passing point, and query the insurance category and the weight corresponding to the matched insurance influencing factors in the second list; determining the insurance weight value of each insurance category according to the matched insurance influence factors and the corresponding insurance categories and weights; determining a target insurance type matched with the driving route according to the obtained insurance application weight value; wherein the first list gives the correspondence between the environment and the application influencing factors, and the second list gives the correspondence between the insurance type, the application influencing factors and the weight.
9. The apparatus of claim 8, wherein the second determining module is further configured to accumulate the weights of the insurance influencing factors belonging to the same insurance category in the matched insurance influencing factors to obtain the insurance weight value of each insurance category.
10. The apparatus according to claim 8, wherein the second determining module is further configured to perform a sorting process on the insurance categories in an order from a large insurance weight value to a small insurance weight value, and determine a preset number of insurance categories as the target insurance categories matching the driving route.
11. The apparatus of claim 7, wherein the first determining module is further configured to determine a plurality of trip waypoints on a travel route of the vehicle toward the destination; converting the longitude and latitude data of the route points of the plurality of routes to obtain a plurality of geographical partitions; and selecting a route point of the trip from each geographical partition to obtain the at least one key route point.
12. The apparatus according to any one of claims 7 to 11, wherein the apparatus is further configured to perform the information recommendation process if it is determined that an abnormal driving environment exists on a driving route of the vehicle to the destination after receiving a route guidance request transmitted by the vehicle; or if the length of the driving route of the vehicle towards the destination is determined to exceed the distance threshold, executing the information recommendation process.
13. A storage medium having stored therein at least one instruction, which is loaded and executed by a processor to implement the information recommendation method according to any one of claims 1 to 6.
14. A server, characterized in that the server comprises a processor and a memory, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to realize the information recommendation method according to any one of claims 1 to 6.
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