CN111062742B - Information recommendation method, device, storage medium and server - Google Patents
Information recommendation method, device, storage medium and server Download PDFInfo
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- CN111062742B CN111062742B CN201911168471.2A CN201911168471A CN111062742B CN 111062742 B CN111062742 B CN 111062742B CN 201911168471 A CN201911168471 A CN 201911168471A CN 111062742 B CN111062742 B CN 111062742B
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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 journey navigation request sent by a vehicle, wherein the journey navigation request comprises a destination; determining at least one key passing point on a driving route of the vehicle to the destination; acquiring environment data at each key passing point; determining a target insurance type matched with the driving route according to the acquired environmental data; policy data corresponding to the target insurance category is recommended to the vehicle. According to the method and the device for recommending the insurance types for the users, the insurance types are recommended for the users according to the environmental data in the journey to be travelled, namely, the insurance types are recommended for the users in a personalized manner according to the environmental data in the journey in the vehicle-mounted environment, the users can be helped to generate more adaptive insurance policy, the user expectations are met more, the driving safety of the users is guaranteed more comprehensively, the applicability is strong and the intelligence is realized, and the generation mode of the policy is enriched.
Description
Technical Field
The application relates to the technical field of internet of vehicles, in particular to an information recommendation method, an information recommendation device, a storage medium and a server.
Background
With the increasing level of living, motor vehicles have become one of the important tools for people to travel daily. With this, motor vehicle insurance (abbreviated as car insurance) has also been widely developed, that is, car insurance has gradually been developed as an important commercial insurance.
The current vehicle condition of the vehicle is commonly adopted by the industry to generate corresponding insurance policy. Among them, vehicle conditions include, but are not limited to: several aspects of body parts, appearance, interior trim, and performance.
The generation mode of the vehicle insurance policy is solidified and has single mode. In order to more comprehensively ensure the driving safety of the user and consider the continuous improvement of the personalized demands of the user, how to recommend information to the user in a vehicle-mounted environment so as to generate a more adaptive vehicle insurance policy becomes a problem to be solved urgently by the person skilled in the art.
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 personalized insurance type recommendation for a user according to environmental data in a journey in a vehicle-mounted environment, can help the user to generate a more adaptive insurance policy, and is more in line with user expectations and more comprehensively guarantees the driving safety of the user, and has strong applicability and intellectualization. The technical scheme is as follows:
in one aspect, there is provided an information recommendation method, the method including:
receiving a journey navigation request sent by a vehicle, wherein the journey navigation request comprises a destination;
determining at least one key passing point on a travel route of the vehicle toward the destination;
acquiring environment data at each key passing point;
determining a target insurance category matched with the driving route according to the acquired environmental data;
and recommending policy data corresponding to the target insurance category to the vehicle.
In one possible implementation manner, the determining, according to the acquired environmental data, the target insurance category matched with the driving route includes:
according to the environment data at each key passing point, matching the insuring influence factors in a first list, and inquiring insurance types and weights corresponding to the matched insuring influence factors in a second list;
determining the application weight value of each insurance type according to the matched application influence factors and the corresponding insurance types and weights;
determining a target insurance type matched with the driving route according to the obtained application weight value;
the first list gives the corresponding relation between the environment and the insuring influence factors, and the second list gives the corresponding relation among insurance types, insuring influence factors and weights.
In one possible implementation manner, the determining the application weight value of each insurance category according to the matched application influence factor and the corresponding insurance category and weight includes:
and accumulating the weights of the insurance influence factors belonging to the same insurance category in the matched insurance influence factors to obtain the insurance weight value of each insurance category.
In one possible implementation manner, the determining, according to the obtained application weight value, the target insurance category matched with the driving route includes:
and sequencing the insurance categories according to the order of the applied weight values from large to small, and determining the preset number of insurance categories as target insurance categories matched with the driving route.
In one possible implementation manner, the determining at least one key passing point on a driving route of the vehicle to the destination includes:
determining a plurality of trip points on a travel route of the vehicle toward the destination;
converting the longitude and latitude data of the travel passing points to obtain a plurality of geographic partitions;
and selecting a journey passing point from each geographical zone to obtain the at least one key passing point.
In one possible implementation, after receiving the route navigation request sent by the vehicle, the method further includes:
if the abnormal running environment exists on the running route of the vehicle to the destination, executing the information recommending process; or alternatively, the first and second heat exchangers may be,
and if the running route length of the vehicle to the destination exceeds the distance threshold value, executing the information recommendation process.
In another aspect, there is provided an information recommendation apparatus, the apparatus including:
the receiving module is configured to receive a journey navigation request sent by a vehicle, wherein the journey navigation request comprises a destination;
a first determination module configured to determine at least one key waypoint on a travel route of the vehicle toward the destination;
an acquisition module configured to acquire environmental data at each of the key route points;
a second determining module configured to determine a target insurance category matching the travel route according to the acquired environmental data;
and a recommending 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 insuring influence factors in a first list according to the environmental data at each key passing point, and query the insurance types and weights corresponding to the matched insuring influence factors in a second list; determining the application weight value of each insurance type according to the matched application influence factors and the corresponding insurance types and weights; determining a target insurance type matched with the driving route according to the obtained application weight value; the first list gives the corresponding relation between the environment and the insuring influence factors, and the second list gives the corresponding relation among insurance types, insuring influence factors and weights.
In one possible implementation manner, the second determining module is further configured to accumulate weights of the insurance influence factors belonging to the same insurance category in the matched insurance influence factors to obtain an insurance weight value of each insurance category;
in one possible implementation manner, the second determining module is further configured to sort the insurance categories according to the order of the applied weight values from the high to the low, and determine the preset number of insurance categories as the target insurance categories matched with the driving route.
In one possible implementation, the first determining module is further configured to determine a plurality of travel route points on a travel route of the vehicle toward the destination; converting the longitude and latitude data of the travel passing points to obtain a plurality of geographic partitions; and selecting a journey passing point from each geographical zone to obtain the at least one key passing point.
In one possible implementation manner, the device is further configured to execute the information recommendation process after receiving a travel navigation request sent by a vehicle, if it is determined that an abnormal running environment exists on a running route of the vehicle to the destination; or if it is determined that the length of the travel route of the vehicle toward the destination exceeds a distance threshold, executing the above-described information recommendation process.
In another aspect, a storage medium having at least one instruction stored therein is provided, the at least one instruction being loaded and executed by a processor to implement the above-described information recommendation method.
In another aspect, a server is provided, the server including a processor and a memory, the memory storing at least one instruction, the at least one instruction loaded and executed by the processor to implement the information recommendation method described above.
The beneficial effects that technical scheme that this application embodiment provided brought are:
after receiving a travel navigation request of a carrying 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 at each key passing point, determines a target insurance category matched with the driving route according to the obtained environment data, and then recommends policy data corresponding to the target insurance category to the vehicle. Based on the description, the embodiment of the application realizes the insurance type recommendation for the user according to the environmental data in the trip to be performed, namely, the insurance type recommendation is performed for the user in a personalized manner according to the environmental data in the trip in the vehicle-mounted environment, so that the user can be helped to generate a more adaptive car insurance policy, the user's expectations are more met, the driving safety of the user is more comprehensively ensured, the applicability is strong and the intelligence is realized, and the generation mode of the car insurance policy is enriched.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an implementation environment related to an information recommendation method according to an embodiment of the present application;
FIG. 2 is a flowchart of an information recommendation method according to an embodiment of the present application;
FIG. 3 is a flowchart of an information recommendation method according to an embodiment of the present application;
fig. 4 is a schematic diagram of an overall execution flow of an information recommendation method according to 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
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Before explaining the embodiments of the present application in detail, some terms or abbreviations that may be involved in the embodiments of the present application are explained.
Environmental data: in the embodiment of the application, the environmental data includes, but is not limited to, road condition data and weather data. In one possible implementation, the road condition data includes, but is not limited to: road congestion, traffic time, traffic accidents, ponding road sections, electronic eyes and the like. For weather data, including but not limited to: season, temperature, humidity, wind power, precipitation, etc.
The following describes an implementation environment related to a message pushing method provided in an embodiment of the present application.
Referring to fig. 1, the implementation environment includes a vehicle 101 and a server 102. Wherein the server 102 may also be referred to herein as an internet of vehicles platform or a backend server or cloud server.
The communication between the server 102 and the vehicle 101 may be performed by a mobile network, or may be performed by information transfer of a road side unit, which is not specifically limited in the embodiment of the present application.
Among them, mobile networks include, but are not limited to: third generation mobile communication technology (the 3rd generation mobile communication technology,3G), fourth generation mobile communication technology (the 4th generation mobile communication technology,4G), fourth generation mobile communication technology (the 5th generation mobile communication technology,5G), and the like.
The information recommendation method provided by the embodiment of the application relates to personalized recommendation of the vehicle insurance for the user at the vehicle side, namely, the vehicle insurance is matched for the user according to the environmental data in the trip of the vehicle, the environmental factors in the trip are taken as insurance matching elements, personalized insurance recommendation is carried out, the user can be helped to generate a more adaptive vehicle insurance policy, and the generated insurance also meets the user expectations and can more comprehensively ensure the driving safety of the user.
The information recommendation method provided in the embodiment of the present application is explained in detail below.
Fig. 2 is a flowchart of an information recommendation method provided in an embodiment of the present application. Referring to fig. 2, a method flow provided in an embodiment of the present application includes:
201. and receiving a journey navigation request sent by the vehicle, wherein the journey navigation request comprises a destination.
202. At least one key waypoint on a travel route of the vehicle toward the destination is determined.
203. Environmental data at each key waypoint is acquired.
204. And determining the target insurance type matched with the driving route according to the acquired environmental 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 of a carrying destination sent by a vehicle is received, at least one key passing point on a driving route of the vehicle to the destination is determined, environment data at each key passing point is further obtained, a target insurance category matched with the driving route is determined according to the obtained environment data, and then policy data corresponding to the target insurance category is recommended to the vehicle. Based on the description, the embodiment of the application realizes the insurance type recommendation for the user according to the environmental data in the trip to be performed, namely, the insurance type recommendation is performed for the user in a personalized manner according to the environmental data in the trip in the vehicle-mounted environment, so that the user can be helped to generate a more adaptive car insurance policy, the user's expectations are more met, the driving safety of the user is more comprehensively ensured, the applicability is strong and the intelligence is realized, and the generation mode of the car insurance policy is enriched.
In one possible implementation manner, the determining, according to the acquired environmental data, the target insurance category matched with the driving route includes:
according to the environment data at each key passing point, matching the insuring influence factors in a first list, and inquiring insurance types and weights corresponding to the matched insuring influence factors in a second list;
determining the application weight value of each insurance type according to the matched application influence factors and the corresponding insurance types and weights;
determining a target insurance type matched with the driving route according to the obtained application weight value;
the first list gives the corresponding relation between the environment and the insuring influence factors, and the second list gives the corresponding relation among insurance types, insuring influence factors and weights.
In one possible implementation manner, the determining the application weight value of each insurance category according to the matched application influence factor and the corresponding insurance category and weight includes:
and accumulating the weights of the insurance influence factors belonging to the same insurance category in the matched insurance influence factors to obtain the insurance weight value of each insurance category.
In one possible implementation manner, the determining, according to the obtained application weight value, the target insurance category matched with the driving route includes:
and sequencing the insurance categories according to the order of the applied weight values from large to small, and determining the preset number of insurance categories as target insurance categories matched with the driving route.
In one possible implementation manner, the determining at least one key passing point on a driving route of the vehicle to the destination includes:
determining a plurality of trip points on a travel route of the vehicle toward the destination;
converting the longitude and latitude data of the travel passing points to obtain a plurality of geographic partitions;
and selecting a journey passing point from each geographical zone to obtain the at least one key passing point.
In one possible implementation, after receiving the route navigation request sent by the vehicle, the method further includes:
if the abnormal running environment exists on the running route of the vehicle to the destination, executing the information recommending process; or alternatively, the first and second heat exchangers may be,
and if the running route length of the vehicle to the destination exceeds the distance threshold value, executing the information recommendation process.
Any combination of the above-mentioned optional solutions may be adopted to form an optional embodiment of the present disclosure, which is not described herein in detail.
Fig. 3 is a flowchart of an information recommendation method provided in 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 in an 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 initiates a navigation operation with a destination on the side of a vehicle, the vehicle initiates a journey navigation request to a cloud server.
The information recommendation method provided in the embodiment of the present application may be performed once after each time the vehicle initiates the journey navigation request, or may be performed 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 navigation request sent by 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 one example, an abnormal driving environment may be severe weather or a bad road condition on a driving route. That is, when bad weather or bad road conditions occur, insurance can be recommended to the user in good time.
Second, after receiving a route guidance request sent by a vehicle, if it is determined that the travel route length of the vehicle to the destination exceeds a distance threshold, the following information recommendation process is performed.
In this case, when the future travel distance of the user is long, the insurance can be timely recommended to the user in consideration of the traveling safety on the travel route. Illustratively, the distance threshold may be a value of 500 km or 1000 km, which is not specifically limited in the embodiments of the present application.
302. The cloud server determines a plurality of travel route points on a travel route of the vehicle toward the destination.
In one possible implementation, the travel route points on the travel route include, but are not limited to: scenic spots, landmark buildings, landmark roads, urban counties, villages and towns, and other administrative areas, to which embodiments of the present application are not particularly limited. In addition, the cloud server can store each travel route point on the travel route after obtaining the travel route point.
303. The cloud server converts longitude and latitude data of the travel passing points to obtain a plurality of geographic partitions; and selecting a travel passing point from each geographical zone to obtain at least one key passing point.
In the embodiment of the application, in order to reduce the operation amount and improve the recommendation speed, the cloud server screens key route points from all obtained route points. As an example, the cloud server may perform conversion processing on longitude and latitude data of the trip route points by adopting a GeoHash algorithm, so as to obtain a plurality of geographic partitions; and selecting a trip passing point from each geographical zone to obtain a key passing point on the driving route.
The GeoHash algorithm performs conversion processing on the latitude and longitude data, namely, two-dimensional latitude and longitude data can be encoded into a character string, so that the two-dimensional data is changed into one-dimensional data, namely, the GeoHash algorithm is an algorithm for partitioning geographic positions.
304. And the cloud server acquires the environmental data at each key passing point.
In the embodiment of the application, in order to accurately recommend insurance to a user, the cloud server can acquire environmental data at each key passing point. The cloud server may pull the environmental data from the third-party environmental service organization through the environmental data interface, which is not specifically limited in the embodiment of the present application.
Illustratively, the environmental data acquired at each key waypoint includes, but is not limited to: road condition data and weather data. In one possible implementation, the road condition data includes, but is not limited to: road congestion, traffic time, traffic accidents, ponding road sections, electronic eyes and the like, which are not particularly 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 also not particularly limited in the embodiments of the present application.
In addition, in order to realize personalized recommendation of insurance, the cloud server also obtains insurance related resources from a third party insurance company, namely, the third party insurance company is required to provide insurance materials for realizing the embodiment of the application. In the embodiment of the present application, the cloud server uses a TF-IDF (Term Frequency-inverse text Frequency index) algorithm in combination with text sources such as insurance types, insurance descriptions, and cases of risk to generate a table of associations between insurance types, applied influence factors, and weights, which is referred to herein as a second list. Wherein the second list gives the correspondence between the insurance kind, the applied influence factor and the weight. Illustratively, table 1 below gives one possible form of the second list.
TABLE 1
| Insurance category | Insuring influencing factors | Weighting of |
| Wading danger | Light rain-heavy rain | 0.2-0.7 |
| Wading danger | (Summer) | 0.3 |
| Wading danger | Ponding water | 0.2 |
| Risk of spontaneous combustion | (Summer) | 0.3 |
| Risk of spontaneous combustion | Air temperature of 30-50 deg.f | 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 one example of the second list. In practical applications, the second list may include more insurance types, insuring influencing factors and weights, which are not specifically limited in the embodiments of the present application.
In addition to the second list, the embodiment of the application also provides a table of association between the environment and the insuring influencing factors, and the table of association is referred to herein as the first list. Wherein the first list gives a correspondence between the environment and the insuring influencing factor. Illustratively, table 2 below gives one possible form of the first list.
TABLE 2
| Environment (environment) | Insuring influencing factors |
| Season | (Summer) |
| Temperature (temperature) | Air temperature of 30-50 deg.f |
| Precipitation of water | Light rain-heavy rain |
| Road condition | Water accumulation road section |
| Humidity of the water | Relative humidity 20 |
| …… | …… |
A second point of description that is needed is that table 2 above is only one example given for the first list. In practical applications, the first list may include more environmental information and insuring influencing factors, which is not limited in detail in the embodiments of the present application.
305. And the cloud server determines the target insurance type matched with the driving route according to the environmental data at each key passing point.
In one possible implementation, the target insurance category matching the driving route is determined according to the acquired environmental data, including but not limited to:
3051. and according to the environment data at each key passing point, matching the insuring influence factors in the first list, and inquiring the insurance types and weights corresponding to the matched insuring influence factors in the second list.
The method comprises the steps that 3 key passing points on a driving route are assumed to be a key passing point A, a key passing point B and a key passing point C, wherein environmental data at the key passing point A is heavy rain, environmental data at the key passing point B is air temperature 33 ℃, and environmental data at the key passing point C is air temperature 36.
Then, the key passing point A can be matched to the insuring influence factor of 'light rain-heavy rain' in the first list, the insuring influence factor exists in the second list, and the corresponding insurance category is 'wading insurance', and the weight is 0.2-0.7. For the key passing point B, the insuring influence factor of 'air temperature 30-50' can be matched in the first list, the insuring influence factor exists in the second list, the corresponding insurance type is 'spontaneous combustion insurance', and the weight is 0.4-0.7. The corresponding insurance category and weight have been given above for the key route point C also matching in the first list to the insuring influence factor of "air temperature 30-50".
3052. And determining the application weight value of each insurance type according to the matched application influence factors and the corresponding insurance types and weights.
In one possible implementation, the application weight value of each insurance category is determined according to the matched application influence 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 category in the matched insurance influence factors to obtain the insurance weight value of each insurance category.
Continuing the above example, the insuring influence factors matched according to the environmental data at each key point are respectively small rain-heavy rain, air temperature 30-50 and air temperature 30-50, wherein the insurance type corresponding to the insuring influence factor of the small rain-heavy rain is wading insurance, and the weight is 0.7; the insuring influence factor of 30-50 air temperature appears twice and corresponds to the environments of the key passing point B and the key passing point C respectively, so that corresponding weights can be selected to be accumulated, and the insuring weight value of the spontaneous combustion risk is 0.4+0.4=0.8 by taking the example of the corresponding weight value of the insuring influence factor of 30-50 air temperature as 0.4. That is, in this example, the applied weight value of the wading risk is 0.7, and the applied weight value of the spontaneous combustion risk is 0.8.
It should be noted that, in addition to the aforementioned water risk and spontaneous combustion risk, the insurance types may also include other risks such as glass risk, and the embodiments of the present application are merely exemplified by these two risks.
In addition, if a particular risk fails to match the environmental data in the travel route, the risk's applied weight value is 0.
3053. And determining the target insurance type matched with the driving route according to the obtained application weight value.
In one possible implementation, the target insurance class matching the driving route is determined according to the obtained application weight value, including but not limited to: sorting the insurance categories according to the order of the applied weight values from large to small to obtain an insurance list; a pre-preset number of insurance categories are determined as target insurance categories matching the travel route.
For example, the preset number may have a value of 1, i.e. one insurance class ordered at the forefront is determined as the insurance most suitable for the travel route to be travelled. The preset number may also be greater than 1, that is, a plurality of insurance categories may be recommended to the user, which is not specifically limited in the embodiment of the present application.
306. The cloud server recommends policy data corresponding to the target insurance category 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 side draws an information recommendation page according to the received policy data for displaying to a user, and displays the drawn information recommendation page to the user for browsing by the user. In one possible implementation, the information recommendation page may be in the form of a service card, which is not specifically limited in the embodiments of the present application.
308. The vehicle acquires a 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 in a green 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 effective after receiving the notification message, and the recommending process of one round is finished. It should be noted that the effective period of the policy is at least longer than the duration of the journey.
According to the method provided by the embodiment of the application, after a travel navigation request of a carrying destination sent by a vehicle is received, at least one key passing point on a driving route of the vehicle to the destination is determined, environment data at each key passing point is further obtained, a target insurance category matched with the driving route is determined according to the obtained environment data, and then policy data corresponding to the target insurance category is recommended to the vehicle. Based on the description, the embodiment of the application realizes the insurance type recommendation for the user according to the environmental data in the trip to be performed, namely, the insurance type recommendation is performed for the user in a personalized manner according to the environmental data in the trip in the vehicle-mounted environment, so that the user can be helped to generate a more adaptive car insurance policy, the user's expectations are more met, the driving safety of the user is more comprehensively ensured, the applicability is strong and the intelligence is realized, and the generation mode of the car insurance policy is enriched.
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 journey navigation request and plans the driving route.
c. And the cloud server determines a travel route point on the driving route.
d. And the cloud server determines key passing points on the driving route from the passing points.
e. And the cloud server acquires the environmental data at the key passing points.
The environmental data includes, but is not limited to, weather data and road condition data, which is not specifically limited in the embodiment of the present application.
f. The cloud server obtains insurance material from a third party insurance company.
g. The cloud server combines text sources such as insurance types, insurance descriptions, insurance cases and the like included in the insurance materials to generate a list A among the insurance types, the insurance influencing factors and the weights.
h. The cloud server generates a list B between the environment and the applied influencing factors.
i. And the cloud server determines the application weight value of each dangerous seed according to the environment data, the list A and the list B at the key passing points.
j. And the cloud server ranks the risk seeds according to the obtained application weight value to obtain a ranked insurance list.
k. The cloud server recommends policy data of the dangerous seed most suitable for the journey route in the insurance list to the vehicle.
And l, the vehicle draws an information recommendation page according to the received policy data and displays the drawn information recommendation page.
And m, the vehicle acquires a 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 into a performance state.
According to the method provided by the embodiment of the application, after a travel navigation request of a carrying destination sent by a vehicle is received, at least one key passing point on a driving route of the vehicle to the destination is determined, environment data at each key passing point is further obtained, a target insurance category matched with the driving route is determined according to the obtained environment data, and then policy data corresponding to the target insurance category is recommended to the vehicle. Based on the description, the embodiment of the application realizes the insurance type recommendation for the user according to the environmental data in the trip to be performed, namely, the insurance type recommendation is performed for the user in a personalized manner according to the environmental data in the trip in the vehicle-mounted environment, so that the user can be helped to generate a more adaptive car insurance policy, the user's expectations are more met, the driving safety of the user is more comprehensively ensured, the applicability is strong and the intelligence is realized, and the generation mode of the car insurance policy is enriched.
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:
a receiving module 501 configured to receive a trip navigation request sent by a vehicle, where the trip navigation request includes a destination;
a first determination module 502 configured to determine at least one key waypoint on a travel route of the vehicle toward the destination;
an obtaining module 503 configured to obtain environmental data at each of the key passing points;
a second determining module 504 configured to determine a target insurance category matching the travel route according to the acquired environmental data;
a recommending 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 receiving the travel navigation request of the carrying destination sent by the vehicle, the embodiment of the application can determine at least one key passing point on the driving route of the vehicle to the destination, further acquire the environmental data of each key passing point, determine the target insurance category matched with the driving route according to the acquired environmental data, and then recommend the policy data corresponding to the target insurance category to the vehicle. Based on the description, the embodiment of the application realizes the insurance type recommendation for the user according to the environmental data in the trip to be performed, namely, the insurance type recommendation is performed for the user in a personalized manner according to the environmental data in the trip in the vehicle-mounted environment, so that the user can be helped to generate a more adaptive car insurance policy, the user's expectations are more met, the driving safety of the user is more comprehensively ensured, the applicability is strong and the intelligence is realized, and the generation mode of the car insurance policy is enriched.
In a possible implementation manner, the second determining module 504 is further configured to match the insuring influence factor in the first list according to the environmental data at each key passing point, and query the second list for the insurance category and the weight corresponding to the matched insuring influence factor; determining the application weight value of each insurance type according to the matched application influence factors and the corresponding insurance types and weights; determining a target insurance type matched with the driving route according to the obtained application weight value; the first list gives the corresponding relation between the environment and the insuring influence factors, and the second list gives the corresponding relation among insurance types, insuring influence factors and weights.
In a possible implementation manner, the second determining module 504 is further configured to accumulate weights of the insurance influence factors belonging to the same insurance class in the matched insurance influence factors to obtain an insurance weight value of each insurance class;
in a possible implementation manner, the second determining module 504 is further configured to sort the insurance categories according to the order of the applied weight values from the top to the bottom, and determine the preset number of insurance categories as the target insurance category matched with the driving route.
In one possible implementation, the first determining module 502 is further configured to determine a plurality of travel route points on a travel route of the vehicle toward the destination; converting the longitude and latitude data of the travel passing points to obtain a plurality of geographic partitions; and selecting a journey passing point from each geographical zone to obtain the at least one key passing point.
In a possible implementation manner, the device is further configured to execute the information recommendation process after receiving a travel navigation request sent by a vehicle, if it is determined that an abnormal running environment exists on a running route of the vehicle to the destination; or if it is determined that the length of the travel route of the vehicle toward the destination exceeds a distance threshold, executing the above-described information recommendation process.
Any combination of the above-mentioned optional solutions may be adopted to form an optional embodiment of the present disclosure, which is not described herein in detail.
It should be noted that: in the information recommendation device provided in the above embodiment, only the division of the above functional modules is used for illustration, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the information recommending apparatus and the information recommending method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the information recommending apparatus and the information recommending method 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 have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 601 and one or more memories 602, where at least one instruction is stored in the memories 602, and the at least one instruction is loaded and executed by the processor 601 to implement the information recommendation method provided in the foregoing method embodiments. Of course, the server may also have a wired or wireless network interface, a keyboard, an input/output interface, and other components for implementing the functions of the device, which are not described herein.
In an exemplary embodiment, a computer readable storage medium, such as a memory comprising instructions executable by a processor in a terminal to perform the information recommendation method in the above embodiment is also provided. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
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 for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments is merely exemplary in nature and is in no way intended to limit the invention, since it is intended that all modifications, equivalents, improvements, etc. that fall within the spirit and scope of the invention.
Claims (10)
1. An information recommendation method, the method comprising:
receiving a journey navigation request sent by a vehicle, wherein the journey navigation request comprises a destination;
if the length of the travel route of the vehicle to the destination exceeds a distance threshold, determining a plurality of travel passing points on the travel route of the vehicle to the destination; converting the longitude and latitude data of the travel passing points to obtain a plurality of geographic partitions; selecting a travel passing point from each geographical zone to obtain at least one key passing point;
acquiring environment data at each key passing point; according to the environment data at each key passing point, matching the insuring influence factors in a first list, and inquiring insurance types and weights corresponding to the matched insuring influence factors in a second list; determining the application weight value of each insurance type according to the matched application influence factors and the corresponding insurance types and weights; determining a target insurance type matched with the driving route according to the obtained application weight value;
the first list gives the corresponding relation between the environment and the insuring influence factors, and the second list gives the corresponding relation among insurance types, insuring influence factors and weights; the corresponding relation given by the second list is generated based on insurance types, insurance description and insurance cases in the insurance materials;
recommending policy data corresponding to the target insurance category to the vehicle in the form of a service message;
the vehicle is used for drawing an information recommendation page in the form of a service card according to the received policy data; and after the policy confirmation operation triggered by the user is acquired, a confirmation notification message is sent to the cloud server, wherein the confirmation notification message is used for indicating the cloud server to set the corresponding policy into a green state, and the effective period of the policy is at least longer than the duration of the journey.
2. The method of claim 1, wherein said determining an applied weight value for each insurance category based on said matched applied impact factors and said corresponding insurance categories and weights comprises:
and accumulating the weights of the insurance influence factors belonging to the same insurance category in the matched insurance influence factors to obtain the insurance weight value of each insurance category.
3. The method of claim 1, wherein the determining a target insurance category matching the travel route based on the obtained application weight value comprises:
and sequencing the insurance categories according to the order of the applied weight values from large to small, and determining the preset number of insurance categories as target insurance categories matched with the driving route.
4. A method according to any one of claims 1 to 3, wherein after receiving a journey navigation request sent by a vehicle, the method further comprises:
and if the abnormal running environment exists on the running route of the vehicle to the destination, executing the step of determining a plurality of travel route points on the running route of the vehicle to the destination.
5. An information recommendation device, characterized in that the device comprises:
the receiving module is configured to receive a journey navigation request sent by a vehicle, wherein the journey navigation request comprises a destination;
a first determination module configured to determine a plurality of trip points on a travel route of the vehicle toward the destination if it is determined that a travel route length of the vehicle toward the destination exceeds a distance threshold; converting the longitude and latitude data of the travel passing points to obtain a plurality of geographic partitions; selecting a travel passing point from each geographical zone to obtain at least one key passing point;
an acquisition module configured to acquire environmental data at each of the key route points;
the second determining module is configured to match the insuring influence factors in the first list according to the environmental data at each key passing point, and inquire the insurance types and weights corresponding to the matched insuring influence factors in the second list; determining the application weight value of each insurance type according to the matched application influence factors and the corresponding insurance types and weights; determining a target insurance type matched with the driving route according to the obtained application weight value;
the first list gives the corresponding relation between the environment and the insuring influence factors, and the second list gives the corresponding relation among insurance types, insuring influence factors and weights; the corresponding relation given by the second list is generated based on insurance types, insurance description and insurance cases in the insurance materials;
a recommending module configured to recommend policy data corresponding to the target insurance category to the vehicle in the form of a service message;
the vehicle is used for drawing an information recommendation page in the form of a service card according to the received policy data; and after the policy confirmation operation triggered by the user is acquired, a confirmation notification message is sent to the cloud server, wherein the confirmation notification message is used for indicating the cloud server to set the corresponding policy into a green state, and the effective period of the policy is at least longer than the duration of the journey.
6. The apparatus of claim 5, wherein the second determination module is further configured to accumulate weights of the matched insurance impact factors that are attributed to the same insurance class to obtain the insurance weight value for each insurance class.
7. The apparatus of claim 5, wherein the second determination module is further configured to sort the insurance categories in order of the higher-order insurance weight values, and determine a pre-set number of insurance categories as target insurance categories matching the driving route.
8. The apparatus according to any one of claims 5 to 7, wherein the apparatus is further configured to, upon receiving a route guidance request sent by a vehicle, perform the step of determining a plurality of route passing points on a route along which the vehicle is driven toward the destination if it is determined that an abnormal driving environment exists on the route along which the vehicle is driven toward the destination.
9. A storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the information recommendation method of any one of claims 1 to 4.
10. A server comprising a processor and a memory, the memory having stored therein at least one instruction that is loaded and executed by the processor to implement the information recommendation method of any of claims 1 to 4.
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| CN113177852A (en) * | 2021-04-30 | 2021-07-27 | 东风汽车集团股份有限公司 | Intelligent policy pushing method, device, equipment and storage medium |
| CN113724093A (en) * | 2021-08-26 | 2021-11-30 | 上海赢科信息技术有限公司 | Vehicle insurance accident verification method, system, electronic device and medium |
| CN116777530B (en) * | 2023-08-23 | 2023-11-07 | 山东四季汽车服务有限公司 | Automobile service system based on intelligent recommendation |
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