CN111651681B - Message pushing method and device based on intelligent information recommendation in cloud network fusion environment - Google Patents
Message pushing method and device based on intelligent information recommendation in cloud network fusion environment Download PDFInfo
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Abstract
The embodiment of the application provides a message pushing method and device based on intelligent information recommendation in a cloud network fusion environment. Through the design, the active push prompt of the public safety event information can be provided for the safe trip of the user, and the public safety event is prevented from spreading or bursting.
Description
Technical Field
The application relates to the technical field of information pushing, in particular to a message pushing method and device based on intelligent information recommendation in a cloud network fusion environment.
Background
In an active information push system based on a public safety event location area, an active specific message push model is a core component of the active information push system, and the active public safety event information push system should respond correspondingly with the change of a position of a traveler, so how to effectively and actively push public safety event information is helpful for prevention and control of public safety events, and the active information push system is a technical problem to be solved in the field.
Disclosure of Invention
In view of this, an object of the present application is to provide a message pushing method and apparatus based on intelligent information recommendation in a cloud network convergence environment, which can provide an active pushing prompt of public safety event information for a safe trip of a user, and prevent a public safety event from spreading or bursting.
According to a first aspect of the present application, a message pushing method based on intelligent information recommendation in a cloud network convergence environment is provided, which is applied to a server in communication connection with a user terminal, and the method includes:
acquiring a current matching space road of the user terminal;
inquiring a matched target information recommendation area from a server according to the current matching space road, calculating a push characteristic parameter of the user terminal based on the target information recommendation area, and determining a push message sequence of the user terminal according to the push characteristic parameter;
and carrying out specific message pushing on the user terminal according to the pushing message sequence of the user terminal.
According to a second aspect of the present application, an embodiment of the present application provides a message pushing method and apparatus based on intelligent information recommendation in a cloud network convergence environment, which are applied to a server in communication connection with a user terminal, and the apparatus includes:
the acquisition module is used for acquiring the current matching space road of the user terminal;
the query module is used for querying a matched target information recommendation area from a server according to the current matching space road, calculating a push characteristic parameter of the user terminal based on the target information recommendation area, and determining a push message sequence of the user terminal according to the push characteristic parameter;
and the pushing module is used for pushing the specific message to the user terminal according to the pushing message sequence of the user terminal.
Based on any aspect, the method comprises the steps of firstly obtaining a current matching space road of the user terminal, then inquiring a matched target information recommending area from the server according to the current matching space road, calculating and extracting a push characteristic parameter of the user terminal based on the target information recommending area, and determining a push message sequence of the user terminal according to the push characteristic parameter, so that specific message pushing is carried out on the user terminal according to the push message sequence of the user terminal. Through the design, the active push prompt of the public safety event information can be provided for the safe trip of the user, and the public safety event is prevented from spreading or bursting.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic view illustrating an application scenario of a message pushing system based on intelligent information recommendation in a cloud network convergence environment according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating a message pushing method based on intelligent information recommendation in a cloud network convergence environment according to an embodiment of the present application;
FIG. 3 is a diagram illustrating construction of an octree index provided by an embodiment of the present application;
fig. 4 is a schematic diagram illustrating functional modules of a message pushing method and device based on intelligent information recommendation in a cloud network convergence environment according to an embodiment of the present application;
fig. 5 is a schematic component structural diagram of a server for executing the intelligent information recommendation-based message pushing method in the cloud network convergence environment according to the embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some of the embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
Fig. 1 is a schematic view illustrating an application scenario of a message pushing system 10 based on intelligent information recommendation in a cloud network convergence environment according to an embodiment of the present application. In this embodiment, the message pushing system 10 based on intelligent information recommendation in a cloud network convergence environment may include a server 100 and a user terminal 200 in communication connection with the server 100. The user terminal 200 may include, but is not limited to, a smart phone, a tablet computer, a smart wearable device, and the like, and is not limited in any way.
In other possible embodiments, the message pushing system 10 based on intelligent information recommendation in the cloud network convergence environment may also include only one of the components shown in fig. 1 or may also include other components.
Fig. 2 is a flowchart illustrating a message pushing method based on intelligent information recommendation in a cloud network convergence environment according to an embodiment of the present application, where in this embodiment, the message pushing method based on intelligent information recommendation in the cloud network convergence environment may be executed by the server 100 shown in fig. 1. It should be understood that, in other embodiments, in the message pushing method based on intelligent information recommendation in the cloud network convergence environment of this embodiment, the order of some steps may be interchanged according to actual needs, or some steps may be omitted or deleted. The detailed steps of the intelligent information recommendation-based message pushing method in the cloud network fusion environment are described as follows.
Step S110, a current matching spatial road of the user terminal is obtained.
And step S120, inquiring a matched target information recommendation area from the server according to the current matching space road, calculating and extracting push characteristic parameters of the user terminal based on the target information recommendation area, and determining a push message sequence of the user terminal according to the push characteristic parameters.
Step S130, performing specific message pushing on the user terminal according to the pushing message sequence of the user terminal.
In this embodiment, the matched target information recommendation area may refer to: at least part of the road segments of the current matching spatial road of the user terminal are located in the target information recommendation area.
In this embodiment, the push characteristic parameter may be used to indicate a push mode of a to-be-pushed message for the user terminal in a push process.
In this embodiment, the pushed specific message may be determined according to a specific service situation, for example, taking a public safety event message as an example, the specific message may be early warning information, suggestive information, and the like related to a public safety event.
By adopting the design, the active push prompt of the public safety event information can be provided for the safe trip of the user, and the public safety event is prevented from spreading or bursting.
In one possible implementation, step S110 may be embodied by the following exemplary sub-steps S111-S113, which are described in detail below.
And a substep S111, obtaining a position fitting straight line corresponding to the current matching point position of the user terminal, wherein the current matching point position is the current position coordinate when the user terminal is in a communication state.
In this embodiment, the current matching point position may represent a position coordinate of the user terminal 200 currently in the communication state, for example, the current matching point position may be a longitude and latitude coordinate, or a coordinate corresponding to any other rule (for example, a city geographic coordinate of a city space), and the like, and is not limited specifically herein.
For example, base station communication data of the user terminal 200 at the current time may be acquired, and then the current matching point position of the user terminal 200 is determined according to the base station communication data, so that the current matching point position and each matching point position determined before the current matching point position are linearly fitted, and a position fitting straight line corresponding to the current matching point position is determined.
For example, with the construction of the current mobile communication network, the user of the user terminal 200 will generally actively invoke background operation of applications and various applications, and will keep receiving and sending of communication data with surrounding base stations during the operation of the applications, so that the update frequency of the base station communication data that can be used for analyzing the terminal position is greatly increased, and the current matching point position of the user terminal 200 is determined by performing data processing based on the base station communication data, which can effectively improve the problem of low positioning accuracy caused when performing position positioning only by using CELL-ID.
For example, in one possible implementation, the determination of the current matching point position of the user terminal 200 according to the base station communication data may be implemented in the following manner, which is described in detail below.
(1) The broadcast control channel parameter, the base station identification code parameter and the location area code parameter of the ue 200 at the current time are obtained from the base station communication data.
In this embodiment, a Broadcast Control CHannel (BCCH) is a point-to-multipoint unidirectional Control CHannel, and is used for broadcasting common information to a mobile station by a base station, and transmitting system common Control information, such as a common Control CHannel number and information on whether to combine with an independent dedicated Control CHannel.
In this embodiment, the Base Station Identity Code (BSIC) is an Identity Code formed by a network color Code (BCCBSIC, format: NCC-BCC, NCC range of 0-7, BCC range of 0-7), and can be used to distinguish different operators or different cells with the same broadcast control channel frequency of the same operator.
In this embodiment, the Location Area Code (LAC) may be composed of two bytes, and 16-ary coding is adopted. For example, its usable range is 0001-FFFEH, and code groups 0000H and FFFFH may not be used. Wherein a location area may comprise one or more cells.
(2) Determining each target communication base station communicating with the user terminal 200 according to the broadcast control channel parameter, the base station identification code parameter and the location area code parameter at the current moment, and acquiring the communication signal strength between the user terminal 200 and each target communication base station.
In this embodiment, each target communication base station communicating with the ue 200 may be determined according to a positioning algorithm of the base station by combining the broadcast control channel parameter, the base station identity code parameter, and the location area code parameter at the current time. On this basis, the communication Signal Strength rssi (received Signal Strength indicator) between the user terminal 200 and each target communication base station can be obtained. For example, the relevant communication signal strength can be calculated according to the principle that radio waves or sound waves are transmitted in a medium, the signal power is attenuated along with the propagation distance, and the preset attenuation model between the signal and the distance is used according to the transmitting power of the known signal of the beacon node and the signal power received by the node.
(3) The normalized value of the communication signal strength between the user terminal 200 and each target communication base station is calculated, and the barycentric coordinates corresponding to the user terminal 200 and each target communication base station are calculated according to the communication signal strength between the user terminal 200 and each target communication base station.
An alternative calculation formula for calculating the normalized value of the communication signal strength between the user terminal 200 and each target communication base station may be:
the calculation formula of the barycentric coordinates of the user terminal 200 corresponding to each target communication base station may be:
(4) and determining the current matching point position of the user terminal 200 according to the barycentric coordinate corresponding to each target communication base station and the normalized value of each communication signal intensity of the user terminal 200.
Wherein, according to the barycentric coordinates corresponding to each target communication base station and the normalized value of each communication signal intensity of the user terminal 200, the calculation formula for determining the current matching point position of the user terminal 200 is as follows:
where longitude [ i ] is a longitude value of the barycentric coordinate, latitude [ i ] is a latitude value of the barycentric coordinate, rsisi [ i ] is the communication signal strength of n base stations communicating with the user terminal 200, λ i is a normalized value, and n = 7.
And a substep S112, determining a candidate road segment sequence associated with the current matching point position of the user terminal from a pre-established road database according to the topological relation among all roads in the road network.
For example, a candidate road region centered on the current matching point position of the user terminal 200 and having a preset distance as a radius may be determined, and an initial link sequence of the candidate road region in the road database may be determined. Then, a candidate link sequence associated with the current matching point position of the user terminal 200 is selected from the initial link sequence according to the topological relation between the respective links in the road network.
In this embodiment, the road database may be obtained from a Map data source provided by an Open Map (OSM) website and stored in the server 100. For example, if the road database of the a city is to be acquired, all the road data in the city area of the a city may be searched from the map data source to build the road database of the a city in the server 100.
And a substep S113, determining a matching road corresponding to the current matching point position from the candidate road section sequence according to the position fitting straight line, and using the matching road as the current matching space road of the user terminal.
In one possible implementation, step S120 may be embodied by the following exemplary sub-steps S121-S125, which are described in detail below.
And a substep S121, calculating the push predicted distance of the current matching space road in the target information recommendation area.
For example, the length of the overlapped part road between the current matching space road and the target information recommendation region may be calculated as the push predicted distance.
And a substep S122 of obtaining the familiarity of the road network where the current matching space road is located to the travelers carrying the user terminal and the current walking speed of the travelers.
In this embodiment, the familiarity of the road network where the current matching space road is located with the trip person carrying the user terminal may be determined by the trip times of the trip person carrying the user terminal on the current matching space road, for example, the higher the trip times, the higher the familiarity. The familiarity may be directly a positive correlation parameter of the number of trips, and may be, for example, a value obtained by multiplying the number of trips by a certain coefficient, or may be the number of trips itself, and is not limited in detail herein. The current walking speed of the pedestrian can be acquired in real time through a speed sensor of the user terminal.
And a substep S123 of calculating the theoretical walking speed of the pedestrian according to the familiarity and the current walking speed, and calculating the total pushing time length of the pedestrian in the target information recommendation area according to the theoretical walking speed and the pushing prediction distance.
In this embodiment, the speed correction coefficient related to the familiarity may be obtained, and specifically, the speed correction coefficient may be configured in advance, and then the theoretical walking speed of the pedestrian may be calculated by multiplying the speed correction coefficient by the current walking speed.
Based on the above, the total pushing duration of the pedestrian in the target information recommendation area can be obtained by dividing the pushing prediction distance by the theoretical walking speed.
And a substep S124, obtaining a plurality of specific messages to be pushed, which are matched with the total pushing duration, from a pre-configured specific message database, and respectively determining the pushing duration, the pushing state, the pushing priority and the pushing intensity of each specific message to be pushed as the pushing characteristic parameters of each specific message to be pushed corresponding to the user terminal.
In this embodiment, for different specific messages to be pushed, the pushing duration of the specific message to be pushed may be pre-estimated according to information such as the message length and the message type thereof, and then the specific messages to be pushed that are relatively popular are preferentially matched, until the sum of the pushing durations of the matched specific messages to be pushed matches the total pushing duration, the matched specific messages to be pushed are used as the specific messages to be pushed subsequently. And on the basis, the pushing duration, the pushing state, the pushing priority and the pushing strength of each specific message to be pushed can be determined. The push state may refer to what state form to push, for example, a state in a picture form, a text form, a video form, a voice form, and the like, and may be configured in advance. Furthermore, the push priority may be pre-configured with a trending degree of the particular message to be pushed, with higher push priorities being pushed earlier. The push strength may refer to the number of pushes in the push process.
And a substep S125, performing associated mapping and arrangement on each specific message to be pushed and the pushing characteristic parameter of each specific message to be pushed to obtain a pushing message sequence of the user terminal.
Thus, in one possible implementation, step S130 may be embodied by the following exemplary sub-steps S131-S132, which are described in detail below.
And a substep S131, sorting each specific message to be pushed according to the pushing priority from high to low.
And a substep S132, performing specific message pushing on the user terminal according to the sorted pushing state and pushing strength of each specific message to be pushed.
For example, assume that each sequenced specific message to be pushed is a specific message a to be pushed, a specific message B to be pushed, a specific message C to be pushed, and a specific message D to be pushed, respectively. The pushing states of the specific message A to be pushed, the specific message B to be pushed, the specific message C to be pushed and the specific message D to be pushed are respectively in a picture form, a character form, a video form and a voice form, the pushing strengths are respectively 1, 2 and 3, so that the specific message A to be pushed can be pushed once in the picture form, the specific message B to be pushed is pushed twice in the character form, the specific message C to be pushed is pushed twice in the video form, and the specific message D to be pushed is pushed three times in the voice form.
The specific information to be pushed may be early warning information, or may also be suggestive information, or science popularization information, and the like, which is not specifically limited herein.
In the process, the track data of the travel user contains rich information, so that the historical position and the current position of the user can be directly reflected, and the information of behavior habits, working conditions, social relations and the like of the user is implied. Therefore, the activity track of the target object of the public safety event can be tracked based on the track data, the prevention and control range is reduced, and the prevention and control resources of the public safety event are saved. The user track is one of basic resources based on the position service, and needs to support multiple frequent searches, and in the process of determining the target information recommendation area, when the tracks of a plurality of users are dynamically changed, the frequent search degree is higher. In the present embodiment, the trajectory dynamic data of each target user is generally performed in a two-dimensional plane, and two dimensions, namely longitude and latitude, can be used when representing the space object. When the scale of the trajectory dynamic data is large, the result of the spatiotemporal range query will occupy a large disk space. Therefore, how to efficiently find out the required sub-track from the dynamic data of a plurality of tracks and to read the data from the disk back to the user of a given row with a minimum time delay is an urgent problem to be solved.
Based on this, in the process of determining the target information recommendation region, the embodiment may first acquire a predefined spatiotemporal range of an initial spatiotemporal region, and divide the initial spatiotemporal region into eight same sub-regions according to the spatiotemporal range.
For example, as shown in fig. 3, assuming that the volume range of the initial spatio-temporal region is V, the volume range may be set as a spatio-temporal range covered within a certain city range of the travel user activity within a set time period (e.g. one year). The initial spatiotemporal region may then be Spatio-temporally segmented into eight identical sub-regions.
Then, each sub-region can be subjected to space-time segmentation to form a corresponding Octree index, a root node of the Octree index corresponds to the initial space-time region, and eight sub-nodes (0, 1, 2, 3, 4, 5, 6, and 7) of the root node are respectively and sequentially associated with eight same sub-regions formed by the space-time segmentation in an iterative manner to form the corresponding Octree index. It should be noted that each child node (0, 1, 2, 3, 4, 5, 6, 7) may point to a disk block of the disk space, the disk block is a starting block of a continuous disk space that stores all position data in a space-time range corresponding to the child node (0, 1, 2, 3, 4, 5, 6, 7), and the position data in the space-time range corresponding to each child node occupies a plurality of continuous disk pages of the trace data file in the disk space.
It is worth noting that, considering the large difference between the time dimension and the space dimension, the range of the space dimension is usually kept constant for a long time, while the time dimension can be expanded infinitely. Therefore, in the present embodiment, the spatial range and the temporal range of the saved data can be set by the system administrator according to actual needs.
Furthermore, when a query request needs to span the time ranges of two cubes in the left graph shown in FIG. 3, the query request may be split into two sub-query requests at the boundaries of the spatio-temporal range, with the spatial range remaining unchanged. Thereafter, queries may be performed in the two spatio-temporal cubes, respectively, and the results of the two sub-queries are merged as the final query result. In the process, the technical problem to be solved is how to adjust the structure of the octree index according to the dynamic change of the track dynamic data of each target user, and the input and output times of a disk are ensured to be minimum when the spatio-temporal range query is processed.
Therefore, next, the octree index is further structurally adjusted according to the trajectory dynamic data of each target user, and a target information recommendation area matched with the current matching space road is searched from the disk space according to the octree index by taking the current matching space road of the user terminal as a query request.
Illustratively, the following embodiments can be implemented in the process of performing structural adjustment on the octree index according to the trajectory dynamic data of each target user, which is described in detail below.
(1) And carrying out grid segmentation on the octree index, and taking each minimum sub-area after segmentation as a segmentation grid unit. Wherein, each cutting grid unit represents a three-dimensional space-time area cell = { r = { (r) }x，ry，rt}, three-dimensional space-time area cell = { rx，ry，rtDenotes the time space interval r in three dimensions, respectivelyx，ryAnd rt。
(2) And acquiring the density of the track dynamic data in the three-dimensional space-time area, and reading the overhead value of the track point of the track dynamic data in the three-dimensional space-time area according to the density.
For example, the density D can be separated from the time space r of three dimensions, respectivelyx，ryAnd rtAnd after product calculation, determining the overhead value C of the track points of the track dynamic data in the three-dimensional space-time area.
(3) And updating the segmentation grid unit corresponding to the three-dimensional space-time region according to the overhead value of the track point of the track dynamic data in the three-dimensional space-time region so as to perform structural adjustment on the octree index.
For example, the time-space intervals of the trajectory dynamic data in each dimension may be obtained first, and the input/output overhead value of the trajectory dynamic data may be calculated according to the time-space intervals of the trajectory dynamic data in each dimension. Wherein, the input and output cost value is used to represent a cross probability P (q: _ cell) between the trajectory dynamic data q and the cell of the sliced grid cell, and the size of the cross probability P (q: _ cell) is related to the spatial dimension and the time dimension of the trajectory dynamic data. Illustratively, P (q # cell) may be calculated by:
wherein q is track dynamic data, cell is a segmentation grid unit, and r isx，ryAnd rtRespectively, the time space interval of three dimensions, and V is a space dimension value.
The input-output overhead value can be calculated by the following method:
wherein, CqFor input and output of overhead values, A is a predetermined coefficient, CcellIs the grid size value of the sliced grid cell.
Then, the optimal segmentation size of the segmentation grid unit can be determined according to the input and output overhead values of the track dynamic data. For example, the optimal slicing size for slicing the grid cells may be
。
Then, the split grid unit of the octree index can be adjusted according to the optimal split size of the split grid unit and the track data density of the track dynamic data, and the split grid unit is divided into eight new sub-grid units, or the split grid unit is combined with other seven split grid units.
In this embodiment, the optimal grid segmentation is closely related to the density D of the trajectory dynamic data. However, the distribution of the trace as the system load is not uniform, and more importantly, the trace of the moving object is updated frequently, and the addition of a new trace point will dynamically affect the distribution of the data. Therefore, the optimal mesh slice size will also change dynamically. As the trace dynamic data continues to grow, the structure of the octree index needs to be dynamically adjusted in order to keep the query overhead at a minimum at all times. The system dynamically divides the space, the times of segmentation of the data-dense region are relatively more, and the times of segmentation of the data-sparse region are relatively less. The unequal depth spatial partitioning corresponds to an octree index. The leaf node points to the disk page storing the track covered by the corresponding splitting unit. When new track dynamic data is added, each split grid unit needs to dynamically judge how to adjust, and the following two ways can be specifically adopted.
One way is to continue the segmentation, and segment the segmented grid cell into eight new sub-grid cells; another way is to merge the sliced grid cell with another 7 sliced grid cells. The judgment basis is whether the optimal segmentation size of the segmentation grid unit subjected to query processing is small, when the optimal segmentation size is small, the segmentation grid unit is combined with the other 7 segmentation grid units, and when the optimal segmentation size is large, segmentation is continued, and the segmentation grid unit is segmented into eight new sub-grid units.
In the above-mentioned process of searching the target information recommendation area matched with the current matching space road from the disk space according to the octree index by using the current matching space road of the user terminal as the query request, the sub-area of the related sub-node is searched from the octree index by using the current matching space road of the user terminal as the query request, and the area formed by the position data in the corresponding disk block is acquired from the sub-area and is used as the target information recommendation area matched with the current matching space road.
Based on the same inventive concept, please refer to fig. 4, which is a schematic diagram illustrating functional modules of the message pushing apparatus 110 based on intelligent information recommendation in the cloud network convergence environment according to the embodiment of the present application, and the embodiment may divide the functional modules of the message pushing apparatus 110 based on intelligent information recommendation in the cloud network convergence environment according to the above method embodiment. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module according to each function, the message pushing apparatus 110 recommended based on the intelligent information in the cloud network convergence environment shown in fig. 4 is only a schematic apparatus diagram. The message pushing device 110 based on intelligent information recommendation in the cloud network convergence environment may include an obtaining module 111, an inquiring module 112, and a pushing module 113, and the functions of the function modules of the message pushing device 110 based on intelligent information recommendation in the cloud network convergence environment are described in detail below.
The obtaining module 111 is configured to obtain a current matching space road of the user terminal. It is understood that the obtaining module 111 can be used to execute the step S110, and for the detailed implementation of the obtaining module 111, reference can be made to the content related to the step S110.
The query module 112 is configured to query the matched target information recommendation area from the server according to the current matching spatial road, calculate a push characteristic parameter of the user terminal based on the target information recommendation area, and determine a push message sequence of the user terminal according to the push characteristic parameter. It is understood that the query module 112 can be used to execute the step S120, and the detailed implementation of the query module 112 can refer to the content related to the step S120.
The pushing module 113 is configured to perform specific message pushing on the user terminal according to a pushing message sequence of the user terminal. It is understood that the pushing module 113 may be configured to perform the step S130, and for the detailed implementation of the pushing module 113, reference may be made to the content related to the step S130.
Based on the same inventive concept, please refer to fig. 5, which shows a schematic block diagram of a server 100 for executing the method for pushing a message based on intelligent information recommendation in a cloud network convergence environment according to an embodiment of the present application, where the server 100 may include a message pushing apparatus 110 based on intelligent information recommendation in a cloud network convergence environment, a machine-readable storage medium 120, and a processor 130.
The machine-readable storage medium 120 is used to store machine-executable instructions for performing aspects of the present application. The processor 130 is configured to execute machine executable instructions stored in the machine readable storage medium 120 to implement the message pushing method based on intelligent information recommendation in the cloud network convergence environment provided by the foregoing method embodiments.
The message pushing apparatus 110 based on smart information recommendation in the cloud network convergence environment may include software functional modules (for example, the obtaining module 111, the querying module 112, and the pushing module 113 shown in fig. 4) stored in the machine-readable storage medium 120, so as to implement the message pushing method based on smart information recommendation in the cloud network convergence environment provided by the foregoing method embodiment when the processor 130 executes the software functional modules in the message pushing apparatus 110 based on smart information recommendation in the cloud network convergence environment.
Since the server 100 provided in the embodiment of the present application is another implementation form of the method embodiment executed by the server 100, and the server 100 may be configured to execute the message pushing method recommended based on the intelligent information in the cloud network convergence environment provided in the above method embodiment, reference may be made to the above method embodiment for obtaining technical effects, and details are not described here.
The above description is only for various embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and all such changes or substitutions are included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. A message pushing method based on intelligent information recommendation in a cloud network convergence environment is characterized by being applied to a server in communication connection with a user terminal, and the method comprises the following steps:
acquiring a current matching space road of the user terminal;
inquiring a matched target information recommendation area from a server according to the current matching space road, calculating and extracting a push characteristic parameter of the user terminal based on the target information recommendation area, and determining a push message sequence of the user terminal according to the push characteristic parameter;
specific message pushing is carried out on the user terminal according to the pushing message sequence of the user terminal;
the step of calculating and extracting the push characteristic parameter of the user terminal and determining the push message sequence of the user terminal according to the push characteristic parameter comprises the following steps:
calculating a push predicted distance of the current matching space road in the target information recommendation area;
acquiring the familiarity of the road network where the current matching space road is located to a person who goes out with the user terminal and the current walking speed of the person who goes out;
calculating a theoretical walking speed of the pedestrian according to the familiarity and the current walking speed, and calculating a total pushing duration of the pedestrian in the target information recommendation area according to the theoretical walking speed and the pushing prediction distance;
acquiring a plurality of specific messages to be pushed matched with the total pushing duration from a pre-configured specific message database, and respectively determining the pushing duration, the pushing state, the pushing priority and the pushing strength of each specific message to be pushed as pushing characteristic parameters of each specific message to be pushed corresponding to the user terminal;
the method comprises the following steps of performing associated mapping and arrangement on each specific message to be pushed and the pushing characteristic parameters of each specific message to be pushed to obtain a pushing message sequence of the user terminal, wherein the step of obtaining the current matching space road of the user terminal comprises the following steps:
acquiring a position fitting straight line corresponding to the current matching point position of the user terminal, wherein the current matching point position is the current position coordinate of the user terminal in a communication state;
determining a candidate road section sequence associated with the current matching point position of the user terminal from a pre-established road database according to the topological relation among all roads in the road network;
determining a matching road corresponding to the current matching point position from the candidate road section sequence according to the position fitting straight line, and using the matching road as a current matching space road of the user terminal;
the method further comprises a step of determining the target information recommendation area, and specifically comprises the following steps:
acquiring a predefined space-time range of an initial space-time region, and segmenting the initial space-time region into eight same sub-regions according to the space-time range;
performing space-time segmentation on each subregion to form a corresponding octree index, wherein a root node of the octree index corresponds to the initial space-time region, eight child nodes of the root node are respectively and sequentially associated with eight same subregions formed by the space-time segmentation in an iterative manner to form a corresponding octree index, each child node points to a disk block of a disk space, the disk block is a starting block of a continuous disk space for storing all position data in a space-time range corresponding to the child node, and the position data in the space-time range corresponding to each child node occupies a plurality of continuous disk pages of an orbit data file in the disk space;
and performing structure adjustment on the octree index according to the track dynamic data of each target user, and searching a target information recommendation area matched with the current matching space road from a disk space by taking the current matching space road of the user terminal as a query request according to the octree index.
2. The method for pushing the message based on the intelligent information recommendation in the cloud network convergence environment according to claim 1, wherein the step of pushing the specific message to the user terminal according to the push message sequence of the user terminal includes:
sequencing each specific message to be pushed according to the sequence of the pushing priority from high to low;
and pushing the specific message to the user terminal according to the sequenced pushing state and pushing strength of each specific message to be pushed.
3. The message pushing method based on intelligent information recommendation in the cloud network convergence environment according to claim 1, wherein the step of performing structure adjustment on the octree index according to the trajectory dynamic data of each target user comprises:
performing grid segmentation on the octree index, taking each segmented minimum sub-region as a segmentation grid unit, wherein each segmentation grid unit represents a three-dimensional space-time region, and the three-dimensional space-time regions respectively represent time space intervals in three dimensions;
acquiring the density of the track dynamic data in the three-dimensional space-time area, and reading the overhead value of the track point of the track dynamic data in the three-dimensional space-time area according to the density;
and updating the segmentation grid unit corresponding to the three-dimensional space-time region according to the overhead value of the track point of the track dynamic data in the three-dimensional space-time region so as to perform structural adjustment on the octree index.
4. The message pushing method based on intelligent information recommendation in the cloud network fusion environment according to claim 3, wherein the step of updating the split grid units corresponding to the three-dimensional space-time region according to the overhead values of the trajectory points of the trajectory dynamic data in the three-dimensional space-time region to perform structure adjustment on the octree index includes:
acquiring time-space intervals of the track dynamic data in each dimension, and calculating input and output overhead values of the track dynamic data according to the time-space intervals of the track dynamic data in each dimension, wherein the input and output overhead values are used for representing the cross probability between the track dynamic data and the segmentation grid unit, and the size of the cross probability is related to the space dimension and the time dimension of the track dynamic data;
determining the optimal segmentation size of the segmentation grid unit according to the input and output overhead value of the track dynamic data;
adjusting the split grid unit of the octree index according to the optimal split size of the split grid unit and the track data density of the track dynamic data, and dividing the split grid unit into eight new sub-grid units in a dividing way or combining the split grid unit with other seven split grid units.
5. The method for pushing the message based on the intelligent information recommendation in the cloud network convergence environment according to claim 3, wherein the step of searching the target information recommendation area matched with the current matching space road from the disk space according to the octree index by using the current matching space road of the user terminal as a query request comprises:
and searching a sub-region of a related child node from the octree index by taking the current matching space road of the user terminal as a query request, and acquiring a region formed by position data in a corresponding disk block from the sub-region as a target information recommendation region matched with the current matching space road.
6. The utility model provides a message pusher based on intelligent information recommends under cloud network fuses environment which characterized in that, is applied to the server with user terminal communication connection, the device includes:
the acquisition module is used for acquiring the current matching space road of the user terminal;
the query module is used for querying a matched target information recommendation area from a server according to the current matching space road, calculating and extracting push characteristic parameters of the user terminal based on the target information recommendation area, and determining a push message sequence of the user terminal according to the push characteristic parameters;
the pushing module is used for pushing a specific message to the user terminal according to the pushing message sequence of the user terminal;
the query module is specifically configured to:
calculating a push predicted distance of the current matching space road in the target information recommendation area;
acquiring the familiarity of the road network where the current matching space road is located to a person who goes out with the user terminal and the current walking speed of the person who goes out;
calculating a theoretical walking speed of the pedestrian according to the familiarity and the current walking speed, and calculating a total pushing duration of the pedestrian in the target information recommendation area according to the theoretical walking speed and the pushing prediction distance;
acquiring a plurality of specific messages to be pushed matched with the total pushing duration from a pre-configured specific message database, and respectively determining the pushing duration, the pushing state, the pushing priority and the pushing strength of each specific message to be pushed as pushing characteristic parameters of each specific message to be pushed corresponding to the user terminal;
performing associated mapping and arrangement on each specific message to be pushed and the pushing characteristic parameters of each specific message to be pushed to obtain a pushing message sequence of the user terminal, wherein the method for obtaining the current matching space road of the user terminal comprises the following steps:
acquiring a position fitting straight line corresponding to the current matching point position of the user terminal, wherein the current matching point position is the current position coordinate of the user terminal in a communication state;
determining a candidate road section sequence associated with the current matching point position of the user terminal from a pre-established road database according to the topological relation among all roads in the road network;
determining a matching road corresponding to the current matching point position from the candidate road section sequence according to the position fitting straight line, and using the matching road as a current matching space road of the user terminal;
the query module is further configured to determine a step of the target information recommendation area, and specifically includes:
acquiring a predefined space-time range of an initial space-time region, and segmenting the initial space-time region into eight same sub-regions according to the space-time range;
performing space-time segmentation on each subregion to form a corresponding octree index, wherein a root node of the octree index corresponds to the initial space-time region, eight child nodes of the root node are respectively and sequentially associated with eight same subregions formed by the space-time segmentation in an iterative manner to form a corresponding octree index, each child node points to a disk block of a disk space, the disk block is a starting block of a continuous disk space for storing all position data in a space-time range corresponding to the child node, and the position data in the space-time range corresponding to each child node occupies a plurality of continuous disk pages of an orbit data file in the disk space;
and performing structure adjustment on the octree index according to the track dynamic data of each target user, and searching a target information recommendation area matched with the current matching space road from a disk space by taking the current matching space road of the user terminal as a query request according to the octree index.
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