CN111815101B

CN111815101B - Information processing method and device, storage medium and electronic equipment

Info

Publication number: CN111815101B
Application number: CN202010043019.XA
Authority: CN
Inventors: 程浩; 周子慕; 童咏昕; 张凌宇; 朱宏图; 叶杰平
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2020-01-15
Filing date: 2020-01-15
Publication date: 2024-05-03
Anticipated expiration: 2040-01-15
Also published as: CN111815101A

Abstract

The embodiment of the disclosure provides an information processing method, an information processing device, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring historical online data, first characteristic information and second characteristic information of each target user, wherein the first characteristic information at least comprises time characteristic information and weather characteristic information, and the second characteristic information at least comprises user characteristic information; inputting historical online data, first characteristic information and second characteristic information into a trained prediction model, and obtaining online time of each target user in a preset time period; grouping target users based on online time length, and ordering the target users in each group; and pairing the target users based on the sequencing result. The method and the system can avoid waste of server resources and social resources, so that the resource utilization rate of road network traffic and the operation efficiency of network vehicle-restraining are further improved.

Description

Information processing method and device, storage medium and electronic equipment

Technical Field

The disclosure relates to the technical field of information processing, and in particular relates to an information processing method, an information processing device, a storage medium and electronic equipment.

Background

In general, in order to promote the work enthusiasm of the network about car driver, increase the income of the network about car driver, can issue some competition matters on the network about car platform, the network about car driver can voluntarily sign into the competition, thereby the network about car driver works in the form of competition, and the resource utilization rate of road network traffic and the operation efficiency of the network about car can be effectively improved.

In general, a period of time before a formal competition is required to issue a competition item, such as a competition time, on a network taxi platform in advance for a network taxi driver to register, and to group and allocate the registered network taxi drivers during the period of time. However, because an emergency situation may exist in the period of the competition time, a part of network taxi drivers cannot participate in the competition or cannot complete the whole competition, and each network taxi driver has a paired network taxi driver, in the formal competition, the network taxi drivers paired with the network taxi drivers of the emergency situation cannot participate in the competition or participate in the competition, and the like, so that grouping and pairing results are invalid, the server resource waste and the social resource waste are caused, and the resource utilization rate of road network traffic and the operation efficiency of the network taxi cannot be further improved.

Disclosure of Invention

In view of the foregoing, an object of an embodiment of the present disclosure is to provide an information processing method, an apparatus, a storage medium, and an electronic device, which can avoid the problem that in the prior art, the resource utilization rate of road network traffic and the operation efficiency of network traffic can not be further improved.

In a first aspect, an embodiment of the present disclosure provides an information processing method, including:

Acquiring historical online data, first characteristic information and second characteristic information of each target user, wherein the first characteristic information at least comprises time characteristic information and weather characteristic information, and the second characteristic information at least comprises user characteristic information;

inputting the historical online data, the first characteristic information and the second characteristic information into a trained prediction model, and obtaining the online time of each target user in a preset time period;

grouping the target users based on the online time period, and ordering the target users in each group;

And pairing the target users based on the sequencing result.

In one possible implementation, the predictive model is trained by:

acquiring a first historical online time length of each historical user at a first time point;

Acquiring a temporary historical online time length of a second time point which is a first preset time interval after the first time point based on the first historical online time length and the first characteristic information;

and correcting the temporary historical online time length through the second characteristic information, and determining a second historical online time length of the historical user at the second time point.

In one possible implementation manner, the inputting the historical online data, the first feature information and the second feature information into a trained prediction model, and the obtaining the online time of each target user in a preset time period includes:

Acquiring adjacent online time lengths of adjacent time points which are separated from each other by a second preset time interval before a starting time point of a preset time period;

determining at least one continuous time point within the preset time period according to the second preset time interval after the starting time point;

acquiring the unit online time length of a first time point through the prediction model based on the adjacent online time length;

sequentially acquiring the unit on-line duration of each time point after the first time point according to a time sequence;

summing the online time durations of all the units at the time points in the preset time period, and obtaining the online time duration of each target user in the preset time period.

In one possible implementation, the grouping the target users based on the online time period, and ordering the target users in each group includes:

Determining at least one online time period grouping reference value;

dividing the online time lengths of all the target users into a plurality of subgroups based on the online time length grouping reference value;

In each of the subgroups, the target users are ranked based on third characteristic information.

In a possible implementation manner, the pairing in the target user based on the sorting result includes:

pairing the target users in each of the subgroups based on the values of the third characteristic information;

When unpaired target users exist in each of the subgroups, a new subgroup is constructed based on the unpaired target users in all of the subgroups, and reordered and paired based on the online time period.

In a second aspect, an embodiment of the present disclosure further provides an information processing apparatus, including:

The first acquisition module is used for acquiring historical online data, first characteristic information and second characteristic information of each target user, wherein the first characteristic information at least comprises time characteristic information and weather characteristic information, and the second characteristic information at least comprises user characteristic information;

The second acquisition module is used for inputting the historical online data, the first characteristic information and the second characteristic information into a trained prediction model to acquire the online time length of each target user in a preset time period;

A ranking module for grouping the target users based on the online time period and ranking the target users in each group;

and the pairing module is used for pairing the target users based on the sequencing result.

In one possible embodiment, the method further comprises:

the training module is used for acquiring a first historical online time length of each historical user at a first time point;

In one possible embodiment, the second acquisition module includes:

a first acquisition unit configured to acquire an adjacent online time length of an adjacent time point that is separated by a second preset time interval before a start time point of a preset time period;

A first determining unit configured to determine at least one continuous time point at the second preset time interval within the preset time period from the start time point;

The second obtaining unit is used for obtaining the unit online time length of the first time point through the prediction model based on the adjacent online time length;

the third acquisition unit is used for sequentially acquiring the unit on-line duration of each time point after the first time point according to time sequence;

And a fourth obtaining unit, configured to sum the online durations of the units at all the time points in the preset time period, and obtain the online duration of each target user in the preset time period.

In one possible implementation, the sorting module includes:

a second determining unit for determining at least one online time period grouping reference value;

A grouping unit configured to group the online time lengths of all the target users into a plurality of subgroups based on the online time length grouping reference value;

And a ranking unit for ranking the target users based on third feature information in each of the subgroups.

In one possible embodiment, the pairing module includes:

a pairing unit configured to pair the target users based on the value of the third characteristic information in each of the subgroups;

a construction unit for constructing a new group based on the unpaired target users in all the subgroups and reordering and pairing based on the online time period when unpaired target users exist in each of the subgroups.

In a third aspect, the disclosed embodiments also provide a computer readable storage medium, wherein the computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the information processing method as described.

In a fourth aspect, an embodiment of the present disclosure further provides an electronic device, including: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating over the bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of the information processing method as described.

In the embodiment of the disclosure, the historical online data, the first characteristic information and the second characteristic information of each target user are utilized to predict the online time length of each target user in a preset time period, and pairing is performed according to the online time length of each target user, so that the problem that when part of target users cannot participate in a competition on time or cannot complete the whole competition course, the target users paired with the target users in emergency cannot participate in the competition or cannot participate in the competition is solved to a certain extent, the waste of server resources and social resources can be avoided, and the resource utilization rate of road network traffic and the operation efficiency of network about vehicles are further improved.

The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.

FIG. 1 shows a flow chart of an information processing method provided by an embodiment of the present disclosure;

FIG. 2 illustrates a flow chart of training a predictive model in an information processing method provided by an embodiment of the present disclosure;

fig. 3 is a flowchart illustrating inputting historical online data, first feature information and second feature information into a trained prediction model to obtain online time of each target user in a preset time period in an information processing method according to an embodiment of the present disclosure;

FIG. 4 is a flow chart showing grouping of target users based on online time length and ordering of target users in each group in an information processing method provided by an embodiment of the present disclosure;

fig. 5 shows a flowchart of pairing the target users based on the sorting result in the information processing method provided by the embodiment of the disclosure;

Fig. 6 shows a schematic structural diagram of an information processing apparatus provided by an embodiment of the present disclosure;

fig. 7 shows a schematic structural diagram of an electronic device provided by an embodiment of the disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present disclosure. It will be apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without the need for inventive faculty, are within the scope of the present disclosure, based on the described embodiments of the present disclosure.

Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.

In order to keep the following description of the embodiments of the present disclosure clear and concise, the present disclosure omits detailed description of known functions and known components.

Aiming at the problems existing in the prior art, the embodiment of the disclosure provides an information processing method, an information processing device, electronic equipment and a storage medium, which can avoid the problem that a part of network taxi drivers cannot participate in a match on time or cannot complete the whole match course, or the network taxi drivers paired with the network taxi drivers in emergency cannot participate in the match or participate in the match with no effect to a certain extent, namely, avoid the waste of server resources and social resources.

A first aspect of the present disclosure provides an information processing method, as shown in fig. 1, which is a flowchart of the information processing method when a server or a processor is used as an execution body in an embodiment of the present disclosure, and specifically includes the following steps:

S101, historical online data, first characteristic information and second characteristic information of each target user are obtained, wherein the first characteristic information at least comprises time characteristic information and weather characteristic information, and the second characteristic information at least comprises user characteristic information.

When the game items are released on the network appointment vehicle platform, at least the area facing the game, the game time, the game duration, the registration rules and the like are released. Considering that the competition result is affected by territory, the network taxi drivers need to be grouped according to the regions before each competition, specifically, the registration addresses of the network taxi drivers are searched, and the network taxi drivers corresponding to the registration addresses belonging to the same region are determined as the same group according to the registration address of each network taxi driver, so that the influence of territory comparison competition result is avoided. For example, the server acquires the registration addresses of the network about vehicle drivers in advance, determines all network about vehicle drivers whose registration addresses belong to beijing as beijing group, determines all network about vehicle drivers whose registration addresses belong to hebei as hebei group, and the like, and of course, the embodiment of the present disclosure is not limited to the province as the grouping basis, but may also use city as the grouping basis, use county as the grouping basis, and the like. Then, the same competition is distributed to the network taxi drivers in the area according to the adjustment (or non-adjustment) of the competition time, the competition duration, the registration rules and the like of the area, so that the network taxi drivers in the area can register.

Wherein, for each area, the net car driver registering the competition in the area is taken as the target user.

In a specific implementation, the registration period is preset, i.e. registration is valid during the registration period. And after the registration time period is over, acquiring historical online data, first characteristic information and second characteristic information of each target user.

Specifically, the historical online data at least includes a historical online time length, and for each target user, the historical online time lengths in a preset time period after a historical time point corresponding to a current time point of the target user in a plurality of preset periods are obtained. The first characteristic information at least comprises time characteristic information and weather characteristic information, and the time characteristic information and the weather characteristic information of the target user at the current time point in a plurality of preset periods are acquired for each target user. Of course, the first characteristic information may also include traffic condition information, road quality information, and the like, to which the embodiments of the present disclosure are not limited.

For example, when the time of the match is 8 to 12 pm on sunday and the preset period is 7 days (one week), after determining the target users registering for the match, searching the historical online time of 8 to 12 pm before 7 days, 14 days, 21 days and 28 days for each target user; the weather characteristic information from 8 to 12 points in 7 days before and 8 to 12 points in 14 days before and 8 to 12 points in 21 days before and 8 to 12 points in 28 days before is obtained from the weather platform, wherein the weather characteristic information comprises weather state, temperature, rainfall intensity, wind power level and the like; and determining time characteristic information of 8 to 12 points in the evenings before 7 days, 14 days, 21 days and 28 days, wherein the time characteristic information comprises the time length of 8 to 12 points, whether 8 to 12 points in the evenings before 7 days, 14 days, 21 days and 28 days belong to the week, whether the evenings belong to holidays, whether the evenings are road sealing days and the like.

Of course, there is an intersection between the time of the game and two consecutive days, for example, the time of the game is 10 pm on sunday to 2 am on monday, at this time, the historical online data, the first characteristic information and the second characteristic information of each target user can be obtained in the same manner as described above.

In consideration of the fact that the preset period time is long, the historical online time length stability of each period is poor, therefore, the historical online data can further comprise historical average online time lengths, and specifically, the historical average online time lengths corresponding to the target user at the current time point in a plurality of preset periods are calculated by utilizing the plurality of historical online time lengths. Here, the current time point is a time point when the game starts, and the period of time is less than or equal to a preset period of time (i.e., a game duration). The online time is the working time. For example, if the preset time period is 1 hour, then the adjacent online time period is acquired within one hour before 8 pm on sunday, i.e., 7 to 8 pm.

Meanwhile, second characteristic information of each target user is acquired, wherein the second characteristic information at least comprises user characteristic information, and the user characteristic information comprises gender, age and the like of the target user. Of course, the second characteristic information may also include a historical receipt amount, a historical driving range, and the like of the target user.

S102, inputting the historical online data, the first characteristic information and the second characteristic information into a trained prediction model, and obtaining the online time of each target user in a preset time period.

In a specific implementation, a trained prediction model is input based on historical online data, first characteristic information and second characteristic information, and online time of each target user in a preset time period after a current time point is predicted. The preset time period is the competition duration.

Specifically, the historical online data, the first characteristic information and the second characteristic information are input into a trained prediction model, and the prediction model outputs the online time of the target user in a preset time period. The prediction model in the embodiment of the present disclosure includes a sequence-to-sequence (Sequence to sequence, seq2 Seq) model, a markov model, and the like, and other models capable of achieving the purpose of prediction may be implemented into the information processing method provided in the embodiment of the present disclosure.

In order to ensure the accuracy of the prediction model, the prediction model to be trained is trained by using the historical working data of the historical user. Further, the prediction model is trained according to the method shown in fig. 2, and the specific steps are as follows:

s201, acquiring a first historical online time of each historical user at a first time point.

Specifically, after the historical users are determined, screening a plurality of groups of training samples from all the historical work data of each historical user, wherein each group of training samples comprises a first historical online time length corresponding to each historical user at a first time point, first characteristic information of the historical user, second characteristic information of the historical user and a real second historical online time length of a second time point, and the second time point is a time point which is a first preset time interval after the first time point.

S202, acquiring temporary historical online time of a second time point which is a first preset time interval after a first time point based on the first historical online time and the first characteristic information.

In the training process, based on the first historical online time length and the first characteristic information of the historical user, after a series of calculation is performed, the temporary historical online time length of a second time point which is a first preset time interval after the first time point is obtained.

And S203, correcting the temporary historical online time through the second characteristic information, and determining a second historical online time of the historical user at a second time point.

Correcting the temporary historical online time length through second characteristic information after the temporary historical online time length of a second time point which is a first preset time interval after the first time point is acquired, determining the second historical online time length of a historical user at the second time point, comparing the second historical online time length determined by the prediction model with the real second historical online time length to obtain an error value of the current round of prediction model, and repeating the current round of training until the error value is smaller than or equal to the preset threshold value if the error value is larger than the preset threshold value, and finishing training to obtain the prediction model.

After the trained prediction model is obtained, the prediction model is used for predicting the online time of each target user in a preset time period, and the method is convenient and quick, and has high accuracy and efficiency. Specifically, after determining the prediction model, according to the method shown in fig. 3, the online time of each target user in a preset time period is predicted based on the historical online data, the first feature information and the second feature information, wherein the specific steps are as follows:

S301, acquiring adjacent online time lengths of adjacent time points which are separated by a second preset time interval before a starting time point of a preset time period.

In a specific implementation, the historical online data, the first feature information and the second feature information of each target user are acquired, and meanwhile, the adjacent online time lengths of adjacent time points, which are separated by a second preset time interval before the starting time point of the preset time period, are acquired, wherein the second preset time interval can be the same as or different from the first preset time interval. In consideration of the fact that the first preset time interval is used when determining the prediction model, it is preferable to set the second preset time interval to be the same as the first preset time interval when actually using the prediction model to ensure the accuracy of the prediction model.

S302, after the starting time point, at least one continuous time point is determined according to a second preset time interval in a preset time period.

After the starting time point, determining at least one continuous time point according to a second preset time interval in a preset time period, for example, determining a time point according to the second preset time interval in the preset time period, wherein the time corresponding to the second preset time interval is the same as the time corresponding to the preset time period; and determining two time points in the preset time period according to the second preset time interval, wherein the time points represent that twice of the duration corresponding to the second preset time interval is identical to the duration corresponding to the preset time period, and the like.

S303, acquiring the unit online time length of the first time point through a prediction model based on the adjacent online time lengths.

After determining at least one continuous time point, acquiring the unit online time length of the first time point through a prediction model based on the adjacent online time length, namely inputting the adjacent online time length, the historical online data, the first characteristic information and the second characteristic information into the prediction model, and outputting the unit online time length of the first time point by the prediction model.

S304, sequentially acquiring the unit on-line duration of each time point after the first time point according to the time sequence.

And the operation is circulated, the adjacent online time length, the historical online data, the first characteristic information and the second characteristic information of the previous time point are input into a prediction model, the prediction model outputs the unit online time length of the next time point, and therefore the unit online time length of each time point after the first time point is sequentially obtained according to the time sequence until the unit online time length of each time point is predicted.

S305, summing the unit online time durations of all time points in a preset time period, and obtaining the online time duration of each target user in the preset time period.

And after predicting the unit on-line time length of each time point, summing the unit on-line time lengths of all the time points in a preset time period, and obtaining the on-line time length of each target user in the preset time period.

For example, the time of the game is 8 to 11 pm on sunday, that is, the preset time period is 3 hours, and the duration corresponding to the preset time period is set to be 3 times the duration corresponding to the second preset time interval, that is, each second preset time interval is 1 hour. The successive time points determined from the start time point 8 are 9, 10 and 11 points, respectively. For each target user, predicting the unit online time length of the target user at 9 points according to the time sequence based on the historical online data of 8 points, the first characteristic information, the second characteristic information and the first adjacent online time length (namely the online time length of 7 points to 8 points of target users); then, based on the 10-point historical online data, the first characteristic information, the second characteristic information and the second adjacent online time length (namely, the predicted online time length of the target user in 8-9 points), predicting the unit online time length of the target user in 10 points; then, based on the historical online data of 11 points, the first characteristic information, the second characteristic information and the third adjacent online time length (namely, the predicted online time length of the target user within 9 to 10 points), the unit online time length of the target user at 11 points is predicted. And finally, carrying out summation calculation on the unit on-line time length of the target user at 9 points, the unit on-line time length of the target user at 10 points and the unit on-line time length of the target user at 11 points, wherein the obtained summation value is the on-line time length of the target user at 8 points to 11 points (namely a preset time period) at night.

It should be noted that, when the prediction calculation still has a certain error and at least twice of the duration corresponding to the second preset time interval is the same as the duration corresponding to the preset time period, when multiple rounds of prediction calculation are needed, the calculation result of the second round needs to be calculated according to the calculation result of the first round, and the error of the calculation result of the last round is increased after multiple iterations of calculation, that is, the accuracy is lower. When the duration corresponding to the second preset time interval is the same as the duration corresponding to the preset time period, only one round of prediction calculation is needed, and compared with the calculation result obtained when at least twice of the duration corresponding to the second preset time interval is the same as the duration corresponding to the preset time period, the accuracy is higher. Therefore, in practical application, the duration corresponding to the second preset time interval is preferably set to be the same as the duration corresponding to the preset time period, so that the online duration with higher accuracy is obtained.

S103, grouping target users based on online time length, and sequencing the target users in each group.

In a specific implementation, after obtaining the online time length of each target user in a preset time period, in order to avoid the problem that the target users paired with the target users in the emergency cannot participate in the competition or cannot complete the whole competition course due to the occurrence of the emergency, all the target users are grouped based on the estimated online time length of each target user, and then the target users in each group are ordered according to the order from long to short in online time length.

Specifically, referring to the method shown in fig. 4, in grouping target users based on online time length and sorting the target users in each group, the specific steps are as follows:

S401, determining at least one online time length grouping reference value.

Here, the online time period grouping reference value is the maximum time interval that can be allowed for the online time periods of all the target users in the same group at the time of the race. The determination may be made based on the number of target users, the duration of the preset time period, etc.

Of course, the online time period grouping reference value may be changed when the number of subsequent persons is small.

S402, based on the online time length grouping reference value, the online time lengths of all target users are divided into a plurality of subgroups.

Here, the number of subgroups is determined based on the determined on-line time period grouping reference value. For example, the game time length is3 hours, and the online time length grouping reference value may be set to 0.2 hours, whereby all target users may be grouped into 15 groups, that is, 3/0.2=15. Of course, the online time period grouping reference value can be adjusted at any time according to the preset time period and the number of target users.

After a plurality of subgroups are determined according to the preset time period and the online time period grouping reference value, each subgroup corresponds to a sub-time period, a sub-time period in which the online time period of each target user falls is determined for each target user, and then the target user is determined to be in the subgroup corresponding to the sub-time period in which the online time period is expected to fall.

S403, in each subgroup, sorting the target users based on the third characteristic information.

After grouping all the target users is completed, the target users are ranked for each group of target users based on the third characteristic information. Specifically, for each group, third characteristic information of each target user in the group is acquired, wherein the third characteristic information comprises the number of orders, order scores and the like of each target user in a certain time period, and corresponding weight values are set for the number of orders, the order scores and the like.

In the embodiment of the disclosure, the service data including the number of orders and the order score are taken as an example to make a detailed explanation, after the number of orders, the order score, the weight value corresponding to the number of orders and the weight value corresponding to the order score of each target user are obtained, the service score of each target user, that is, the value of the third feature information, is obtained by calculating the number of orders, the order score, the weight value corresponding to the number of orders and the weight value corresponding to the order score.

And S104, pairing the target users based on the sequencing result.

In a specific implementation, after ordering is completed for each group of target users, pairing is performed for each group of target users based on the ordering result of the group. For example, pairing is performed in pairs in order, or pairing is performed in pairs in odd order, and pairing is performed in pairs in even order, as shown in fig. 5, and specifically includes the following steps:

s501, pairing target users in each subgroup based on the value of the third characteristic information;

S502, when unpaired target users exist in each subgroup, a new subgroup is built based on the unpaired target users in all subgroups, and the subgroups are reordered and paired based on online time length.

And pairing the target users in the group pairwise according to the value of the third characteristic information of each target user from high to low. That is, the target user with the first value rank of the third feature information is paired with the target user with the second value rank of the third feature information, the target user with the third value rank of the third feature information is paired with the target user with the fourth value rank of the third feature information, and the like, so that pairwise pairing of all the target users in each group is completed.

In practical applications, there are cases where the number of target users in the group is singular, that is, there are cases where the pairing of target users in the group whose value of the third characteristic information is the lowest fails. If only one group exists in all the groups and unpaired target users exist in the groups, prompting that the target users fail to participate; if unpaired target users exist in the multiple subgroups, pairing all unpaired target users pairwise according to the online time length of the unpaired target users in a preset time period from long to short.

Considering that the time interval between the expected online time durations of the unpaired target users is larger, the unpaired target users are paired in pairs from long to short according to the expected online time durations, and the waste of server resources and social resources is avoided.

If the unpaired target users which are failed to pair again still exist after pairing is completed for the unpaired target users, prompting that the unpaired target users are failed to participate in the match.

In summary, in the embodiment of the present disclosure, the historical online data, the first feature information and the second feature information of each target user are utilized to predict the online time of each target user in a preset time period, all the target users are grouped based on the online time of each target user, and the target users in each group are ordered; and then, based on the sorting result of each group, pairing the target users of the group, so that the problem that when part of target users cannot participate in a game on time or cannot complete the whole game course, the target users paired with the target users in emergency cannot participate in the game or participate in the game in invalidation is avoided to a certain extent, namely, the waste of server resources (namely, the server performs pairing operation) and social resources (the target users capable of participating in the game on time and completing the whole game course) is avoided, and meanwhile, the experience degree and enthusiasm of the target users capable of participating in the game on time and completing the whole game course can be ensured.

In a specific implementation, the historical online data, the first feature information and the second feature information are input into a trained prediction model, when the online time of each target user in a preset time period is obtained, the time corresponding to the first preset time interval and the time corresponding to the preset time period can be set to be the same when the prediction model is trained, so that the historical online data, the first feature information and the second feature information of each target user are obtained, and the online time of each target user in the preset time period is predicted based on the historical online data, the first feature information and the second feature information, namely, only one round of prediction calculation is needed.

Of course, the duration corresponding to the first preset time interval and the duration corresponding to the preset time period may also be set to be different, and in consideration that the duration corresponding to the first preset time interval is smaller than the duration corresponding to the preset time period, an integer multiple of the duration corresponding to the first preset time interval may be set to be the same as the duration corresponding to the preset time period.

According to the method and the device for matching the online time of the road network traffic, the historical online data, the first characteristic information and the second characteristic information of each target user are utilized to predict the online time of each target user in a preset time period, matching is conducted according to the online time of each target user, the problem that when part of target users cannot participate in a match on time or cannot complete the whole match course, the target users matched with the target users in emergency cannot participate in the match or participate in the match, and the problem that the match is invalid is solved, waste of server resources and social resources can be avoided, and the resource utilization rate of road network traffic and the operation efficiency of network about vehicles are further improved.

Based on the same inventive concept, the embodiments of the present disclosure also provide an information processing apparatus corresponding to an information processing method, and as shown in fig. 6, a second aspect of the present disclosure provides an information processing apparatus including: the first acquisition module 10, the second acquisition module 20, the sorting module 30 and the pairing module 40, which are coupled to each other, wherein:

The first obtaining module 10 is configured to obtain historical online data of each target user, first feature information and second feature information, where the first feature information includes at least time feature information and weather feature information, and the second feature information includes at least user feature information.

The second obtaining module 20 is configured to input the historical online data, the first feature information and the second feature information into a trained prediction model, and obtain an online time length of each target user in a preset time period.

To ensure accuracy of the predictive model, the apparatus of the present disclosure further includes a training module 50 that utilizes the training module 50 to determine the predictive model, and in particular, utilizes historical work data of the historical user to train the predictive model to be trained. Specifically, after the historical users are determined, multiple groups of training samples are screened from all the historical work data of each historical user, wherein each group of training samples comprises a first historical online time length corresponding to each historical user at a first time point, first characteristic information of the historical user, second characteristic information of the historical user and a real second historical online time length at a second time point. Specifically, in the training process, based on a first historical online time length and first characteristic information of a historical user, acquiring a temporary historical online time length of a second time point which is a first preset time interval after a first time point, correcting the temporary historical online time length through second characteristic information, determining a second historical online time length of the historical user at the second time point, comparing the second historical online time length determined by a prediction model with a real second historical online time length to obtain an error value of a current round of prediction model, and repeating the current round of training until the error value is smaller than or equal to a preset threshold value if the error value is larger than the preset threshold value, and obtaining the prediction model after the training is finished.

After the trained prediction model is obtained, the prediction model is used for predicting the online time of each target user in a preset time period, and the method is convenient and quick, and has high accuracy and efficiency.

In one embodiment, the second acquiring module 20 includes:

A first acquisition unit configured to acquire an adjacent online time length of an adjacent time point that is separated by a second preset time interval before a start time point of the preset time period.

A first determining unit configured to determine at least one consecutive time point at the second preset time interval within the preset time period from the start time point.

And the second acquisition unit is used for acquiring the unit online time length of the first time point through the prediction model based on the adjacent online time length.

And the third acquisition unit is used for sequentially acquiring the unit on-line duration of each time point after the first time point according to time sequence.

For example, the time of the game is 8 to 11 pm on sunday, that is, the preset time period is 3 hours, and the duration corresponding to the preset time period is set to be 3 times the duration corresponding to the second preset time interval, that is, each second preset time interval is 1 hour. The successive time points determined from the start time point 8 are 9, 10 and 11 points, respectively. For each target user, predicting the unit online time length of the target user at 9 points based on the historical online data of 8, the first characteristic information, the second characteristic information and the first adjacent online time length (namely the online time length of 7-point to 8-point target users) according to the time sequence; then, based on the 10-point historical online data, the first characteristic information, the second characteristic information and the second adjacent online time length (namely, the predicted online time length of the target user in 8-9 points), predicting the unit online time length of the target user in 10 points; then, based on the historical online data of 11 points, the first characteristic information, the second characteristic information and the third adjacent online time length (namely, the predicted online time length of the target user within 9 to 10 points), the unit online time length of the target user at 11 points is predicted. And finally, carrying out summation calculation on the unit on-line time length of the target user at 9 points, the unit on-line time length of the target user at 10 points and the unit on-line time length of the target user at 11 points, wherein the obtained summation value is the on-line time length of the target user at 8 points to 11 points (namely a preset time period) at night.

A ranking module 30 for grouping the target users based on the online time period and ranking the target users in each group.

Specifically, the sorting module 30 includes:

And a second determining unit for determining at least one online time period grouping reference value.

And a grouping unit for grouping the online time lengths of all the target users into a plurality of subgroups based on the online time length grouping reference value.

After grouping all the target users is completed, the sorting unit 303 sorts the target users based on the third characteristic information for each group of target users. Specifically, for each group, third characteristic information of each target user in the group is acquired, wherein the third characteristic information comprises the number of orders, order scores and the like of each target user in a certain time period, and corresponding weight values are set for the number of orders, the order scores and the like.

In the embodiment of the disclosure, the sorting unit takes the example that the service data includes the order number and the order score as detailed description, and calculates the service score of each target user, that is, the value of the third feature information, by using the order number, the order score, the weight value corresponding to the order number and the weight value corresponding to the order score after obtaining the order number, the order score, the weight value corresponding to the order number and the weight value corresponding to the order score of each target user.

And the pairing module 40 is used for pairing the target users based on the sequencing result.

In a specific implementation, after ordering is completed for each group of target users, pairing is performed for each group of target users based on the ordering result of the group. For example, pairing is performed in pairs in order, or pairing is performed in pairs in odd order, and pairing is performed in pairs in even order.

Specifically, the pairing module 40 includes:

and a pairing unit configured to pair the target users based on the value of the third characteristic information in each of the subgroups.

The third aspect of the present disclosure also provides a storage medium, which is a computer readable medium storing a computer program, which when executed by a processor implements the method provided by any embodiment of the present disclosure, comprising the steps of:

s11, historical online data, first characteristic information and second characteristic information of each target user are obtained, wherein the first characteristic information at least comprises time characteristic information and weather characteristic information, and the second characteristic information at least comprises user characteristic information;

S12, inputting the historical online data, the first characteristic information and the second characteristic information into a trained prediction model, and obtaining the online time of each target user in a preset time period;

S13, grouping the target users based on the online time length, and sequencing the target users in each group;

And S14, pairing the target users based on the sequencing result.

The computer program is executed by the processor to input the historical online data, the first characteristic information and the second characteristic information into a trained prediction model, and the processor further executes the following steps before acquiring the online time of each target user in a preset time period: acquiring a first historical online time length of each historical user at a first time point; acquiring a temporary historical online time length of a second time point which is a first preset time interval after the first time point based on the first historical online time length and the first characteristic information; and correcting the temporary historical online time length through the second characteristic information, and determining a second historical online time length of the historical user at the second time point.

The computer program is executed by the processor to input the historical online data, the first characteristic information and the second characteristic information into a trained prediction model, and when the online time of each target user in a preset time period is obtained, the processor specifically executes the following steps: acquiring adjacent online time lengths of adjacent time points which are separated from each other by a second preset time interval before a starting time point of a preset time period; determining at least one continuous time point within the preset time period according to the second preset time interval after the starting time point; acquiring the unit online time length of a first time point through the prediction model based on the adjacent online time length; sequentially acquiring the unit on-line duration of each time point after the first time point according to a time sequence; summing the online time durations of all the units at the time points in the preset time period, and obtaining the online time duration of each target user in the preset time period.

The computer program is executed by the processor to group the target users based on the online time length, and when the target users are ordered in each group, the following steps are executed by the processor specifically: determining at least one online time period grouping reference value; dividing the online time lengths of all the target users into a plurality of subgroups based on the online time length grouping reference value; in each of the subgroups, the target users are ranked based on third characteristic information.

The computer program is executed by the processor, based on the sorting result, and when pairing is performed in the target user, the following steps are executed by the processor specifically: pairing the target users in each of the subgroups based on the values of the third characteristic information; when unpaired target users exist in each of the subgroups, a new subgroup is constructed based on the unpaired target users in all of the subgroups, and reordered and paired based on the online time period.

The fourth aspect of the present disclosure further provides an electronic device, which may be shown in fig. 7, and at least includes a memory 701 and a processor 702, where the memory 701 stores a computer program, and the processor 702 implements a method provided by any embodiment of the present disclosure when executing the computer program on the memory 701. Exemplary, the electronic device computer program steps are as follows:

s21, historical online data, first characteristic information and second characteristic information of each target user are obtained, wherein the first characteristic information at least comprises time characteristic information and weather characteristic information, and the second characteristic information at least comprises user characteristic information;

s22, inputting the historical online data, the first characteristic information and the second characteristic information into a trained prediction model, and obtaining the online time of each target user in a preset time period;

S23, grouping the target users based on the online time length, and sequencing the target users in each group;

And S24, pairing the target users based on the sequencing result.

The processor further executes the following computer program before executing the input of the historical online data, the first characteristic information and the second characteristic information stored in the memory into the trained prediction model to acquire the online time of each target user in a preset time period: acquiring a first historical online time length of each historical user at a first time point; acquiring a temporary historical online time length of a second time point which is a first preset time interval after the first time point based on the first historical online time length and the first characteristic information; and correcting the temporary historical online time length through the second characteristic information, and determining a second historical online time length of the historical user at the second time point.

The processor further executes the following computer program when executing the online time length of each target user in a preset time period, wherein the online time length is stored in the memory and the historical online data, the first characteristic information and the second characteristic information are input into a trained prediction model: acquiring adjacent online time lengths of adjacent time points which are separated from each other by a second preset time interval before a starting time point of a preset time period; determining at least one continuous time point within the preset time period according to the second preset time interval after the starting time point; acquiring the unit online time length of a first time point through the prediction model based on the adjacent online time length; sequentially acquiring the unit on-line duration of each time point after the first time point according to a time sequence; summing the online time durations of all the units at the time points in the preset time period, and obtaining the online time duration of each target user in the preset time period.

The processor, when executing the grouping of the target users based on the online time period stored on the memory and ordering the target users in each group, further executes the computer program of: determining at least one online time period grouping reference value; dividing the online time lengths of all the target users into a plurality of subgroups based on the online time length grouping reference value; in each of the subgroups, the target users are ranked based on third characteristic information.

The processor, when executing the pairing among the target users based on the ranking results stored on the memory, further executes a computer program of: pairing the target users in each of the subgroups based on the values of the third characteristic information; when unpaired target users exist in each of the subgroups, a new subgroup is constructed based on the unpaired target users in all of the subgroups, and reordered and paired based on the online time period.

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

The storage medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects an internet protocol address from the at least two internet protocol addresses and returns the internet protocol address; receiving an Internet protocol address returned by node evaluation equipment; wherein the acquired internet protocol address indicates an edge node in the content distribution network.

Or the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

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

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

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

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

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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

Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

While various embodiments of the present disclosure have been described in detail, the present disclosure is not limited to these specific embodiments, and various modifications and embodiments can be made by those skilled in the art on the basis of the concepts of the present disclosure, and these modifications and modifications should be within the scope of the present disclosure as claimed.

Claims

1. An information processing method, characterized by comprising:

Inputting the historical online data, the first characteristic information and the second characteristic information into a trained prediction model, and obtaining the online time of each target user in a preset time period; wherein the predictive model is trained by:

Correcting the temporary historical online time length through the second characteristic information, and determining a second historical online time length of the historical user at the second time point;

And pairing the target users based on the sequencing result.

2. The information processing method according to claim 1, wherein the inputting the historical online data, the first feature information, and the second feature information into a trained predictive model, the obtaining an online time of each target user within a preset period of time includes:

3. The information processing method according to claim 1, wherein the grouping the target users based on the online time period, and ordering the target users in each group comprises:

Determining at least one online time period grouping reference value;

in each of the subgroups, the target users are ranked based on third characteristic information including the number of orders and order scores for each target user over a period of time.

4. The information processing method according to claim 3, wherein the pairing in the target user based on the ranking result includes:

5. An information processing apparatus, characterized by comprising:

The training module is used for acquiring a first historical online time length of each historical user at a first time point; acquiring a temporary historical online time length of a second time point which is a first preset time interval after the first time point based on the first historical online time length and the first characteristic information; correcting the temporary historical online time length through the second characteristic information, and determining a second historical online time length of the historical user at the second time point;

6. The information processing apparatus according to claim 5, wherein the second acquisition module includes:

7. The information processing apparatus of claim 5, wherein the ranking module comprises:

and a ranking unit for ranking the target users in each of the subgroups based on third characteristic information including the number of orders and order scores of each target user over a period of time.

8. The information processing apparatus according to claim 7, wherein the pairing module includes:

9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the information processing method according to any one of claims 1 to 4.

10. An electronic device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating over the bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of the information processing method according to any of claims 1 to 4.