CN113965535A - Message push optimization method, device, equipment and readable storage medium - Google Patents
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- CN113965535A CN113965535A CN202111225794.8A CN202111225794A CN113965535A CN 113965535 A CN113965535 A CN 113965535A CN 202111225794 A CN202111225794 A CN 202111225794A CN 113965535 A CN113965535 A CN 113965535A
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/02—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
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Abstract
The application discloses a message pushing optimization method, a device, equipment and a readable storage medium, wherein the message pushing optimization method comprises the following steps: acquiring a message to be pushed corresponding to a target user terminal and a corresponding estimated information feedback probability, and transmitting the message to be pushed and the estimated information feedback probability to a Chatbot service; acquiring the accumulated estimated information feedback quantity of each preset Chatbot corresponding to the Chatbot service; selecting a target Chatbot for the message to be pushed in each preset Chatbot through the Chatbot service according to each accumulated estimated information feedback quantity; updating and accumulating the feedback quantity of the estimated information through a Chatbot service according to the feedback quantity of the estimated information corresponding to the feedback probability of the estimated information, and pushing a message to be pushed to a target user terminal; and if a user session request of the target user terminal for the message to be pushed is received, receiving feedback information corresponding to the user session request through the target Chatbot. The message pushing method and the message pushing device solve the technical problem of low message pushing efficiency.
Description
Technical Field
The present application relates to the field of communications technologies of financial technologies (Fintech), and in particular, to a method, an apparatus, a device, and a readable storage medium for message push optimization.
Background
With the continuous development of financial science and technology, especially internet science and technology, more and more technologies (such as distributed technology, artificial intelligence and the like) are applied to the financial field, but the financial industry also puts higher requirements on the technologies, for example, higher requirements on the distribution of backlog in the financial industry are also put forward.
With the coming of the 5G era, the richness of the message recommendation content is required to be higher and higher, at present, a message to be pushed can provide richer recommendation content for a user, and when the message to be pushed is pushed, the message to be pushed can be pushed to a user terminal through a Chatbot (chat robot) service.
Disclosure of Invention
The present application mainly aims to provide a method, an apparatus, a device and a readable storage medium for optimizing message pushing, and aims to solve the technical problem of low message pushing efficiency in the prior art.
In order to achieve the above object, the present application provides a message pushing optimization method, where the message pushing optimization method is applied to a message pushing optimization device, and the message pushing optimization method includes:
acquiring a message to be pushed corresponding to a target user terminal and a corresponding estimated information feedback probability, and transmitting the message to be pushed and the estimated information feedback probability to a Chatbot service;
acquiring the accumulated estimated information feedback quantity of each preset Chatbot corresponding to the Chatbot service;
selecting a target Chatbot for the message to be pushed in each preset Chatbot through the Chatbot service according to each accumulated estimated information feedback quantity;
updating the accumulated estimated information feedback quantity through the Chatbot service according to the estimated information feedback quantity corresponding to the estimated information feedback probability, and pushing the message to be pushed to the target user terminal;
and if a user session request of the target user terminal for the message to be pushed is received, receiving feedback information corresponding to the user session request through the target Chatbot.
Optionally, before the step of obtaining the accumulated estimated information feedback quantity of each preset Chatbot corresponding to the Chatbot service, the message pushing optimization method further includes:
acquiring information feedback probability corresponding to each un-fed user in a preset time period of the preset Chatbot;
and calculating the accumulated estimated information feedback quantity according to each information feedback probability and the preset single information feedback quantity.
Optionally, the step of obtaining the information feedback probability corresponding to each unrevealed user in a preset time period by the preset Chatbot includes:
acquiring a downlink user ID set and an uplink user ID set of the preset Chatbot;
determining each unreeeded user ID according to the downlink user ID set and the uplink user ID set;
and inquiring the information feedback probability according to the ID of each unrevealed user.
Optionally, the step of selecting a target Chatbot for the message to be pushed in each preset Chatbot through the Chatbot service according to each accumulated estimated information feedback amount includes:
selecting each idle Chatbot with a thread idle rate larger than a preset idle rate threshold value from each preset Chatbot;
and selecting the Chatbot with the minimum accumulated estimated information feedback quantity from the idle chatbots as the target Chatbot.
Optionally, after the step of pushing the message to be pushed to the target user terminal, the message pushing optimization method further includes:
acquiring a user ID corresponding to the target user terminal and a Chatbot ID corresponding to the target Chatbot;
and performing association cache on the user ID and the Chatbot ID to obtain an association cache result.
Optionally, the step of receiving, by the target Chatbot, feedback information corresponding to the user session request if the user session request of the target user terminal for the message to be pushed is received includes:
if the user session request is received, acquiring a feedback user ID corresponding to the user session request;
searching a target Chatbot corresponding to the feedback user ID in each preset Chatbot according to the feedback user ID and the correlation cache result;
and receiving feedback information corresponding to the user session request through the target Chatbot.
Optionally, the target Chatbot at least includes a preset thread, and the step of receiving feedback information corresponding to the user session request through the target Chatbot includes:
acquiring an uplink timestamp corresponding to the user session request and a downlink historical timestamp corresponding to the feedback user ID;
and if the uplink timestamp and the downlink historical timestamp are in a preset session period, receiving feedback information corresponding to the user session request according to a preset thread corresponding to the downlink historical timestamp.
The present application further provides a message pushing optimization device, the message pushing optimization device is a virtual device, and the message pushing optimization device is applied to a message pushing optimization device, the message pushing optimization device includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a message to be pushed corresponding to a target user terminal and a corresponding pre-estimated information feedback probability and transmitting the message to be pushed and the pre-estimated information feedback probability to a Chatbot service;
a second obtaining module, configured to obtain an accumulated estimated information feedback amount of each preset Chatbot corresponding to the Chatbot service;
a Chatbot selecting module, configured to select a target Chatbot for the message to be pushed in each preset Chatbot through the Chatbot service according to each accumulated estimated information feedback amount;
the message pushing module is used for updating the accumulated estimated information feedback quantity through the Chatbot service according to the estimated information feedback quantity corresponding to the estimated information feedback probability and pushing the message to be pushed to the target user terminal;
and the feedback receiving module is used for receiving feedback information corresponding to the user session request through the target Chatbot if the user session request of the target user terminal for the message to be pushed is received.
Optionally, the message pushing optimization device is configured to:
acquiring information feedback probability corresponding to each un-fed user in a preset time period of the preset Chatbot;
and calculating the accumulated estimated information feedback quantity according to each information feedback probability and the preset single information feedback quantity.
Optionally, the message pushing optimization device is configured to:
acquiring a downlink user ID set and an uplink user ID set of the preset Chatbot;
determining each unreeeded user ID according to the downlink user ID set and the uplink user ID set;
and inquiring the information feedback probability according to the ID of each unrevealed user.
Optionally, the message pushing optimization device is configured to:
selecting each idle Chatbot with a thread idle rate larger than a preset idle rate threshold value from each preset Chatbot;
and selecting the Chatbot with the minimum accumulated estimated information feedback quantity from the idle chatbots as the target Chatbot.
Optionally, the message pushing optimization device is configured to:
acquiring a user ID corresponding to the target user terminal and a Chatbot ID corresponding to the target Chatbot;
and performing association cache on the user ID and the Chatbot ID to obtain an association cache result.
Optionally, the feedback receiving module is further configured to:
if the user session request is received, acquiring a feedback user ID corresponding to the user session request;
searching a target Chatbot corresponding to the feedback user ID in each preset Chatbot according to the feedback user ID and the correlation cache result;
and receiving feedback information corresponding to the user session request through the target Chatbot.
Optionally, the target Chatbot includes at least a preset thread, and the feedback receiving module is further configured to:
acquiring an uplink timestamp corresponding to the user session request and a downlink historical timestamp corresponding to the feedback user ID;
and if the uplink timestamp and the downlink historical timestamp are in a preset session period, receiving feedback information corresponding to the user session request according to a preset thread corresponding to the downlink historical timestamp.
The present application further provides a message pushing optimization device, where the message pushing optimization device is an entity device, and the message pushing optimization device includes: a memory, a processor and a program of the message push optimization method stored on the memory and executable on the processor, which program, when executed by the processor, may implement the steps of the message push optimization method as described above.
The present application also provides a readable storage medium having stored thereon a program for implementing a message push optimization method, which when executed by a processor, implements the steps of the message push optimization method as described above.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the message push optimization method as described above.
The application provides a message pushing optimization method, a device, equipment, a readable storage medium and a computer program product, and particularly, the application acquires a message to be pushed corresponding to a target user terminal and a corresponding estimated information feedback probability, transmits the message to be pushed and the estimated information feedback probability to a Chatbot service, acquires an accumulated estimated information feedback quantity of each preset Chatbot corresponding to the Chatbot service, selects a target Chatbot for the message to be pushed in each preset Chatbot service according to each accumulated estimated information feedback quantity, updates the accumulated estimated information feedback quantity through the Chatbot service according to the estimated information feedback quantity corresponding to the estimated information feedback probability, pushes the message to be pushed to the target user terminal, and if a user session request of the target user terminal to the message to be pushed is received, the feedback information corresponding to the user session request is received by the target Chatbot, so that the purpose of processing the downlink data (to-be-pushed message) and the uplink data (feedback information) belonging to the same session through the same Chatbot is realized, the probability of data uplink error and data chaos can be reduced, each preset Chatbot maintains an accumulated estimated information feedback quantity in real time, the accumulated estimated information feedback quantity is updated in real time along with the pushing of the message, and the information feedback quantity to be received by each preset Chatbot can be estimated in real time according to the probability of successful pushing of the message, so that the workload can be distributed for each Chatbot in a balanced manner under the scenes of multiple chatbots and multiple sessions, each preset Chatbot receives the uplink data in order under the scenes of multiple chatbots and multiple sessions, and the condition that data chaos or data cross uplink is caused by overload is prevented, the probability of data uplink errors and data confusion can be reduced, so that the technical defect that the message pushing efficiency is affected due to the fact that a plurality of Chatbot receive uplink message data of a large number of users simultaneously and parallelly under the scene of a plurality of chatbots and a plurality of sessions and the situation of data confusion and data cross uplink errors is overcome, and the message pushing efficiency is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a first embodiment of a message push optimization method according to the present application;
fig. 2 is a system architecture diagram of message pushing in the message pushing optimization method of the present application;
fig. 3 is a flowchart illustrating a second embodiment of the message push optimization method according to the present application;
fig. 4 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
The objectives, features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
First, it should be understood that in a scenario with multiple Chatbot and multiple sessions, a situation with data crossing uplink often occurs, that is, data in the same session of the same user terminal goes uplink through multiple chatbots, and thus uplink data received by the product recommendation system is very confused, in the embodiment of the present application, a Chatbot ID may be bound with a user ID to reduce the probability of data crossing uplink, but since not every user feeds back a message, there is a possibility that some chatbots receive more feedback information and some chatbots need to receive less feedback information, and in an extreme case, it is easy to cause overload of part of chatbots, for example, a large number of users send uplink data to the Chatbot at the same time, and at this time, other chatbots need to call feedback information of the users, so that there is a situation with data crossing uplink also, thereby affecting the efficiency of message pushing.
In a first embodiment of the message push optimization method of the present application, referring to fig. 1, the message push optimization method includes:
step S10, obtaining a message to be pushed corresponding to a target user terminal and a corresponding pre-estimated information feedback probability, and transmitting the message to be pushed and the pre-estimated information feedback probability to a Chatbot service;
step S20, obtaining the accumulated estimated information feedback quantity of each preset Chatbot corresponding to the Chatbot service;
step S30, selecting a target Chatbot for the message to be pushed in each preset Chatbot through the Chatbot service according to each accumulated estimated information feedback quantity;
step S40, updating the accumulated estimated information feedback quantity through the Chatbot service according to the estimated information feedback quantity corresponding to the estimated information feedback probability, and pushing the message to be pushed to the target user terminal;
step S50, if a user session request from the target user terminal to the message to be pushed is received, receiving feedback information corresponding to the user session request through the target Chatbot.
The embodiment of the present application provides a message push optimization method under a multi-Chatbot multi-session scenario, specifically, when sending downlink data (message to be pushed) to a target user terminal through the same target Chatbot and receiving uplink data (feedback information) of the target user terminal from the target Chatbot, selecting a target Chatbot for the message to be pushed according to the accumulated estimated information feedback quantity of each preset Chatbot corresponding to the Chatbot service, so that each preset Chatbot is balanced on the accumulated estimated information feedback quantity as much as possible, wherein the accumulated estimated information feedback quantity is an information quantity to be received by the preset Chatbot calculated according to the estimated information feedback probability, and can reduce the probability that some chatbots receive too much feedback information and some chatbots need to receive too little feedback information, thereby reducing the probability of data cross uplink and data chaos, and the accumulated estimated information feedback quantity is updated in real time according to the preset information feedback probability of the message to be pushed, the method ensures the real-time effectiveness of the accumulated estimated information feedback quantity, further ensures the accuracy of matching the target Chatbot according to the information to be pushed of each accumulated estimated information feedback quantity, further reduces the probability of the situation that some chatbots receive too much feedback information and some chatbots need too little feedback information, and further reduces the probability of data cross uplink and data chaos, so as to overcome the technical defect that the situation that a plurality of chatbots simultaneously receive a large amount of user uplink message data in parallel under the scene of a plurality of chatbots and a plurality of conversations, the situation that the data chaos and the data cross uplink are easy to make mistakes, the message pushing efficiency is influenced, and the message pushing efficiency is improved.
In this embodiment, it should be noted that the message to be pushed is generated by a recommendation system, and the message to be pushed may be contents such as text, video, and picture, and in addition, the message to be pushed may further include a tag of the target user terminal, which is used to identify the identity of the target user terminal, and may be an identity card number, a mobile phone number, or an equipment ID of the user. The estimated information feedback probability is the estimated probability that the target user terminal generates feedback information on the message to be pushed, and the feedback information may be user behavior information, for example, if the message to be pushed is a car credit message, the user behavior information may be whether the user clicks that the user wants to handle, or the user reads the message to be pushed. The accumulated estimated information feedback quantity is a data quantity of feedback information which is estimated to be received by a corresponding preset Chatbot, for example, assuming that a session period is 30 minutes, each message to be fed back and without the feedback information can be determined, and an estimated information feedback probability (p1, p 2., pn) and a preset single feedback data quantity size M corresponding to each message to be fed back and to be fed back are obtained, the accumulated estimated information feedback quantity is (p1+ p2+ ·+ pn) M, and if no feedback information is obtained in more than 30 minutes, it is determined that the user cannot feed back information any more, that is, the preset Chatbot receives uplink data with the size of (p1+ p2+ ·+ pn) M at least in a next session period from the current time. The method specifically comprises the following steps: the method comprises the steps that a product marketing request is received through a recommendation system, a target user terminal is selected according to the product marketing request, a message to be pushed corresponding to the target user terminal and a corresponding pre-estimated information feedback probability are generated, and the message to be pushed and the pre-estimated information feedback probability are sent to Chatbot service through the recommendation system. And inquiring the accumulated estimated information feedback quantity corresponding to each preset Chatbot according to the Chatbot ID of each preset Chatbot corresponding to each Chatbot service. And selecting the minimum accumulated estimated information feedback quantity from the accumulated estimated information feedback quantities through the Chatbot service, and taking the preset Chatbot corresponding to the minimum accumulated estimated information feedback quantity as the target Chatbot. And calculating the estimated information feedback quantity corresponding to the message to be pushed according to the estimated information feedback probability and the preset single feedback data quantity, further accumulating the estimated information feedback quantity to the accumulated estimated information feedback quantity corresponding to the target Chatbot through the Chatbot service, updating the accumulated estimated information feedback quantity, and pushing the message to be pushed to the target user terminal through the target Chatbot. And if a user session request of the target user terminal for the message to be pushed is received, positioning the target Chatbot in each preset Chatbot according to the user session request, and receiving feedback information corresponding to the user session request through the target Chatbot. Further, the feedback information can be transmitted back to the recommendation system through the target Chatbot, so that the recommendation system can update a product recommendation strategy, wherein the product recommendation strategy can be a product recommendation model, and is used for predicting the feedback probability of the user on the push message and generating a corresponding estimated information feedback probability.
Wherein, the step of pushing the information to be pushed to the target user terminal through the target Chatbot comprises:
and issuing the message to be pushed and the label of the target user terminal to an operator system through the target Chatbot, so that the operator system can send the message to be pushed to the target user terminal according to the label of the target user terminal.
In a practical manner, before step S20, the message push optimization method further includes:
step A10, obtaining information feedback probability corresponding to each un-fed user in a preset time period of the preset Chatbot;
in this embodiment, it should be noted that the preset time period may be set to a session period, the unrevealed user is a user who does not perform information feedback on the push information in the session period, the preset Chatbot records the user ID of each feedback information user to form an unrevealed user ID set, and when the user does not feedback information or does not feedback information during a session period, the user ID of the user is removed from the unrevealed user ID set, and when the preset Chatbot receives a new message to be pushed, the user ID corresponding to the new message to be pushed is stored in the unrevealed user ID set.
Specifically, each unrevealed user ID is extracted from the unrevealed user ID set, and the information feedback probability corresponding to each unrevealed user is inquired according to each unrevealed user ID.
Step a20, calculating the accumulated estimated information feedback amount according to each information feedback probability and a preset single information feedback amount.
In this embodiment, specifically, the product between each of the information feedback probabilities and the preset single feedback amount is calculated and summed to obtain the accumulated estimated information feedback amount. The preset single feedback quantity can be set as the average value of the data quantity of all historical feedback information and is updated regularly. And the accumulated estimated information feedback quantity is updated in real time in the message pushing process. And further, the purpose of predicting the information quantity at least to be received by the preset Chatbot in a session period is realized.
Wherein, step a10 further includes:
step A11, acquiring a downlink user ID set and an uplink user ID set of the preset Chatbot;
step A12, determining each unrevealed user ID according to the downlink user ID set and the uplink user ID set;
in this embodiment, it should be noted that the preset Chatbot maintains a downlink user ID set and an uplink user ID set, where the downlink user ID set is an ID set of a push target user when a message is pushed to the push target user, and the uplink user ID set is an ID set of a user who feeds back information when receiving feedback information of the user. And if the user ID exists in the uplink user ID set, deleting the user ID together.
Specifically, a downlink user ID set and an uplink user ID set of the preset Chatbot are obtained, and a difference set between the downlink user ID set and the uplink user ID set is calculated, that is, each user ID existing in the downlink user ID set but not existing in the uplink user ID set is obtained as an unrefed user ID.
Step a13, inquiring each information feedback probability according to each unrevealed user ID.
In this embodiment, specifically, each unrevealed user ID is used as an index to query a corresponding information feedback probability.
Wherein, step S30 further includes:
step S31, selecting each idle Chatbot with a thread idle rate greater than a preset idle rate threshold value from each preset Chatbot;
step S32, selecting the Chatbot with the smallest accumulated estimated information feedback quantity from the idle chatbots as the target Chatbot.
In this embodiment, when it needs to be described, according to different services, a plurality of processes can be deployed in a Chatbot service, where a preset Chatbot can be set to at least correspond to one process, and one process at least includes one thread for performing message pushing or feedback information receiving. The thread idle rate is the duty ratio of idle threads in each thread corresponding to the preset Chatbot, for example, if the preset Chatbot corresponds to 100 idle threads and 50 threads are in an idle state, the thread idle rate is 50%.
Specifically, a thread idle rate corresponding to each preset Chatbot is calculated, and then each idle Chatbot with the thread idle rate being greater than a preset idle rate threshold is selected from each preset Chatbot by comparing each thread idle rate with the preset idle rate threshold, and then the Chatbot with the smallest accumulated estimated information feedback quantity is selected from each idle Chatbot as the target Chatbot, wherein it needs to be stated that the thread idle rate can be used for representing the current load of the preset Chatbot, and the accumulated estimated information feedback quantity can be used for representing the estimated load of the preset Chatbot in a period of time in the future, so that the selection of the preset Chatbot with smaller current load and future load for the message to be pushed is realized, and further the load balance of each Chatbot under a multi-session scene of multiple chatbots can be realized.
In an implementation manner, as shown in fig. 2, a system architecture diagram during message pushing according to an embodiment of the present application is shown, where a message to be pushed is a 5G message, the message to be pushed flows to a user through a Chatbot service, and feedback information flows to the Chatbot service through the user.
The embodiment of the application provides a message pushing optimization method, a message pushing optimization device, a message pushing optimization equipment, a readable storage medium and a computer program product, and particularly, in the embodiment of the application, a message to be pushed corresponding to a target user terminal and a corresponding estimated information feedback probability are firstly obtained, the message to be pushed and the estimated information feedback probability are transmitted to a Chatbot service, accumulated estimated information feedback quantity of each preset Chatbot corresponding to the Chatbot service is obtained, according to each accumulated estimated information feedback quantity, a target Chatbot is selected for the message to be pushed in each preset Chatbot through the Chatbot service, further according to the estimated information feedback quantity corresponding to the estimated information feedback probability, the accumulated estimated information feedback quantity is updated through the Chatbot service, the message to be pushed is pushed to the target user terminal, and if a user session request of the target user terminal for the message to be pushed is received, the feedback information corresponding to the user session request is received by the target Chatbot, so that the purpose of processing the downlink data (to-be-pushed message) and the uplink data (feedback information) belonging to the same session through the same Chatbot is realized, the probability of data uplink error and data chaos can be reduced, each preset Chatbot maintains an accumulated estimated information feedback quantity in real time, the accumulated estimated information feedback quantity is updated in real time along with the pushing of the message, and the information feedback quantity to be received by each preset Chatbot can be estimated in real time according to the probability of successful pushing of the message, so that the workload can be distributed for each Chatbot in a balanced manner under the scenes of multiple chatbots and multiple sessions, each preset Chatbot receives the uplink data in order under the scenes of multiple chatbots and multiple sessions, and the condition that data chaos or data cross uplink is caused by overload is prevented, the probability of data uplink errors and data confusion can be reduced, so that the technical defect that the message pushing efficiency is affected due to the fact that a plurality of Chatbot receive uplink message data of a large number of users simultaneously and parallelly under the scene of a plurality of chatbots and a plurality of sessions and the situation of data confusion and data cross uplink errors is overcome, and the message pushing efficiency is improved.
Further, referring to fig. 3, based on the first embodiment in the present application, in another embodiment of the present application, after step S40, the message push optimization method further includes:
step B10, obtaining the user ID corresponding to the target user terminal and the Chatbot ID corresponding to the target Chatbot;
and step B20, performing associative cache on the user ID and the Chatbot ID, and obtaining an associative cache result.
In this embodiment, it should be noted that each target user terminal has a user ID, where the user ID may be a user mobile phone number or a terminal equipment ID, and each preset Chatbot has a Chatbot ID.
Specifically, a user ID corresponding to the target user terminal and a Chatbot ID corresponding to the target Chatbot are obtained, and further the user ID and the Chatbot ID are correlated through a Chatbot service, and the user ID and the Chatbot ID are cached in a key value pair manner, so that a correlation cache result is obtained. Wherein, the user ID can be set to Key and the Chatbot ID can be set to value.
Further, step S50 further includes:
step S51, if the user session request is received, obtaining a feedback user ID corresponding to the user session request;
step S52, according to the feedback user ID and the correlation cache result, searching a target Chatbot corresponding to the feedback user ID in each preset Chatbot;
step S53, receiving feedback information corresponding to the user session request through the target Chatbot.
In this embodiment, specifically, if the user session request is received, the user ID of the user sending the user session request is obtained, the feedback user ID is obtained, and then, according to the feedback user ID, the corresponding Chatbot ID is queried in the association cache result maintained by each preset Chatbot, so as to obtain the target Chatbot ID, and the Chatbot corresponding to the target Chatbot ID is taken as the target Chatbot. And then the purpose of accurately positioning the corresponding Chatbot for the uplink data is realized, and further the purpose of processing the downlink data and the uplink data of the same session by using the same Chatbot can be realized, so that the promotion effect on preventing data confusion can be realized on the Chatbot level, and meanwhile, the data cross uplink can be avoided.
Wherein, the target Chatbot at least includes a preset thread, and step S53 further includes:
step S531, obtaining an uplink timestamp corresponding to the user session request and a downlink historical timestamp corresponding to the feedback user ID;
step S532, if the uplink timestamp and the downlink historical timestamp are within a preset session period, receiving feedback information corresponding to the user session request according to a preset thread corresponding to the downlink historical timestamp.
In this embodiment, it should be noted that the uplink timestamp is a time point when the message is pushed to the target user terminal through the Chatbot service, and the downlink timestamp is a time point when the feedback message of the target user terminal is received through the Chatbot service.
Specifically, an uplink timestamp corresponding to the user session request and a downlink historical timestamp corresponding to the feedback user ID are obtained, and then if the uplink timestamp and the downlink historical timestamp are within a preset session period, a preset thread used by a message to be pushed corresponding to the downlink historical timestamp is determined, and feedback information corresponding to the user session request is received through the preset thread corresponding to the downlink historical timestamp; and if the uplink timestamp and the downlink historical timestamp are not in a preset session period, not receiving feedback information corresponding to the user session request.
The embodiment of the application provides a method for receiving downlink data in a multi-Chatbot multi-session scene, that is, a user ID corresponding to a target user terminal and a Chatbot ID corresponding to a target Chatbot are first obtained, and then the user ID and the Chatbot ID are subjected to associative cache to obtain an associative cache result. And if the user session request is received, acquiring a feedback user ID corresponding to the user session request, searching a target Chatbot corresponding to the feedback user ID in each preset Chatbot according to the feedback user ID and the associated cache result, and receiving feedback information corresponding to the user session request through the target Chatbot. After receiving the user session request, the target Chatbot corresponding to the user is accurately positioned according to the feedback user ID and the associated cache result, so that the purpose of receiving and sending data by using the same Chatbot for uplink data and downlink data belonging to the same session is realized, and the probability of data chaos and data cross uplink can be reduced.
Referring to fig. 4, fig. 4 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
As shown in fig. 4, the message push optimization device may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used for realizing connection communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory device separate from the processor 1001 described above.
Optionally, the message push optimization device may further include a rectangular user interface, a network interface, a camera, a Radio Frequency (RF) circuit, a sensor, a hard disk circuit, a WiFi module, and the like. The rectangular user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also comprise a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface).
It will be appreciated by a person skilled in the art that the message push optimisation device architecture shown in figure 4 does not constitute a limitation of the message push optimisation device and may comprise more or less components than shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 4, a memory 1005, which is a type of computer storage medium, may include an operating system, a network communication module, and a message push optimization program. The operating system is a program that manages and controls the hardware and software resources of the message push optimization device, supporting the operation of the message push optimization program as well as other software and/or programs. The network communication module is used for communication among the components in the memory 1005 and with other hardware and software in the message push optimization system.
In the message push optimization device shown in fig. 4, the processor 1001 is configured to execute a message push optimization program stored in the memory 1005, and implement the steps of the message push optimization method described in any one of the above.
The specific implementation of the message push optimization device of the present application is substantially the same as the embodiments of the message push optimization method, and is not described herein again.
An embodiment of the present application further provides a message pushing optimization device, where the message pushing optimization device is applied to a message pushing optimization device, and the message pushing optimization device includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a message to be pushed corresponding to a target user terminal and a corresponding pre-estimated information feedback probability and transmitting the message to be pushed and the pre-estimated information feedback probability to a Chatbot service;
a second obtaining module, configured to obtain an accumulated estimated information feedback amount of each preset Chatbot corresponding to the Chatbot service;
a Chatbot selecting module, configured to select a target Chatbot for the message to be pushed in each preset Chatbot through the Chatbot service according to each accumulated estimated information feedback amount;
the message pushing module is used for updating the accumulated estimated information feedback quantity through the Chatbot service according to the estimated information feedback quantity corresponding to the estimated information feedback probability and pushing the message to be pushed to the target user terminal;
and the feedback receiving module is used for receiving feedback information corresponding to the user session request through the target Chatbot if the user session request of the target user terminal for the message to be pushed is received.
Optionally, the message pushing optimization device is configured to:
acquiring information feedback probability corresponding to each un-fed user in a preset time period of the preset Chatbot;
and calculating the accumulated estimated information feedback quantity according to each information feedback probability and the preset single information feedback quantity.
Optionally, the message pushing optimization device is configured to:
acquiring a downlink user ID set and an uplink user ID set of the preset Chatbot;
determining each unreeeded user ID according to the downlink user ID set and the uplink user ID set;
and inquiring the information feedback probability according to the ID of each unrevealed user.
Optionally, the message pushing optimization device is configured to:
selecting each idle Chatbot with a thread idle rate larger than a preset idle rate threshold value from each preset Chatbot;
and selecting the Chatbot with the minimum accumulated estimated information feedback quantity from the idle chatbots as the target Chatbot.
Optionally, the message pushing optimization device is configured to:
acquiring a user ID corresponding to the target user terminal and a Chatbot ID corresponding to the target Chatbot;
and performing association cache on the user ID and the Chatbot ID to obtain an association cache result.
Optionally, the feedback receiving module is further configured to:
if the user session request is received, acquiring a feedback user ID corresponding to the user session request;
searching a target Chatbot corresponding to the feedback user ID in each preset Chatbot according to the feedback user ID and the correlation cache result;
and receiving feedback information corresponding to the user session request through the target Chatbot.
Optionally, the target Chatbot includes at least a preset thread, and the feedback receiving module is further configured to:
acquiring an uplink timestamp corresponding to the user session request and a downlink historical timestamp corresponding to the feedback user ID;
and if the uplink timestamp and the downlink historical timestamp are in a preset session period, receiving feedback information corresponding to the user session request according to a preset thread corresponding to the downlink historical timestamp.
The specific implementation of the message pushing optimization device of the present application is substantially the same as that of each embodiment of the message pushing optimization method, and is not described herein again.
The present application provides a readable storage medium, and the readable storage medium stores one or more programs, which can be further executed by one or more processors for implementing the steps of the message push optimization method described in any one of the above.
The specific implementation manner of the readable storage medium of the present application is substantially the same as that of each embodiment of the message push optimization method, and is not described herein again.
The present application provides a computer program product, and the computer program product includes one or more computer programs, which can also be executed by one or more processors for implementing the steps of the message push optimization method described in any one of the above.
The specific implementation of the computer program product of the present application is substantially the same as the embodiments of the message push optimization method, and is not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A message push optimization method, wherein the message push optimization comprises:
acquiring a message to be pushed corresponding to a target user terminal and a corresponding estimated information feedback probability, and transmitting the message to be pushed and the estimated information feedback probability to a Chatbot service;
acquiring the accumulated estimated information feedback quantity of each preset Chatbot corresponding to the Chatbot service;
selecting a target Chatbot for the message to be pushed in each preset Chatbot through the Chatbot service according to each accumulated estimated information feedback quantity;
updating the accumulated estimated information feedback quantity through the Chatbot service according to the estimated information feedback quantity corresponding to the estimated information feedback probability, and pushing the message to be pushed to the target user terminal;
and if a user session request of the target user terminal for the message to be pushed is received, receiving feedback information corresponding to the user session request through the target Chatbot.
2. The message push optimization method as claimed in claim 1, wherein before the step of obtaining the cumulative predicted information feedback quantity of each preset Chatbot corresponding to the Chatbot service, the message push optimization method further comprises:
acquiring information feedback probability corresponding to each un-fed user in a preset time period of the preset Chatbot;
and calculating the accumulated estimated information feedback quantity according to each information feedback probability and the preset single information feedback quantity.
3. The message push optimization method according to claim 2, wherein the step of obtaining the information feedback probability corresponding to each un-fed user in the preset time period from the preset Chatbot comprises:
acquiring a downlink user ID set and an uplink user ID set of the preset Chatbot;
determining each unreeeded user ID according to the downlink user ID set and the uplink user ID set;
and inquiring the information feedback probability according to the ID of each unrevealed user.
4. The message push optimization method as claimed in claim 1, wherein said step of selecting a target Chatbot for said message to be pushed in each of said predetermined chatbots by said Chatbot service according to each of said cumulative estimated information feedback amounts comprises:
selecting each idle Chatbot with a thread idle rate larger than a preset idle rate threshold value from each preset Chatbot;
and selecting the Chatbot with the minimum accumulated estimated information feedback quantity from the idle chatbots as the target Chatbot.
5. The message push optimization method according to any of claims 1-4, wherein after the step of pushing the message to be pushed to the target user terminal, the message push optimization method further comprises:
acquiring a user ID corresponding to the target user terminal and a Chatbot ID corresponding to the target Chatbot;
and performing association cache on the user ID and the Chatbot ID to obtain an association cache result.
6. The message pushing optimization method of claim 5, wherein the step of receiving feedback information corresponding to the user session request through the target Chatbot when receiving the user session request of the target user terminal for the message to be pushed comprises:
if the user session request is received, acquiring a feedback user ID corresponding to the user session request;
searching a target Chatbot corresponding to the feedback user ID in each preset Chatbot according to the feedback user ID and the correlation cache result;
and receiving feedback information corresponding to the user session request through the target Chatbot.
7. The message push optimization method of claim 6, wherein the target Chatbot comprises at least a predefined thread, and the step of receiving feedback information corresponding to the user session request via the target Chatbot comprises:
acquiring an uplink timestamp corresponding to the user session request and a downlink historical timestamp corresponding to the feedback user ID;
and if the uplink timestamp and the downlink historical timestamp are in a preset session period, receiving feedback information corresponding to the user session request according to a preset thread corresponding to the downlink historical timestamp.
8. A message push optimization device, the message push optimization device comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a message to be pushed corresponding to a target user terminal and a corresponding pre-estimated information feedback probability and transmitting the message to be pushed and the pre-estimated information feedback probability to a Chatbot service;
a second obtaining module, configured to obtain an accumulated estimated information feedback amount of each preset Chatbot corresponding to the Chatbot service;
a Chatbot selecting module, configured to select a target Chatbot for the message to be pushed in each preset Chatbot through the Chatbot service according to each accumulated estimated information feedback amount;
the message pushing module is used for updating the accumulated estimated information feedback quantity through the Chatbot service according to the estimated information feedback quantity corresponding to the estimated information feedback probability and pushing the message to be pushed to the target user terminal;
and the feedback receiving module is used for receiving feedback information corresponding to the user session request through the target Chatbot if the user session request of the target user terminal for the message to be pushed is received.
9. A message push optimization device, characterized in that the message push optimization device comprises: a memory, a processor, and a program stored on the memory for implementing the message push optimization method,
the memory is used for storing a program for realizing the message pushing optimization method;
the processor is configured to execute a program implementing the message push optimization method to implement the steps of the message push optimization method according to any one of claims 1 to 7.
10. A readable storage medium, having a program for implementing a message push optimization method stored thereon, the program being executed by a processor to implement the steps of the message push optimization method according to any one of claims 1 to 7.
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| CN111555963A (en) * | 2020-04-30 | 2020-08-18 | 北京思特奇信息技术股份有限公司 | Message pushing method and device, electronic equipment and storage medium |
| CN112579754A (en) * | 2020-12-18 | 2021-03-30 | 深圳云之家网络有限公司 | Intelligent robot conversation interaction method and device, and computer equipment |
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| WO2014012477A1 (en) * | 2012-07-16 | 2014-01-23 | 腾讯科技（深圳）有限公司 | Network information pushing system and method |
| CN111555963A (en) * | 2020-04-30 | 2020-08-18 | 北京思特奇信息技术股份有限公司 | Message pushing method and device, electronic equipment and storage medium |
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