CN113449593A - Early warning method and device for anchor loss situation - Google Patents
Early warning method and device for anchor loss situation Download PDFInfo
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Abstract
The application provides an early warning method and device for a main broadcasting loss condition, comprising the following steps: acquiring live broadcast characteristic information of a target anchor in a preset time period; inputting the live broadcast characteristic information into a prediction model to obtain the survival probability value of the target anchor output by the prediction model; and sending an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is less than or equal to a preset probability threshold. According to the method and the device, under the condition that the survival probability value used for representing the probability that the anchor is in the normal live broadcast state is smaller than or equal to the preset probability threshold value, the fact that the abnormal loss precursor signal occurs to the anchor is determined, early warning processing is achieved, the purpose of early warning before the anchor is in the loss state is achieved, the content production capacity of the anchor is improved, and a user side can enjoy high-quality live broadcast service.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to an early warning method and device for a main broadcasting loss condition, a training method and device for a prediction model, electronic equipment, a computer storage medium and a computer program product.
Background
In a live scenario, the content consumption behavior of the user is important, as is the production of the content by the anchor, which provides the basis for the content consumption behavior of the user.
In the related art, attention can be paid to the live broadcast state of the anchor, and the anchor is judged to be in the loss state when attention is paid to that the anchor does not carry out live broadcast in a long time range or under the conditions that the number of live broadcast times and the live broadcast time length in the long time range are less, and at the moment, the operator can recall the anchor in the loss state, so that the content production of the anchor is improved.
However, in the current scheme, an exception can be found only when the anchor is in a loss state, which brings passivity to subsequent management links, and when the anchor is in the loss state, the loss of content production is already caused, thereby affecting the content consumption capability of the user.
Disclosure of Invention
The embodiment of the application provides an early warning method and device for a main broadcast loss condition, a training method and device for a prediction model, electronic equipment, a computer storage medium and a computer program product, so as to solve the problem that in the related art, an abnormality can only be found under the condition that the main broadcast is in a loss state.
In a first aspect, an embodiment of the present application provides an early warning method for a anchor churn condition, where the method includes:
acquiring live broadcast characteristic information of a target anchor in a preset time period, wherein the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the target anchor;
inputting the live broadcast characteristic information into a prediction model to obtain a survival probability value of the target anchor output by the prediction model, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state;
and sending an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is less than or equal to a preset probability threshold.
In an alternative embodiment, the method further comprises:
drawing a survival probability value change curve for representing the change of the survival probability value along with time according to the historical survival probability value and the current survival probability value of the target anchor;
and sending the survival probability value change curve of the target anchor to the operation server so that the operation server can determine the live broadcast condition of the target anchor according to the survival probability value change curve.
In an alternative embodiment, the method further comprises:
and sending an early warning message aiming at the target anchor to the operation server under the condition that the survival probability value change curve fluctuates in an abnormal threshold range and/or the difference value of two adjacent fluctuations of the survival probability value change curve is greater than or equal to a preset difference value threshold.
In an alternative embodiment, the preset probability threshold comprises 0.3, the anomaly threshold range comprises 0 to 0.5, and the preset difference threshold comprises 0.2.
In an optional embodiment, the live feature information includes: the target anchor is in the live broadcast day number in the preset time period, the target anchor is in the interval day number between the first live broadcast in the preset time period and the last live broadcast in the preset time period, the target anchor is in the interval day number between the first live broadcast in the preset time period and the last day in the preset time period, and the target anchor is in the live broadcast average duration in the preset time period.
In an optional embodiment, the live feature information includes: the longest live broadcast time length of the target anchor in the preset time period and the last live broadcast time length of the target anchor in the preset time period;
the method further comprises the following steps:
determining the difference value between the longest live broadcast time length and the last live broadcast time length;
determining the ratio of the difference to the longest live broadcast time length as the live broadcast time length descending speed of the target anchor, wherein the live broadcast time length descending speed is used for representing the descending speed of the live broadcast time length of the target anchor;
and sending the reduction speed of the live broadcast time length of the target anchor to the operation server.
In an alternative embodiment, the method further comprises:
determining a difference between 100% and the survival probability value;
and sending an early warning message aiming at the target anchor to the operation server under the condition that the drop speed of the live broadcast time length is greater than or equal to the difference value.
In an optional embodiment, the live feature information includes: the live broadcast average time length of the target anchor in the preset time period;
the method further comprises the following steps:
taking the product of the live broadcast average time length and the survival probability value as a live broadcast time length predicted value of the target anchor, wherein the live broadcast time length predicted value is used for representing the live broadcast time length of the target anchor in a future time period;
determining a difference between 100% and the survival probability value;
taking the product of the live broadcast average time length and the difference value as a recall probability value of the target anchor, wherein the recall probability value is used for representing the recalled probability size of the target anchor in a future time period;
and sending the live broadcast duration prediction value and the recall probability value of the target anchor to the operation server.
In an alternative embodiment, the method further comprises:
and calculating to obtain the preset probability threshold according to the value range of the survival probability value and the prediction model.
In an optional implementation manner, the calculating the preset probability threshold according to the value range of the survival probability value and the prediction model includes:
calculating the recall rate and the accuracy rate corresponding to each value in the value range based on the prediction model;
adding and averaging the recall rate and the accuracy rate of each value, and taking the calculation result as a score corresponding to the value;
and taking the value with the maximum score as the preset probability threshold.
In an optional implementation manner, the warning message includes live broadcast parameter information of the target anchor, so that the operation server determines a processing manner of the target anchor according to the live broadcast parameter information.
In an alternative embodiment, the method further comprises:
determining the amount of users paying attention to the anchor;
and determining the anchor with the user amount within a preset threshold range as the target anchor.
In a second aspect, an embodiment of the present application further provides a method for training a prediction model, where the method is used to train and obtain the prediction model in the method, and the method includes:
acquiring live broadcast characteristic information of a sample anchor in a preset time period, wherein the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the sample anchor;
establishing a corresponding relation between the live broadcast characteristic information and a real survival probability value;
and training a prediction model according to the corresponding relation, so that the prediction model takes live broadcast characteristic information of the target anchor in a preset time period as model input, and takes the survival probability value of the target anchor as model output, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state.
In a third aspect, an embodiment of the present application further provides an early warning device for a anchor churn condition, where the early warning device includes:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire live broadcast characteristic information of a target anchor in a preset time period, and the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the target anchor;
the prediction module is configured to input the live broadcast feature information into a prediction model to obtain a survival probability value of the target anchor output by the prediction model, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state;
the first early warning module is configured to send an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is smaller than or equal to a preset probability threshold.
In an alternative embodiment, the apparatus further comprises:
the drawing module is configured to draw a survival probability value change curve used for representing the change of the survival probability value along with time according to the historical survival probability value and the current survival probability value of the target anchor;
the first sending module is configured to send the survival probability value change curve of the target anchor to the operation server, so that the operation server can determine the live broadcast condition of the target anchor according to the survival probability value change curve.
In an alternative embodiment, the apparatus further comprises:
and the second early warning module is configured to send an early warning message aiming at the target anchor to the operation server under the condition that the survival probability value change curve fluctuates within an abnormal threshold range and/or the difference value of two adjacent fluctuations of the survival probability value change curve is greater than or equal to a preset difference value threshold.
In an alternative embodiment, the preset probability threshold comprises 0.3, the anomaly threshold range comprises 0 to 0.5, and the preset difference threshold comprises 0.2.
In an optional embodiment, the live feature information includes: the target anchor is in the live broadcast day number in the preset time period, the target anchor is in the interval day number between the first live broadcast in the preset time period and the last live broadcast in the preset time period, the target anchor is in the interval day number between the first live broadcast in the preset time period and the last day in the preset time period, and the target anchor is in the live broadcast average duration in the preset time period.
In an optional embodiment, the live feature information includes: the longest live broadcast time length of the target anchor in the preset time period and the last live broadcast time length of the target anchor in the preset time period; the device further comprises:
a first difference module configured to determine a difference between the longest live time duration and the last live time duration;
a first calculating module configured to determine a ratio of the difference to the longest live broadcast time length as a live broadcast time length descending speed of the target anchor, where the live broadcast time length descending speed is used to characterize a descending speed of the live broadcast time length of the target anchor;
and the second sending module is configured to send the reduction speed of the live broadcast time length of the target anchor to the operation server.
In an alternative embodiment, the apparatus further comprises:
a second difference module configured to determine a difference between 100% and the survival probability value;
and the third early warning module is configured to send an early warning message aiming at the target anchor to the operation server under the condition that the drop speed of the live broadcast time length is greater than or equal to the difference value.
In an optional embodiment, the live feature information includes: the live broadcast average time length of the target anchor in the preset time period;
the device further comprises:
a second calculation module configured to use a product of the live broadcast average time length and the survival probability value as a live broadcast time length predicted value of the target anchor, where the live broadcast time length predicted value is used to represent a live broadcast time length of the target anchor in a future time period;
a third difference module configured to determine a difference between 100% and the survival probability value;
a third calculation module configured to take a product of the live broadcast average time length and the difference value as a recall probability value of the target anchor, wherein the recall probability value is used for representing the recalled probability size of the target anchor in a future time period;
and the third sending module is configured to send the live broadcast duration predicted value and the recall probability value of the target anchor to the operation server.
In an alternative embodiment, the apparatus further comprises:
and the fourth calculation module is configured to calculate to obtain the preset probability threshold according to the value range of the survival probability value and the prediction model.
In an alternative embodiment, the fourth calculation module comprises:
the first calculation submodule is configured to calculate a recall rate and an accuracy rate corresponding to each value in the value range based on the prediction model;
the second calculation submodule is configured to add and average the recall rate and the accuracy rate of each value, and take the calculation result as a score corresponding to the value;
a third calculation submodule configured to take the value with the largest score as the preset probability threshold.
In an optional implementation manner, the warning message includes live broadcast parameter information of the target anchor, so that the operation server determines a processing manner of the target anchor according to the live broadcast parameter information.
In an alternative embodiment, the apparatus further comprises:
a screening module configured to determine an amount of users who focus on the anchor;
the determining module is configured to determine the anchor with the user amount within a preset threshold range as the target anchor.
In a fourth aspect, an embodiment of the present application further provides a device for training a prediction model, where the device is used to train and obtain the prediction model in the method, and the device includes:
the training data acquisition module is configured to acquire live broadcast characteristic information of a sample anchor in a preset time period, wherein the live broadcast characteristic information is used for representing live broadcast frequency and duration of the sample anchor;
the establishing module is configured to establish a corresponding relation between the live broadcast characteristic information and a real survival probability value;
and the training module is configured to train a prediction model according to the corresponding relation, so that the prediction model takes live broadcast characteristic information of the target anchor in a preset time period as model input and takes a survival probability value of the target anchor as model output, and the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state.
In a fifth aspect, an embodiment of the present application further provides an electronic device, including a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the early warning of the anchor churn condition.
In a sixth aspect, the present application further provides a storage medium, where instructions in the computer-readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the foregoing early warning for a anchor churn condition.
In a seventh aspect, an embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the early warning for the anchor churn condition is implemented.
In the embodiment of the application, the live characteristic information of the live frequency and duration of the anchor is characterized in that a survival probability value for representing the probability of the anchor in a normal live state is determined, and under the condition that the survival probability value is less than or equal to a preset probability threshold value, the anchor is determined to have an abnormal loss precursor signal, at the moment, a live broadcast server can send an early warning message for a target anchor to an operation server, so that the operation server can further manage the target anchor according to the early warning message, the aim of early warning before the anchor is in a loss state is fulfilled, the production capacity of the anchor is improved, and a user side can enjoy high-quality live broadcast service.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating steps of an early warning method for a loss situation of a main broadcast according to an embodiment of the present application;
fig. 2 is a flowchart illustrating specific steps of an early warning method for a loss situation of a main broadcast according to an embodiment of the present application;
FIG. 3 is a graph illustrating a change in a survival probability value according to an embodiment of the present disclosure;
FIG. 4 is a flowchart illustrating steps of a method for training a predictive model according to an embodiment of the present disclosure;
fig. 5 is a block diagram of an early warning apparatus for an anchor churn condition according to an embodiment of the present disclosure;
FIG. 6 is a block diagram of a training apparatus for a prediction model according to an embodiment of the present disclosure;
FIG. 7 is a logical block diagram of an electronic device of one embodiment of the present application;
fig. 8 is a logic block diagram of an electronic device according to another embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 is a flowchart of steps of an early warning method for a anchor churn condition according to an embodiment of the present application, and as shown in fig. 1, the method may include:
In this application embodiment, the live broadcast service server of the live broadcast platform can provide live broadcast service for the anchor, and provide for the user to watch live broadcast service, the anchor is in the in-process of living broadcast, and the live broadcast server can gather the live broadcast characteristic information of the anchor, and the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the anchor, and the live broadcast characteristic information can include: the live frequency (usually can be expressed according to number of times or days) of the anchor, the live time length of each live broadcast of the anchor and the like, wherein the live time length of each live broadcast of the anchor can be counted to obtain the average value of the live time length, the live frequency of the anchor can be counted to obtain the number of times that the anchor carries out live broadcast in a detection time range, the number of days between the first live broadcast and the last live broadcast of the anchor in a detection time range and the like.
Specifically, the average value of the live time duration of each anchor can represent the content production of the anchor, and the larger the average value of the live time duration is, the higher the content production of the anchor is. The number of times that the anchor performs live broadcast in a detection time period, and the number of days between the first live broadcast and the last live broadcast of the anchor in the detection time period have positive correlation conditions with the probability that the anchor is in the 'survival' state, that is, the greater the number of times that the anchor performs live broadcast in a detection time period, the greater the number of days between the first live broadcast and the last live broadcast of the anchor in a detection time period, the greater the probability that the anchor is in the 'survival' state. Wherein, the "alive" state may refer to that the anchor is in an active and efficient content production state for a detection period, i.e. in a normal live alive state; the "churn" state, as opposed to it, may refer to the anchor being in a stagnant and inefficient content production state for a detection period, i.e., in a churn state that is not normally live.
Therefore, the live broadcast characteristic information can be used for accurately representing the content production amount of the anchor and for judging the probability of the anchor in an unweared survival state, and the step further manages the target anchor according to the probability by acquiring the live broadcast characteristic information of the target anchor within a preset time period so as to subsequently determine the probability of the target anchor in a normal live broadcast state (survival state) based on the live broadcast characteristic information, for example, determine whether the target anchor is recalled according to the probability, or determine whether the target anchor is awarded or not. Preferably, the preset time period may be within the previous 180 days.
And 102, inputting the live broadcast characteristic information into a prediction model to obtain a survival probability value of the target anchor output by the prediction model, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state.
In the embodiment of the application, the prediction model can output the survival probability value of the target anchor based on the input live broadcast characteristic information, the survival probability value represents the probability that the target anchor is in a normal live broadcast state, the higher the survival probability value is, the higher the probability that the target anchor is in an unweared live broadcast state is, and the target anchor can provide high-quality and high-efficiency content production; the smaller the survival probability value is, the higher the probability that the target anchor is in the loss state of abnormal live broadcast is, such target anchor may not be in the loss state yet, but an abnormal loss precursor signal already appears, and if the signal is concerned, initiative can be brought to the later management process.
And 103, sending an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is less than or equal to a preset probability threshold value.
In the embodiment of the application, the live broadcast platform can be deployed with an operation server besides a live broadcast service server providing live broadcast service, the operation server can pay attention to the operation process of the live broadcast service and realize the management of each anchor with the help of operation personnel, specifically, the operation server can receive an early warning message for a target anchor and realize the management of the target anchor based on the early warning message, one of the management modes of the anchor is a recall mode, and the recall mode is to provide means such as support and excitation for the anchor in a lost state or an anchor in a state about to be lost so that the anchor can restore the survival state of normal live broadcast, thereby improving the content production of the anchor and enabling a user side to enjoy the live broadcast service with higher quality. In addition, the management mode can also comprise the reward of the target anchor and the like.
Specifically, in this step, a preset probability threshold may be set as an early warning value concerning whether the target anchor has a loss precursor signal, and when the survival probability value of the target anchor is less than or equal to the preset probability threshold, it is determined that the target anchor has an abnormal loss precursor signal, at this time, the live broadcast server may send an early warning message for the target anchor to the operation server, so that the operation server further manages the target anchor according to the early warning message, for example, the operation terminal provides means such as support and excitation for the anchor to be in a loss state, so that the anchor can restore a normal live broadcast survival state.
To sum up, the early warning method for the anchor loss condition provided by the embodiment of the present application includes: acquiring live broadcast characteristic information of a target anchor in a preset time period, wherein the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the target anchor; inputting the live broadcast characteristic information into a prediction model to obtain a survival probability value of the target anchor output by the prediction model, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state; and sending an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is less than or equal to a preset probability threshold. The method and the device can determine the survival probability value for representing the probability of the anchor in the normal live broadcast state based on the live broadcast characteristic information representing the live broadcast frequency and duration of the anchor, determine that the anchor has abnormal loss precursor signals under the condition that the survival probability value is less than or equal to the preset probability threshold, and send the early warning message aiming at the target anchor to the operation server by the live broadcast server at the moment, so that the operation server can further manage the target anchor according to the early warning message, the aim of early warning before the anchor is in the loss state is fulfilled, the content production capacity of the anchor is improved, and a user side can enjoy the high-quality live broadcast service.
Fig. 2 is a flowchart of another early warning method for an anchor churn condition according to an embodiment of the present application, where as shown in fig. 2, the method may include:
In the embodiment of the application, the popularity of the anchor can be determined by paying attention to the user quantity of the anchor, in one case, the user quantity can be the vermicelli quantity of the anchor, the anchor in a preset threshold range can be determined as a target anchor, and the target anchor is the intermediate force of live content output so as to pay attention to the subsequent survival probability value of the target anchor and achieve the purpose of early warning processing. Additionally, the user volume may also include the user attention volume, subscription volume, collection volume, etc. of the anchor.
This step may specifically refer to step 101, which is not described herein again.
Optionally, the live feature information includes: the target anchor is in the live broadcast day number in the preset time period, the target anchor is in the interval day number between the first live broadcast in the preset time period and the last live broadcast in the preset time period, the target anchor is in the interval day number between the first live broadcast in the preset time period and the last day in the preset time period, and the target anchor is in the live broadcast average duration in the preset time period.
In the embodiment of the application, the average value of the live time duration of each anchor can represent the content production of the anchor, and the larger the average value of the live time duration is, the higher the content production of the anchor is. The number of times that the anchor performs live broadcast in a detection time period, and the number of days between the first live broadcast and the last live broadcast of the anchor in the detection time period have positive correlation conditions with the probability that the anchor is in the 'survival' state, that is, the greater the number of times that the anchor performs live broadcast in a detection time period, the greater the number of days between the first live broadcast and the last live broadcast of the anchor in a detection time period, the greater the probability that the anchor is in the 'survival' state.
This step may specifically refer to step 102, which is not described herein again.
Optionally, the early warning message includes live broadcast parameter information of the target anchor, so that the operation server determines a processing mode of the target anchor according to the live broadcast parameter information. .
In the embodiment of the application, the live broadcast parameter information may include the watching duration, the watching number, the pollination amount, the number of received gifts, the interaction efficiency value and the like of the live broadcast room of the target anchor, and in the early warning, the watching duration, the watching number, the pollination amount, the number of received gifts, the interaction efficiency value and the like of the live broadcast room of the target anchor may be added into the early warning message to be sent, and the operation terminal may determine the processing mode of the target anchor by using these information as a reference.
In addition, information such as the watching time length, the watching number, the vermicelli amount, the number of received gifts, the interaction efficiency value and the like of the live broadcast room of the target anchor is used as core index data of the target anchor, an operation end can analyze the factors of loss signals of the target anchor aiming at the data conveniently, the whole live broadcast operation frame can be optimized based on the factors obtained through analysis, and management strategies and the like can be adjusted.
And 204, sending an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is less than or equal to a preset probability threshold value.
This step may specifically refer to step 103, which is not described herein again.
Optionally, the method may further include:
and step 205, drawing a survival probability value change curve for representing the change of the survival probability value along with time according to the historical survival probability value and the current survival probability value of the target anchor.
In the embodiment of the application, the prediction model can output the survival probability value of the target anchor according to the live broadcast characteristic information of the target anchor in real time, so that a survival probability value change curve used for representing the change of the survival probability value along with time can be drawn according to the historical survival probability value and the current survival probability value of the target anchor.
For example, referring to fig. 3, fig. 3 is a graph of change in survival probability value provided by the embodiment of the present application, which captures a change curve of survival probability value of the anchor in 2020 and 7 months to 2021 and 2 months over time.
And step 206, sending the survival probability value change curve of the target anchor to the operation server so that the operation server can determine the live broadcast condition of the target anchor according to the survival probability value change curve.
In the embodiment of the application, the survival probability value change curve of the target anchor can be sent to the operation server, so that the operation server can determine the live broadcast condition of the target anchor according to the survival probability value change curve and make a corresponding operation adjustment strategy. For example, under the condition that the survival probability value change curve has a greatly descending trend, the target anchor is considered to have a loss risk, and the operation server can inform the operation personnel to recall the target anchor; under the condition that the survival probability value change curve is stable and unchanged in a lower range for a long time; the target anchor is considered to be in the service bottleneck period, and the operation server can inform the operation personnel of performing auxiliary means such as stream pushing and excitation on the target anchor and the like, so that the target anchor is helped to break through the service bottleneck period.
Optionally, the method may further include:
and step 207, sending an early warning message aiming at the target anchor to the operation server under the condition that the survival probability value change curve fluctuates within an abnormal threshold range and/or the difference value of two adjacent fluctuations of the survival probability value change curve is greater than or equal to a preset difference value threshold.
In the embodiment of the application, the survival probability value change curve of the target anchor fluctuates along with time, so that the fluctuation range of the survival probability value change curve and the difference value of two adjacent fluctuations can represent whether the content production state of the target anchor is normal or not.
Optionally, the preset probability threshold includes 0.3, the anomaly threshold range includes 0 to 0.5, and the preset difference threshold includes 0.2.
For example, referring to fig. 3, the target anchor shown in fig. 3 is live last in 10 months of 2020, with 0.3 as the preset probability threshold of the target anchor, and then the survival probability value of the target anchor in 12 months of 2020 is already lower than the preset probability threshold of 0.3, and the live broadcast server may trigger the early warning mechanism.
Further, when the survival probability value change curve of the target anchor fluctuates within the abnormal threshold range (0-0.5), it indicates that the target anchor has a time period with a lower survival probability value level, and the content production capacity of the target anchor is low and there is a loss risk, as shown in fig. 3, the target anchor is in a state of low content production capacity during a period from 11 months of 2020 to 2 months of 2021; under the condition that the survival probability value change curve of the target anchor fluctuates outside the abnormal threshold range (0-0.5), it is indicated that the target anchor has a time period with a higher survival probability value level, at this time, the content production of the target anchor is efficient, and there is no loss risk, for example, in the time period of 7 months to 10 months in 2020 in fig. 3, the target anchor is in a state of efficient content production.
In addition, if the difference value of two adjacent fluctuations of the survival probability value change curve of the target anchor is greater than or equal to the preset difference value threshold (0.2), it also indicates that the target anchor has an abnormal fluctuation of the survival probability, and an early warning is needed to find out the reason of the abnormal fluctuation.
Optionally, the live broadcast feature information includes: the longest live broadcast time length of the target anchor in the preset time period and the last live broadcast time length of the target anchor; after step 203, the method may further comprise:
and 208, determining the difference value between the longest live broadcast time length and the last live broadcast time length.
And 209, determining the ratio of the difference to the longest live broadcast time length as the live broadcast time length descending speed of the target anchor, wherein the live broadcast time length descending speed is used for representing the descending speed of the live broadcast time length of the target anchor.
In the embodiment of the application, the live time length of the anchor can represent the content production amount of the anchor, and the live time length is positively correlated with the content production amount. The method comprises the steps that the ratio of the difference value between the longest live broadcast time length of a target anchor in a preset time period and the last live broadcast time length to the longest live broadcast time length can be used as the live broadcast time length reduction speed of the target anchor and used for representing the reduction speed of the content production of the target anchor, and the content production of the target anchor is reduced faster under the condition that the reduction speed of the live broadcast time length is larger.
And step 210, sending the reduction speed of the live broadcast time length of the target anchor to the operation server.
In this step, the live broadcast server may send the live broadcast duration reduction speed of the target anchor to the operation server, so that the operation server may judge the content production volume change trend of the target anchor according to the live broadcast duration reduction speed, and make a corresponding support policy adjustment based on the content production volume change trend, so as to promote the content production volume of the target anchor.
Optionally, the method may further include:
and step 211, determining a difference value between 100% and the survival probability value, and sending an early warning message aiming at the target anchor to the operation server under the condition that the dropping speed of the live broadcast time length is greater than or equal to the difference value.
In the embodiment of the application, for the target anchor, under the condition that the reduction speed of the live broadcast time length of the target anchor is greater than or equal to 100% and the difference value between the survival probability value of the target anchor, it can be considered that the content production amount of the target anchor is losing rapidly, and at this time, the live broadcast server can send an early warning message for the target anchor to the operation server, so that an operator can find out the reason of the abnormal loss of the content production amount of the target anchor according to the early warning message.
Optionally, the live feature information includes: the live broadcast average time length of the target anchor in the preset time period; after step 203, the method may further comprise:
and 212, taking the product of the live broadcast average time length and the survival probability value as a live broadcast time length predicted value of the target anchor, wherein the live broadcast time length predicted value is used for representing the live broadcast time length of the target anchor in a future time period.
In the embodiment of the application, a positive correlation factor exists between the content production of the anchor and the live broadcast time length of the anchor, so that the product of the live broadcast average time length of the target anchor in a preset time period and the survival probability value of the target anchor can be used as the predicted value of the future live broadcast time length of the target anchor, and the larger the predicted value of the live broadcast time length of the target anchor is, the higher the content production of the target anchor in the future time period is.
And step 213, determining a difference value between 100% and the survival probability value, taking the product of the live broadcast average time length and the difference value as a recall probability value of the target anchor, wherein the recall probability value is used for representing the recalled probability of the target anchor in a future time period.
In the embodiment of the application, a positive correlation factor exists between the recalling probability of the anchor and the live broadcast time length of the anchor, so that the product of the live broadcast average time length of the target anchor in a preset time period and the difference value between 100% and the survival probability value of the target anchor can be used as the recalling probability of the target anchor, and the higher the recalling probability value of the target anchor is, the larger the content production amount generated by the operation terminal in recalling the target anchor is.
And step 214, sending the live broadcast duration prediction value and the recall probability value of the target anchor to the operation server.
In the embodiment of the application, the live broadcast duration predicted value and the recall probability value can quantize the content production of the target anchor, and the live broadcast duration predicted value and the recall probability value of the target anchor are sent to the operation server by the live broadcast server, so that the operation terminal can make corresponding support strategy adjustment based on the quantized values to promote the content production of the target anchor.
Optionally, the method may further include:
step 215, calculating to obtain the preset probability threshold according to the value range of the survival probability value and the prediction model.
In the embodiment of the application, the survival probability value represents the probability that the target anchor is in a normal live broadcast state, the preset probability threshold value is taken as an early warning value for paying attention to whether the target anchor generates the loss precursor signal or not, and is a precondition for realizing the early warning of the target anchor.
Optionally, step 215 may specifically include:
and a substep 2151 of calculating a recall rate and an accuracy rate corresponding to each value in the value range based on the prediction model.
And a substep 2152 of adding and averaging the recall rate and the accuracy rate of each value, and taking the calculated result as a score corresponding to the value.
Substep 2153, taking the value with the largest score as the preset probability threshold.
In the embodiment of the application, the prediction model has higher requirements on recall rate and accuracy rate, and the prediction model with higher recall rate and accuracy rate can play a role better. Wherein, the definition of recall (recall) comprises: if the current broadcasting date is judged to be the anchor broadcasting in the loss state (for example, the broadcasting is not carried out within 60 days), the number of days from the last broadcasting date to the first day when the predicted survival probability value is reduced below the preset probability threshold value is less than 60 days, and the current broadcasting date is considered to be successfully recalled; accuracy (precision) definition: and acquiring the anchor with the survival probability below a specified threshold value output by the prediction model 60 days before the end of the preset time period, and checking the number of the repeated anchors within the 60 days.
The embodiment of the application can obtain the value of the preset probability threshold through two grid searches, and specifically includes:
firstly, determining that the value range of the survival probability value is 0.1-0.9, then, reserving 1 digit number after decimal point to carry out the first grid search operation, calculating the recall rate and the accuracy corresponding to each value in the value range based on a prediction model, calculating the average value of the recall rate and the accuracy corresponding to each value, taking the average value as the score of the value, and obtaining the following results as the following table 1:
| value taking | Recall rate | Rate of accuracy | Score of |
| 0.1 | 0.921875 | 0.694103 | 0.791937 |
| 0.2 | 0.953125 | 0.686385 | 0.798057 |
| 0.3 | 0.976852 | 0.676253 | 0.799222 |
| 0.4 | 0.986690 | 0.667582 | 0.796358 |
| 0.5 | 0.993056 | 0.661107 | 0.793774 |
| 0.6 | 0.998264 | 0.651252 | 0.788257 |
| 0.7 | 1.000000 | 0.645679 | 0.784696 |
| 0.8 | 1.000000 | 0.633404 | 0.775563 |
| 0.9 | 1.000000 | 0.617241 | 0.763326 |
TABLE 1
From the above table 1, it can be preliminarily determined that the score is the largest when the value is 0.3, that is, the average value of the recall rate and the accuracy corresponding to the value 0.3 is the largest, so that the recall rate and the accuracy of the prediction model can both have higher standards under the condition that the value is 0.3.
Further, the values obtained by the first grid search and 2 digits of the decimal value are reserved for the second grid search operation, the recall rate and the accuracy rate corresponding to each value in the value range are calculated based on the prediction model, the average value of the recall rate and the accuracy rate corresponding to each value is calculated, the average value is used as the score of the value, and the obtained result is as the following table 2:
TABLE 2
From the above table 1, when the value is still 0.3, the score is the largest, that is, the average value of the recall rate and the accuracy corresponding to the value 0.3 is the largest, so that under the condition that the value is 0.3, the recall rate and the accuracy of the prediction model can both have higher standards, and then the embodiment of the application can use the preset probability threshold value 0.3 as an early warning value for paying attention to whether the loss precursor signal occurs in the target anchor.
To sum up, the early warning method for the anchor loss condition provided by the embodiment of the present application includes: acquiring live broadcast characteristic information of a target anchor in a preset time period, wherein the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the target anchor; inputting the live broadcast characteristic information into a prediction model to obtain a survival probability value of the target anchor output by the prediction model, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state; and sending an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is less than or equal to a preset probability threshold. The method and the device can determine the survival probability value for representing the probability of the anchor in the normal live broadcast state based on the live broadcast characteristic information representing the live broadcast frequency and duration of the anchor, determine that the anchor has abnormal loss precursor signals under the condition that the survival probability value is less than or equal to the preset probability threshold, and send the early warning message aiming at the target anchor to the operation server by the live broadcast server at the moment, so that the operation server can further manage the target anchor according to the early warning message, the aim of early warning before the anchor is in the loss state is fulfilled, the content production capacity of the anchor is improved, and a user side can enjoy the high-quality live broadcast service.
Fig. 4 is a flowchart of steps of a method for training a prediction model according to an embodiment of the present application, for training the prediction model in the foregoing embodiment, as shown in fig. 4, including:
And step 302, establishing a corresponding relation between the live broadcast characteristic information and the real survival probability value.
In this application embodiment, for the training process of the prediction model, in this application embodiment, some anchor can be selected as sample anchors, and live broadcast feature information of the sample anchors in a preset time period is collected as training data, and the collected live broadcast feature information may include: the method comprises the steps of the number of live broadcast days of a sample anchor in a preset time period, the number of interval days between the first live broadcast and the last live broadcast of the sample anchor in a historical time period, the number of interval days between the first live broadcast and the last live broadcast of the sample anchor in the historical time period, and the average live broadcast duration of the sample anchor in the historical time period.
Further, a corresponding real survival probability value can be labeled to the live broadcast feature information, a corresponding relation between the live broadcast feature information and the real survival probability value is established, so that a training data pair is formed, the acquired live broadcast feature information can be used as input in the later training process, the survival probability value of the sample anchor is used as output and used as a training purpose, specifically, the output value of the model after the live broadcast feature information is input and the real survival probability value corresponding to the input live broadcast feature information can be determined, a loss value is calculated based on the output value and the real survival probability value, parameters of the model are trained by combining a preset loss function until the training goal is reached, and the adopted model can comprise a probability model (buy rule model, BTYD model).
To sum up, the early warning method for the anchor loss condition provided by the embodiment of the present application includes: acquiring live broadcast characteristic information of a sample anchor in a preset time period, wherein the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the sample anchor; establishing a corresponding relation between the live broadcast characteristic information and the real survival probability value; and training the prediction model according to the corresponding relation, so that the prediction model takes the live broadcast characteristic information of the target anchor in a preset time period as model input, and takes the survival probability value of the target anchor as model output, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state. The method and the device can determine the survival probability value for representing the probability of the anchor in the normal live broadcast state based on the live broadcast characteristic information and the trained prediction model, and determine that the anchor has abnormal loss precursor signals under the condition that the survival probability value is smaller than or equal to the preset probability threshold value, so that the aim of early warning before the anchor is in the loss state is fulfilled, the content production capacity of the anchor is improved, and a user side can enjoy the high-quality live broadcast service.
Fig. 5 is a block diagram of an early warning apparatus for a anchor churn condition according to an embodiment of the present application, as shown in fig. 5, including: the system comprises an acquisition module 401, a prediction module 402 and a first early warning module 403.
An obtaining module 401, configured to obtain live broadcast feature information of a target anchor in a preset time period, where the live broadcast feature information is used to represent live broadcast frequency and duration of the target anchor;
a prediction module 402, configured to input the live broadcast feature information into a prediction model, and obtain a survival probability value of the target anchor output by the prediction model, where the survival probability value is used to represent a probability that the target anchor is in a normal live broadcast state;
a first warning module 403, configured to send a warning message for the target anchor to an operation server if the survival probability value is less than or equal to a preset probability threshold.
In an alternative implementation, the apparatus further includes:
the drawing module is configured to draw a survival probability value change curve used for representing the change of the survival probability value along with time according to the historical survival probability value and the current survival probability value of the target anchor;
the first sending module is configured to send the survival probability value change curve of the target anchor to the operation server, so that the operation server can determine the live broadcast condition of the target anchor according to the survival probability value change curve.
In an alternative implementation, the apparatus further includes:
and the second early warning module is configured to send an early warning message aiming at the target anchor to the operation server under the condition that the survival probability value change curve fluctuates within an abnormal threshold range and/or the difference value of two adjacent fluctuations of the survival probability value change curve is greater than or equal to a preset difference value threshold.
In an optional implementation manner, the preset probability threshold includes 0.3, the anomaly threshold range includes 0 to 0.5, and the preset difference threshold includes 0.2.
In an optional implementation, the live feature information includes: the target anchor is in the live broadcast day number in the preset time period, the target anchor is in the interval day number between the first live broadcast in the preset time period and the last live broadcast in the preset time period, the target anchor is in the interval day number between the first live broadcast in the preset time period and the last day in the preset time period, and the target anchor is in the live broadcast average duration in the preset time period.
In an optional implementation, the live feature information includes: the longest live broadcast time length of the target anchor in the preset time period and the last live broadcast time length of the target anchor in the preset time period; the device further comprises:
a first difference module configured to determine a difference between the longest live time duration and the last live time duration;
a first calculating module configured to determine a ratio of the difference to the longest live broadcast time length as a live broadcast time length descending speed of the target anchor, where the live broadcast time length descending speed is used to characterize a descending speed of the live broadcast time length of the target anchor;
and the second sending module is configured to send the reduction speed of the live broadcast time length of the target anchor to the operation server.
In an alternative implementation, the apparatus further includes:
a second difference module configured to determine a difference between 100% and the survival probability value;
and the third early warning module is configured to send an early warning message aiming at the target anchor to the operation server under the condition that the drop speed of the live broadcast time length is greater than or equal to the difference value.
In an optional implementation, the live feature information includes: the live broadcast average time length of the target anchor in the preset time period;
the device further comprises:
a second calculation module configured to use a product of the live broadcast average time length and the survival probability value as a live broadcast time length predicted value of the target anchor, where the live broadcast time length predicted value is used to represent a live broadcast time length of the target anchor in a future time period;
a third difference module configured to determine a difference between 100% and the survival probability value;
a third calculation module configured to take a product of the live broadcast average time length and the difference value as a recall probability value of the target anchor, wherein the recall probability value is used for representing the recalled probability size of the target anchor in a future time period;
and the third sending module is configured to send the live broadcast duration predicted value and the recall probability value of the target anchor to the operation server.
In an alternative implementation, the apparatus further includes:
and the fourth calculation module is configured to calculate to obtain the preset probability threshold according to the value range of the survival probability value and the prediction model.
In an optional implementation manner, the first calculating sub-module is configured to calculate, based on the prediction model, a recall rate and an accuracy rate corresponding to each value in the value range;
the second calculation submodule is configured to add and average the recall rate and the accuracy rate of each value, and take the calculation result as a score corresponding to the value;
a third calculation submodule configured to take the value with the largest score as the preset probability threshold.
In an optional implementation manner, the early warning message includes live broadcast parameter information of the target anchor, so that the operation server determines a processing manner of the target anchor according to the live broadcast parameter information.
In an alternative implementation, the apparatus further includes:
a screening module configured to determine an amount of users who focus on the anchor;
the determining module is configured to determine the anchor with the user amount within a preset threshold range as the target anchor.
To sum up, the early warning device to anchor condition that runs off that this application embodiment provided, this application can be based on the live characteristic information of the live frequency and the duration of the representation anchor, confirm the survival probability value that is used for representing the probability that the anchor is in the state of normal live, and under the condition that the survival probability value is less than or equal to and predetermines the probability threshold, confirm that the anchor has appeared the unusual loss precursor signal, can send the early warning message to the target anchor by the live broadcast server to the operation server this moment, carry out further management to the target anchor according to the early warning message for the operation server, reached and carried out the purpose of early warning before the anchor is in the state of running off, thereby the content production volume of anchor has been improved, make the user side can enjoy higher-quality live service.
Fig. 6 is a block diagram of a training apparatus for a prediction model according to an embodiment of the present application, as shown in fig. 6, including: training data module 501, establishing module 502, training module 503.
A training data obtaining module 501, configured to obtain live broadcast feature information of a sample anchor in a preset time period, where the live broadcast feature information is used to represent live broadcast frequency and duration of the sample anchor;
an establishing module 502 configured to establish a corresponding relationship between the live broadcast feature information and a real survival probability value;
the training module 503 is configured to train a prediction model according to the correspondence, so that the prediction model takes live broadcast feature information of a target anchor in a preset time period as a model input, and takes a survival probability value of the target anchor as a model output, where the survival probability value is used to represent a probability that the target anchor is in a normal live broadcast state.
The method and the device can determine the survival probability value for representing the probability of the anchor in the normal live broadcast state based on the live broadcast characteristic information and the trained prediction model, and determine that the anchor has abnormal loss precursor signals under the condition that the survival probability value is smaller than or equal to the preset probability threshold value, so that the aim of early warning before the anchor is in the loss state is fulfilled, the content production capacity of the anchor is improved, and a user side can enjoy the high-quality live broadcast service.
Fig. 7 is a block diagram illustrating an electronic device 600 according to an example embodiment. For example, the electronic device 600 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 7, electronic device 600 may include one or more of the following components: a processing component 602, a memory 604, a power component 606, a multimedia component 608, an audio component 610, an interface to input/output (I/O) 612, a sensor component 614, and a communication component 616.
The processing component 602 generally controls overall operation of the electronic device 600, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 602 can include one or more modules that facilitate interaction between the processing component 602 and other components. For example, the processing component 602 can include a multimedia module to facilitate interaction between the multimedia component 608 and the processing component 602.
The memory 604 is used to store various types of data to support operations at the electronic device 600. Examples of such data include instructions for any application or method operating on the electronic device 600, contact data, phonebook data, messages, pictures, multimedia, and so forth. The memory 604 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 608 includes a screen that provides an output interface between the electronic device 600 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense demarcations of a touch or slide action, but also detect a duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 600 is in an operation mode, such as a photographing mode or a multimedia mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 610 is used to output and/or input audio signals. For example, the audio component 610 may include a Microphone (MIC) for receiving external audio signals when the electronic device 600 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 604 or transmitted via the communication component 616. In some embodiments, audio component 610 further includes a speaker for outputting audio signals.
The I/O interface 612 provides an interface between the processing component 602 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor component 614 includes one or more sensors for providing status assessment of various aspects of the electronic device 600. For example, the sensor component 614 may detect an open/closed state of the electronic device 600, the relative positioning of components, such as a display and keypad of the electronic device 600, the sensor component 614 may also detect a change in the position of the electronic device 600 or a component of the electronic device 600, the presence or absence of user contact with the electronic device 600, orientation or acceleration/deceleration of the electronic device 600, and a change in the temperature of the electronic device 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 616 is operable to facilitate wired or wireless communication between the electronic device 600 and other devices. The electronic device 600 may access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 616 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to implement an early warning method for an anchor churn condition according to an embodiment of the present application.
In an exemplary embodiment, a non-transitory computer storage medium including instructions, such as the memory 604 including instructions, executable by the processor 620 of the electronic device 600 to perform the above-described method is also provided. For example, the non-transitory storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Fig. 8 is a block diagram illustrating an electronic device 700 in accordance with an example embodiment. For example, the electronic device 700 may be provided as a server. Referring to fig. 8, electronic device 700 includes a processing component 722 that further includes one or more processors, and memory resources, represented by memory 732, for storing instructions, such as applications, that are executable by processing component 722. The application programs stored in memory 732 may include one or more modules that each correspond to a set of instructions. In addition, the processing component 722 is configured to execute instructions to perform an early warning method for an anchor churn condition provided by the embodiments of the present application.
The electronic device 700 may also include a power component 726 that is configured to perform power management of the electronic device 700, a wired or wireless network interface 750 that is configured to connect the electronic device 700 to a network, and an input output (I/O) interface 758. The electronic device 700 may operate based on an operating system stored in memory 732, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
An embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for early warning of a anchor churn condition is implemented.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. An early warning method for a anchor churn condition, the method comprising:
acquiring live broadcast characteristic information of a target anchor in a preset time period, wherein the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the target anchor;
inputting the live broadcast characteristic information into a prediction model to obtain a survival probability value of the target anchor output by the prediction model, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state;
and sending an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is less than or equal to a preset probability threshold.
2. The method of claim 1, further comprising:
drawing a survival probability value change curve for representing the change of the survival probability value along with time according to the historical survival probability value and the current survival probability value of the target anchor;
and sending the survival probability value change curve of the target anchor to the operation server so that the operation server can determine the live broadcast condition of the target anchor according to the survival probability value change curve.
3. The method of claim 2, further comprising:
and sending an early warning message aiming at the target anchor to the operation server under the condition that the survival probability value change curve fluctuates in an abnormal threshold range and/or the difference value of two adjacent fluctuations of the survival probability value change curve is greater than or equal to a preset difference value threshold.
4. The method of claim 3, wherein the preset probability threshold comprises 0.3, the anomaly threshold range comprises 0 to 0.5, and the preset difference threshold comprises 0.2.
5. A method for training a predictive model, the method being used for training a predictive model in a method according to any one of claims 1 to 4, the method comprising:
acquiring live broadcast characteristic information of a sample anchor in a preset time period, wherein the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the sample anchor;
establishing a corresponding relation between the live broadcast characteristic information and a real survival probability value;
and training a prediction model according to the corresponding relation, so that the prediction model takes live broadcast characteristic information of the target anchor in a preset time period as model input, and takes the survival probability value of the target anchor as model output, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state.
6. An early warning device for a anchor churn condition, the device comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire live broadcast characteristic information of a target anchor in a preset time period, and the live broadcast characteristic information is used for representing the live broadcast frequency and duration of the target anchor;
the prediction module is configured to input the live broadcast feature information into a prediction model to obtain a survival probability value of the target anchor output by the prediction model, wherein the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state;
the first early warning module is configured to send an early warning message aiming at the target anchor to an operation server under the condition that the survival probability value is smaller than or equal to a preset probability threshold.
7. An apparatus for training a prediction model, the apparatus being used for training the prediction model in the apparatus of claim 6, the apparatus comprising:
the training data acquisition module is configured to acquire live broadcast characteristic information of a sample anchor in a preset time period, wherein the live broadcast characteristic information is used for representing live broadcast frequency and duration of the sample anchor;
the establishing module is configured to establish a corresponding relation between the live broadcast characteristic information and a real survival probability value;
and the training module is configured to train a prediction model according to the corresponding relation, so that the prediction model takes live broadcast characteristic information of the target anchor in a preset time period as model input and takes a survival probability value of the target anchor as model output, and the survival probability value is used for representing the probability that the target anchor is in a normal live broadcast state.
8. An electronic device, comprising: a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of any one of claims 1 to 5.
9. A computer storage medium, wherein instructions in the computer-readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any of claims 1-5.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the method of any of claims 1-5 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108665321A (en) * | 2018-05-18 | 2018-10-16 | 广州虎牙信息科技有限公司 | High viscosity customer loss prediction technique, device and computer readable storage medium |
| CN109636446A (en) * | 2018-11-16 | 2019-04-16 | 北京奇虎科技有限公司 | Customer churn prediction technique, device and electronic equipment |
| CN109962795A (en) * | 2017-12-22 | 2019-07-02 | 中国移动通信集团广东有限公司 | A kind of 4G customer churn method for early warning and system based on multidimensional union variable |
| CN110049372A (en) * | 2019-04-23 | 2019-07-23 | 广州虎牙信息科技有限公司 | Main broadcaster stablizes prediction technique, device, equipment and the storage medium of retention ratio |
| CN110475155A (en) * | 2019-08-19 | 2019-11-19 | 北京字节跳动网络技术有限公司 | Live video temperature state identification method, device, equipment and readable medium |
| CN110996116A (en) * | 2019-12-18 | 2020-04-10 | 广州市百果园信息技术有限公司 | Anchor information pushing method and device, computer equipment and storage medium |
| CN111666535A (en) * | 2020-04-24 | 2020-09-15 | 北京奇艺世纪科技有限公司 | Method and device for determining user activity duration, electronic equipment and storage medium |
-
2021
- 2021-05-25 CN CN202110573101.8A patent/CN113449593A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109962795A (en) * | 2017-12-22 | 2019-07-02 | 中国移动通信集团广东有限公司 | A kind of 4G customer churn method for early warning and system based on multidimensional union variable |
| CN108665321A (en) * | 2018-05-18 | 2018-10-16 | 广州虎牙信息科技有限公司 | High viscosity customer loss prediction technique, device and computer readable storage medium |
| CN109636446A (en) * | 2018-11-16 | 2019-04-16 | 北京奇虎科技有限公司 | Customer churn prediction technique, device and electronic equipment |
| CN110049372A (en) * | 2019-04-23 | 2019-07-23 | 广州虎牙信息科技有限公司 | Main broadcaster stablizes prediction technique, device, equipment and the storage medium of retention ratio |
| CN110475155A (en) * | 2019-08-19 | 2019-11-19 | 北京字节跳动网络技术有限公司 | Live video temperature state identification method, device, equipment and readable medium |
| CN110996116A (en) * | 2019-12-18 | 2020-04-10 | 广州市百果园信息技术有限公司 | Anchor information pushing method and device, computer equipment and storage medium |
| CN111666535A (en) * | 2020-04-24 | 2020-09-15 | 北京奇艺世纪科技有限公司 | Method and device for determining user activity duration, electronic equipment and storage medium |
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