CN111832780A - Private network data prediction method, private network data prediction device, private network data prediction equipment and storage medium - Google Patents
Private network data prediction method, private network data prediction device, private network data prediction equipment and storage medium Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a storage medium for predicting private network data, wherein the method comprises the following steps: acquiring a plurality of historical private network data; respectively establishing a plurality of models according to a plurality of historical private network data; selecting a target model according to the plurality of models; according to the target model, the private network data is predicted, and the accuracy of the private network data prediction is improved.
Description
Technical Field
The invention relates to the technical field of private network data analysis, in particular to a private network data prediction method, a private network data prediction device, private network data prediction equipment and a private network data storage medium.
Background
With the continuous development of science and technology, electronic technology has also gained rapid development, and the variety of electronic products is also more and more, and people also enjoy various conveniences brought by the development of science and technology. People can enjoy comfortable life brought along with the development of science and technology through various types of mobile terminals. For example, mobile terminals such as smart phones and tablet computers have become an important part of people's lives, and users can listen to music, play games and the like by using the mobile terminals such as smart phones and tablet computers, so as to relieve pressure brought by modern fast-paced lives. In different time periods, dates and the like, the use frequency of the user to the terminal and the network requirements are different, and in order to reduce the network congestion and breakdown, the private network data needs to be reasonably predicted.
In the prior art, for the prediction of private network data, a simple statistical analysis method is usually adopted for historical private network data, and the private network data is roughly predicted so as to take relevant intervention methods or improvement measures in advance.
However, in the prior art, the private network data is predicted in a statistical analysis mode of historical private network data, and the prediction accuracy is low.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for predicting private network data, which are used for realizing the prediction of the private network data.
In a first aspect, the present application provides a method for predicting private network data, including:
a plurality of historical private network data are obtained.
And respectively establishing a plurality of models according to a plurality of historical private network data.
From the plurality of models, a target model is selected.
And predicting the private network data according to the target model.
According to the scheme, the multiple models are respectively established according to the multiple historical private network data, the target model is selected from the multiple models, the private network data are predicted according to the target model, and the accuracy of private network data prediction is improved.
Optionally, before respectively building a plurality of models according to a plurality of historical private network data, the method includes:
and preprocessing the plurality of historical private network data to obtain a plurality of preprocessed historical private network data.
Correspondingly, according to a plurality of historical private network data, a plurality of models are respectively established, and the method comprises the following steps: and respectively establishing a plurality of models according to the preprocessed plurality of historical private network data.
According to the scheme, the historical private network data are preprocessed before the plurality of models are respectively established according to the historical private network data, so that the quality of the historical private network data is improved, then the models are respectively established according to the preprocessed historical private network data, and the accuracy of establishing the models is improved.
Optionally, respectively establishing a plurality of models according to a plurality of historical private network data, including:
and selecting the first historical private network data as a training set from the plurality of historical private network data.
And aiming at any model, training the model according to the first historical private network data to obtain a trained model.
And selecting second historical private network data as a test set from the plurality of historical private network data.
And testing the trained model according to the second historical private network data aiming at any trained model to obtain a tested model.
In the scheme, the multiple models are trained and tested respectively by using the multiple historical private network data, so that the multiple models are established.
Optionally, selecting the target model according to a plurality of models includes:
obtaining the prediction accuracy of a plurality of models according to the tested models; and selecting the target model according to the prediction accuracy of the plurality of models.
Optionally, after predicting the private network data according to the target model, the method further includes:
and pushing a prediction result for predicting the private network data to the user.
Optionally, the historical private network data includes:
historical traffic times and/or historical core network traffic.
The contents and effects of the prediction apparatus, device, storage medium, and computer program product for private network data provided by the present invention are described below with reference to the first aspect and the optional manner of the first aspect of the embodiment of the present invention.
In a second aspect, the present invention provides a private network data prediction apparatus, including:
the acquisition module is used for acquiring a plurality of historical private network data.
And the establishing module is used for respectively establishing a plurality of models according to the plurality of historical private network data.
And the selection module is used for selecting the target model according to the plurality of models.
And the prediction module is used for predicting the private network data according to the target model.
Optionally, the prediction apparatus for private network data provided by the present invention further includes:
and the preprocessing module is used for preprocessing the plurality of historical private network data to obtain the plurality of preprocessed historical private network data.
Correspondingly, the establishing module is specifically configured to:
and respectively establishing a plurality of models according to the preprocessed plurality of historical private network data.
Optionally, the establishing module is specifically configured to:
and selecting the first historical private network data as a training set from the plurality of historical private network data.
And aiming at any model, training the model according to the first historical private network data to obtain a trained model.
And selecting second historical private network data as a test set from the plurality of historical private network data.
And testing the trained model according to the second historical private network data aiming at any trained model to obtain a tested model.
Optionally, the selection module is specifically configured to:
obtaining the prediction accuracy of a plurality of models according to the tested models; and selecting the target model according to the prediction accuracy of the plurality of models.
Optionally, the prediction apparatus for private network data provided by the present invention further includes:
and the pushing module is used for pushing a prediction result for predicting the private network data to the user.
Optionally, the historical private network data includes:
historical traffic times and/or historical core network traffic.
In a third aspect, an embodiment of the present invention provides an apparatus, including:
a processor; a memory; and a computer program; wherein a computer program is stored in the memory and configured to be executed by the processor, the computer program comprising instructions for performing the method of predicting private network data as described in the first aspect or the first aspect alternative.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and the computer program enables a server to execute the method for predicting private network data according to the first aspect or the optional manner of the first aspect.
In a fifth aspect, an embodiment of the present invention provides a computer program product, including: executable instructions for implementing a method of prediction of private network data as described in the first aspect or an alternative to the first aspect.
According to the private network data prediction method, device, equipment and storage medium, the plurality of historical private network data are obtained, then the plurality of models are respectively established according to the plurality of historical private network data, the target model is selected according to the plurality of models, and finally the private network data are predicted according to the target model. Because a plurality of models are respectively established according to a plurality of historical private network data, and a target model is selected from the plurality of models, the private network data is predicted according to the target model, and the accuracy of private network data prediction is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention 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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for predicting private network data according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method for predicting private network data according to another embodiment of the present invention;
fig. 3 is a flowchart illustrating a method for predicting private network data according to another embodiment of the present invention;
fig. 4 is a schematic structural diagram of a private network data prediction apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a private network data prediction apparatus according to another embodiment of the present invention;
fig. 6 is a schematic structural diagram of a private network data prediction apparatus according to another embodiment of the present invention;
fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
With the continuous development of science and technology, electronic technology has also gained rapid development, and the variety of electronic products is also more and more, and people also enjoy various conveniences brought by the development of science and technology. People can enjoy comfortable life brought along with the development of science and technology through various types of mobile terminals. In different time periods, dates and the like, the use frequency of the user to the terminal and the network requirements are different, and in order to reduce the network congestion and breakdown, the private network data needs to be reasonably predicted. At present, for the prediction of private network data, a simple statistical analysis mode is usually adopted for historical private network data, the private network data is roughly predicted, so that a relevant intervention method or improvement measures are taken in advance, but the accuracy rate of predicting the private network data is low. In order to solve the above problems, the present invention provides a method, an apparatus, a device and a storage medium for predicting private network data.
An exemplary application scenario of the embodiments of the present invention is described below.
In daily life, some important competitions, festivals, activities, etc. may occur, such as: under the conditions, users using the terminal may be concentrated, and further, the conditions that the traffic of a certain area is large, the telephone traffic frequency is increased, and the like are caused. Based on the method, the device, the equipment and the storage medium, the private network data are predicted.
Fig. 1 is a flowchart illustrating a method for predicting private network data according to an embodiment of the present invention, where the method may be performed by a device for predicting private network data, and the device may be implemented by software and/or hardware, for example: the device may be part or all of a terminal device, the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, or the like, and the following describes a method for predicting private network data with the terminal device as an execution subject, as shown in fig. 1, the method for predicting private network data provided in the embodiment of the present invention may include:
step S101: a plurality of historical private network data are obtained.
The terminal device obtains a plurality of historical private network data, which may be obtained by a Server, for example, a Home Subscriber Server (HSS), or a terminal device. In addition, the selection manner and the number of the plurality of historical private network data are not limited in the embodiment of the present invention, and for example, the historical private network data may be historical private network data for a certain area, such as a certain downtown area, a certain street, and the like, may be historical private network data for each department, such as a public security department, and the like, and may also be historical private network data for a certain group, such as students, white collars, blue collars, and the like. The historical private network data may be historical private network data in a recent period of time, for example, historical private network data in a recent year, historical private network data in a recent half year, and the like, which is not limited in this embodiment of the present invention.
The historical private network data may include a plurality of data or one data, and optionally, the historical private network data includes: one or both of historical traffic times and historical core network traffic. The embodiment of the invention does not limit the type of the historical private network data, for example, the historical private network data can also comprise the voice call times, the video call times, the voice call time, the video call time, the internet surfing time and the like of the user, and a plurality of historical private network data can be obtained according to actual requirements.
Step S102: and respectively establishing a plurality of models according to a plurality of historical private network data.
The multiple models may be decision tree regression models, neural network models, Autoregressive Integrated Moving Average models (ARIMA), and the like, and the embodiments of the present invention do not limit the types of the multiple models.
In order to implement the establishment of the plurality of models respectively according to the plurality of historical private network data, in a possible embodiment, the establishment of the plurality of models respectively according to the plurality of historical private network data includes:
and selecting the first historical private network data as a training set from the plurality of historical private network data. And aiming at any model, training the model according to the first historical private network data to obtain a trained model. And selecting second historical private network data as a test set from the plurality of historical private network data. And testing the trained model according to the second historical private network data aiming at any trained model to obtain a tested model.
Taking a plurality of historical private network data in the last year as an example, in the plurality of historical private network data in the last year, the first historical private network data is selected as a training set, and then, in a plurality of models, for any model, the model is trained according to the first historical private network data to obtain a trained model, wherein the first historical private network data may be historical private network data of previous months in the last year, for example: historical private network data for the first eight months of the last year, which is not limited by the present example. After the trained model is obtained, selecting second historical private network data from the plurality of historical private network data as a test set, and testing any trained model in the plurality of trained models to obtain a tested model, wherein the second historical private network data can be selected from data except the first historical private network data in the plurality of historical private network data, for example: the second historical private network data is historical private network data of four months after the last year, or the second historical private network data is historical private network data of 9 th to 10 th months in the last year, and the like. The multiple models are trained and tested respectively by using the multiple historical private network data, so that the multiple models are established.
Step S103: from the plurality of models, a target model is selected.
After the multiple models are respectively established according to the multiple historical private network data, an appropriate model can be selected from the multiple models as a target model according to user requirements, and in one possible implementation, the selecting the target model according to the multiple models includes:
obtaining the prediction accuracy of a plurality of models according to the tested models; and selecting the target model according to the prediction accuracy of the plurality of models.
In order to verify the prediction accuracy of the model, the tested model may be verified, for example, a part of the historical private network data is selected as a true value from the multiple pieces of historical private network data, the tested model is used to predict the part of the private network data to obtain a tested value of the part of the historical private network data, and the tested value and the true value of the part of the historical private network data are compared to obtain the accuracy of the model.
The target model is selected based on the prediction accuracy of the plurality of models, and for example, the model with the highest prediction accuracy may be selected as the target model. And selecting a target model according to the prediction accuracy of the plurality of models, and selecting a proper model as the target model by comprehensively considering the prediction accuracy, the complexity of model parameters and the time required by training the model.
Taking the example of respectively establishing a neural network model, a decision tree regression model and an ARIMA model for the core network traffic in private network data, and comparing the specific modes of selecting a target model according to a plurality of models.
Table 1 shows the advantages and disadvantages of the neural network model, the decision tree regression model, and the ARIMA model when used for predicting the core network traffic, as shown in table 1:
from the data in table 1, a target model is selected, for example, an ARIMA model may be selected if the user demand is that future trends can be predicted and the runtime is short, a neural network model may be selected if the user demand is that future trends and peaks can be predicted, and a decision tree regression model may be selected if the user demand is that future trends can be predicted and multithreading can be performed. For different private network data and different models, advantages, disadvantages and the like may be different, specifically, the target model is determined according to a plurality of models, and specific analysis may be performed according to specific situations.
Step S104: and predicting the private network data according to the target model.
According to the target model, the private network data is predicted, and the time required to be predicted can be input, such as: the private network data is predicted in a week, a month, a day, etc. in the future, which is not limited in the embodiment of the present invention.
In summary, the plurality of models are respectively established according to the plurality of historical private network data, and the target model is selected from the plurality of models, so that the private network data is predicted according to the target model, and the accuracy of predicting the private network data is improved.
Optionally, in order to improve the quality of multiple historical private network data and improve the accuracy of the model, fig. 2 is a flowchart of a private network data prediction method according to another embodiment of the present invention, which may be executed by a private network data prediction apparatus, and the apparatus may be implemented by software and/or hardware, for example: the apparatus may be part or all of a terminal device, the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, or the like, and the following describes a method for predicting private network data with the terminal device as an execution subject, as shown in fig. 2, the method for predicting private network data provided in the embodiment of the present invention may further include, before step S102:
step S201: and preprocessing the plurality of historical private network data to obtain a plurality of preprocessed historical private network data.
Optionally, the preprocessing the plurality of historical private network data may include sorting the historical private network data in a time sequence, for example: the number of times of the traffic is arranged according to a day and/or an hour, and the traffic of the core network is arranged according to a day and/or an hour, which is not limited in the embodiment of the present invention.
Optionally, the preprocessing is performed on the plurality of historical private network data, and may include cleaning abnormal data from the historical private network data, correcting error data, and the like, so as to improve the quality of the historical private network data. The method can also be used for preprocessing a plurality of historical private network data to preliminarily judge the rule of the historical private network data, and then a plurality of models are selected. For example: the multiple models are selected to be a decision tree regression model, a neural network model, an ARIMA and the like, and the specific types of the models are not limited in the embodiment of the invention.
Optionally, the preprocessing is performed on the plurality of historical private network data, and the time dimension of the historical private network data can be enriched. For example, from the time data of the historical private network data, it is determined whether the date is a holiday, if the date belongs to a holiday, it is also determined that the date is the next day of a consecutive holiday, and the like, in the morning, noon, afternoon, evening, or early morning, and the like, in the year, month, day of the year, and day of the week, and the time is in the morning, noon, afternoon, evening, or early morning, and the like, and the accuracy of model prediction can be improved by enriching the time dimension.
Accordingly, step S102 may include:
step S202: and respectively establishing a plurality of models according to the preprocessed plurality of historical private network data.
The manner of respectively establishing the multiple models according to the preprocessed multiple pieces of historical private network data is similar to the manner of respectively establishing the multiple models according to the multiple pieces of historical private network data in step S102, and reference may be specifically made to step S102, which is not described again.
In summary, the quality of the historical private network data is improved by preprocessing the historical private network data before the plurality of models are respectively established according to the historical private network data, and then the models are respectively established according to the preprocessed historical private network data, so that the accuracy of establishing the models is improved.
Optionally, fig. 3 is a flowchart illustrating a method for predicting private network data according to another embodiment of the present invention, where the method may be executed by a device for predicting private network data, and the device may be implemented by software and/or hardware, for example: the apparatus may be part or all of a terminal device, the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, or the like, and the following describes a method for predicting private network data with the terminal device as an execution subject, as shown in fig. 3, the method for predicting private network data according to the embodiment of the present invention, after step S104, may further include:
step S301: and pushing a prediction result for predicting the private network data to the user.
The prediction result of predicting the private network data may be a prediction result of every week and every day, a prediction condition of every time period every day, and the like, and the prediction result of predicting the private network data in the embodiment of the present invention is not limited. The terminal pushes the prediction result of predicting the private network data to the user, and the prediction result of predicting the private network data can be pushed to a terminal display screen in a form of a report.
The content and effect of the prediction apparatus, device, storage medium, and computer program product for private network data provided by the present invention can refer to the prediction method for private network data provided by the embodiment of the present invention, and are not described again.
The present invention provides a private network data prediction apparatus, and fig. 4 is a schematic structural diagram of a private network data prediction apparatus provided in an embodiment of the present invention, and the apparatus may be implemented by software and/or hardware, for example: the device may be part or all of a terminal device, and the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, or the like, as shown in fig. 4, the prediction device of private network data provided in the embodiment of the present invention may include:
an obtaining module 41, configured to obtain multiple pieces of historical private network data.
Optionally, the historical private network data includes:
historical traffic times and/or historical core network traffic.
And the establishing module 42 is used for respectively establishing a plurality of models according to the plurality of historical private network data.
Optionally, the establishing module 42 is specifically configured to:
and selecting the first historical private network data as a training set from the plurality of historical private network data.
And aiming at any model, training the model according to the first historical private network data to obtain a trained model.
And selecting second historical private network data as a test set from the plurality of historical private network data.
And testing the trained model according to the second historical private network data aiming at any trained model to obtain a tested model.
A selection module 43 for selecting the target model based on the plurality of models.
Optionally, the selecting module 43 is specifically configured to:
obtaining the prediction accuracy of a plurality of models according to the tested models; and selecting the target model according to the prediction accuracy of the plurality of models.
And the prediction module 44 is used for predicting the private network data according to the target model.
Optionally, fig. 5 is a schematic structural diagram of a prediction apparatus for private network data according to another embodiment of the present invention, which may be implemented by software and/or hardware, for example: the device may be part or all of a terminal device, and the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, or the like, as shown in fig. 5, the prediction device of private network data provided in the embodiment of the present invention may further include:
the preprocessing module 51 is configured to preprocess the multiple pieces of historical private network data to obtain multiple pieces of preprocessed historical private network data.
Correspondingly, the establishing module 42 is specifically configured to:
and respectively establishing a plurality of models according to the preprocessed plurality of historical private network data.
Optionally, fig. 6 is a schematic structural diagram of a prediction apparatus for private network data according to another embodiment of the present invention, which may be implemented by software and/or hardware, for example: the device may be part or all of a terminal device, and the terminal device may be a personal computer, a smart phone, a user terminal, a tablet computer, a wearable device, or the like, as shown in fig. 6, the prediction device of private network data provided in the embodiment of the present invention may further include:
and the pushing module 61 is configured to push a prediction result of predicting the private network data to the user.
An apparatus is provided in an embodiment of the present invention, fig. 7 is a schematic structural diagram of a terminal apparatus provided in an embodiment of the present invention, and as shown in fig. 7, the terminal apparatus provided in the embodiment of the present invention includes:
a processor 71, a memory 72, a transceiver 73 and a computer program; wherein the transceiver 73 enables data transmission between the server and other devices, a computer program is stored in the memory 72 and configured to be executed by the processor 71, the computer program comprising instructions for performing the above-mentioned prediction method for private network data, the contents and effects of which refer to the method embodiments.
An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program that enables a server to refer to the method for predicting private network data, the content and effect of which may refer to the method embodiment.
An embodiment of the present invention provides a computer program product, including: executable instructions for implementing the method for predicting private network data as described above, the contents and effects of which may be referred to method embodiments.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for predicting private network data is characterized by comprising the following steps:
acquiring a plurality of historical private network data;
respectively establishing a plurality of models according to the plurality of historical private network data;
selecting a target model according to the plurality of models;
and predicting the private network data according to the target model.
2. The method of claim 1, prior to building a plurality of models from the plurality of historical private network data, respectively, comprising:
preprocessing the plurality of historical private network data to obtain a plurality of preprocessed historical private network data;
correspondingly, the respectively establishing a plurality of models according to the plurality of historical private network data includes: and respectively establishing the plurality of models according to the plurality of preprocessed historical private network data.
3. The method of claim 2, wherein building a plurality of models from the plurality of historical private network data, respectively, comprises:
selecting first historical private network data from the plurality of historical private network data as a training set;
aiming at any model, training the model according to the first historical private network data to obtain a trained model;
selecting second historical private network data as a test set from the plurality of historical private network data;
and aiming at any trained model, testing the trained model according to the second historical private network data to obtain a tested model.
4. The method of claim 3, wherein selecting a target model from the plurality of models comprises:
obtaining the prediction accuracy of the plurality of models according to the tested models;
and selecting a target model according to the prediction accuracy of the plurality of models.
5. The method according to any of claims 1-4, further comprising, after predicting the private network data according to the objective model:
and pushing the prediction result of predicting the private network data to a user.
6. The method of claim 5, wherein the historical private network data comprises:
historical traffic times and/or historical core network traffic.
7. An apparatus for predicting private network data, comprising:
the acquisition module is used for acquiring a plurality of historical private network data;
the establishing module is used for respectively establishing a plurality of models according to the plurality of historical private network data;
a selection module for selecting a target model according to the plurality of models;
and the prediction module is used for predicting the private network data according to the target model.
8. The apparatus of claim 7, further comprising:
the preprocessing module is used for preprocessing the plurality of historical private network data to obtain a plurality of preprocessed historical private network data;
correspondingly, the establishing module is specifically configured to:
and respectively establishing the plurality of models according to the plurality of preprocessed historical private network data.
9. An apparatus, comprising:
a processor;
a memory; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor, the computer program comprising instructions for performing the method of any of claims 1-6.
10. A computer-readable storage medium, characterized in that it stores a computer program that causes a server to execute the method of any one of claims 1-6.
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| CN201910299739.XA CN111832780A (en) | 2019-04-15 | 2019-04-15 | Private network data prediction method, private network data prediction device, private network data prediction equipment and storage medium |
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| US20230007325A1 (en) * | 2020-12-02 | 2023-01-05 | SimpleBet, Inc. | Method and system for self-correcting match states |
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