Communication data processing method and device based on user behavior prediction and medium

Abstract

The application discloses a communication data processing method and device based on user behavior prediction and a medium, and can be applied to the technical field of communication. According to the application, signaling flow data is acquired from a core network element after a start analysis signal is acquired, a user behavior habit set is constructed according to the signaling flow data, then preheating user service data is constructed according to the user behavior habit set and first user service data, and the preheating user service data is sent to the core network element in a first preset time period, so that the core network element performs communication service according to a user communication request and the preheating user service data, thereby the number of times of acquiring user data from a user database can be reduced, and then communication data related to calls or short messages can be processed in time and accurately in multiple service scenes, and network resource utilization can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a communication data processing method and device based on user behavior prediction and medium. BACKGROUND

[0002] In the related art, the core network usually adopts a centralized user database storage mode. When a user initiates a call or short message request, the core network needs to send a query request to the user database to obtain the relevant information of the user, so as to continue the subsequent service process. This centralized data loading and query mode cannot timely and accurately process call or short message related communication data when facing large-scale concurrent access and complex and diverse service requirements, and a large number of core network resources need to be invested during the non-peak period, resulting in low network resource utilization.

[0003] In summary, the technical problems in the related art need to be improved. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a communication data processing method and device based on user behavior prediction and medium, which can timely and accurately process call or short message related communication data in multiple service scenarios and improve network resource utilization.

[0005] To achieve the above purpose, one aspect of the embodiments of the present application provides a communication data processing method based on user behavior prediction, which comprises the following steps:

[0006] Obtaining a start analysis signal;

[0007] Obtaining signaling flow data from a core network element according to the start analysis signal;

[0008] Constructing a user behavior habit set according to the signaling flow data;

[0009] Constructing preheating user service data according to the user behavior habit set and first user service data;

[0010] Sending the preheating user service data to the core network element in a first preset time period, so that the core network element performs communication service according to user communication request and the preheating user service data.

[0011] In some embodiments, the constructing a user behavior habit set according to the signaling flow data comprises:

[0012] Obtaining service history data in the signaling flow data and service time nodes and first user numbers corresponding to each piece of service history data;

[0013] According to the second preset time period and the service time node, a service timestamp is calculated;

[0014] According to the second user number and the first user number, the service history data is grouped to obtain a grouping result;

[0015] According to the grouping result, the second user number is screened to obtain a third user number;

[0016] According to the service timestamp, the service history data corresponding to each third user number is clustered to obtain a user behavior habit set.

[0017] In some embodiments, the screening of the second user number according to the grouping result to obtain a third user number comprises:

[0018] According to the grouping result, it is determined that the service times corresponding to the second user number are less than a times threshold, and the second user number is deleted;

[0019] According to the grouping result, it is determined that the service times corresponding to the second user number are greater than or equal to the times threshold, and the second user number is taken as the third user number.

[0020] In some embodiments, the clustering of the service history data corresponding to each third user number according to the service timestamp to obtain a user behavior habit set comprises:

[0021] According to the service timestamp, the service history data corresponding to each third user number is clustered to obtain a clustering cluster;

[0022] When the number of service history data in the clustering cluster is greater than or equal to a number threshold, the centroid time node of each clustering cluster is obtained;

[0023] The centroid time node is added to the corresponding user behavior habit set.

[0024] In some embodiments, the construction of the preheating user service data according to the user behavior habit set and the first user service data comprises:

[0025] The second preset time period is divided into a plurality of first preset time periods;

[0026] When the centroid time node in the user behavior habit set is located in the first preset time period, the first user service data of the third user number corresponding to the centroid time node is obtained;

[0027] The preheating user service data is constructed according to the third user number and the first user service data.

[0028] In some embodiments, the sending the pre-warmed user service data to the core network element in the first preset time period comprises:

[0029] When the timer counting is over, obtaining the pre-warmed user service data of the next first preset time period;

[0030] Obtaining address information of the core network element;

[0031] According to the address information, sending the pre-warmed user service data of the next first preset time period to the core network element.

[0032] In some embodiments, the communicating service according to the user communication request and the pre-warmed user service data comprises:

[0033] According to the user communication request, querying the pre-warmed user service data from the core network element preferentially;

[0034] According to the pre-warmed user service data, controlling the communication service of the current number.

[0035] To achieve the above object, another aspect of the embodiment of the present application proposes a communication data processing device based on user behavior prediction, which comprises:

[0036] A first module is configured to obtain a start analysis signal;

[0037] A second module is configured to obtain signaling stream data from a core network element according to the start analysis signal;

[0038] A third module is configured to construct a user behavior habit set according to the signaling stream data;

[0039] A fourth module is configured to construct pre-warmed user service data according to the user behavior habit set and first user service data;

[0040] A fifth module is configured to send the pre-warmed user service data to the core network element in a first preset time period, so that the core network element performs communication service according to a user communication request and the pre-warmed user service data.

[0041] To achieve the above object, another aspect of the embodiment of the present application proposes a computer device, which comprises:

[0042] At least one processor;

[0043] At least one memory for storing at least one program;

[0044] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0045] To achieve the above object, another aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above method.

[0046] The embodiment of the present application at least has the following beneficial effects: the present application provides a communication data processing method and device based on user behavior prediction and medium, the scheme obtains signaling flow data from the core network element after obtaining the start analysis signal, constructs the user behavior habit set according to the signaling flow data, then constructs the preheating user service data according to the user behavior habit set and the first user service data, and sends the preheating user service data to the core network element in the first preset time period, so that the core network element performs communication service according to the user communication request and the preheating user service data, thereby the number of times of obtaining user data from the user database can be reduced, and the communication data related to calls or short messages can be processed in time and accurately in multiple service scenarios, and the network resource utilization rate is improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a flowchart of the communication data processing method based on user behavior prediction provided by the embodiment of the present application;

[0048] Figure 2 is an interaction flowchart of the communication data processing method based on user behavior prediction provided by the embodiment of the present application;

[0049] Figure 3 is a module schematic diagram of the user habit analysis application server provided by the embodiment of the present application;

[0050] Figure 4 is a whole flowchart of the preheating user service data generated by the user habit analysis application server provided by the embodiment of the present application;

[0051] Figure 5 is an update flowchart of the preheating user service data in the core network element provided by the embodiment of the present application;

[0052] Figure 6 is a flowchart of the core network query data provided by the embodiment of the present application;

[0053] Figure 7 is a structure schematic diagram of the communication data processing device based on user behavior prediction provided by the embodiment of the present application;

[0054] Figure 8 is a hardware structure schematic diagram of the computer device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application. When the following description refers to the accompanying drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the examples of the present application. They are only examples of devices and methods consistent with some aspects of the examples of the present application.

[0056] It can be understood that the terms "first", "second" and the like used in the present application can be used herein to describe various concepts, but unless specifically stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the examples of the present application, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "when" or "in response to determining".

[0057] The terms "at least one", "multiple", "each", "any" and the like used in the present application include one, two or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the examples of the present application, and are not intended to limit the present application.

[0059] Before the examples of the present application are described in detail, first, some nouns and terms involved in the examples of the present application are described, and the nouns and terms involved in the examples of the present application are applicable to the following explanations:

[0060] HSS (Home Subscriber Server, User Home Server) is a core network element in a mobile communication network. It is mainly responsible for storing and managing user's personal information and authentication data, supporting mobile management, call and session creation functions.

[0061] UHAAS (User habit analysis application server) is used to analyze the behavior habit information of user communication.

[0062] A DB (Database) is a repository that organizes, stores, and manages data according to a data structure. There are many types of databases, from simple tables that store various data to large database systems that can store massive amounts of data, and they are widely used in various fields. In a 5G network, a database can be used to store and manage network configuration information, user data, traffic data, network status information, and the like.

[0063] Clustering is a data analysis technique that divides objects in a dataset into multiple subsets, called clusters or categories, based on their features or attributes. Objects within the same cluster have a high degree of similarity with each other, but a lower degree of similarity with objects in other clusters. The key in clustering is a unique identifier column in the database, which is a combination of one or more columns in the database table, used to uniquely identify each row of data in the table. The centroid in clustering represents the "average position" of a certain cluster in the dataset. Specifically, the centroid is the mean of all data points in the cluster, and is usually used to represent the center of the cluster.

[0064] A 5GC (5G Core Network) is the core of a 5G mobile network. It establishes reliable and secure network connections for end users and provides access to their services. The core domain handles various basic functions in a mobile network, such as connectivity and mobility management, identity verification and authorization, user data management, and policy management. The 5G core network functions are fully software-based and designed as cloud-native, meaning they are independent of the underlying cloud infrastructure, enabling higher deployment agility and flexibility.

[0065] MSISDN (Mobile Subscriber International ISDN / PSTN number) is a telephone number used to identify mobile users in GSM (Global System for Mobile Communications) networks. MSISDN is similar to the PSTN (Public Switched Telephone Network) number in fixed networks, which is used to uniquely identify mobile users in the public telephone network numbering plan.

[0066] In related technologies, with the rapid development of Internet technology, mobile communication networks have undergone a transformation from voice calls to high-speed data transmission today, becoming an indispensable part of modern society. In particular, the popularity of 5G technology not only significantly improves data transmission speed, but also provides strong support for multiple frontier fields such as the Internet of Things and autonomous driving. However, even today, when data services are increasingly dominant, traditional calls and text messaging services remain an important part of users' daily communication. In the face of the rapid growth of user numbers and the diversification of service needs, how to effectively manage and optimize network resources and improve the quality of service for calls and text messaging services has become a major challenge for operators.

[0067] In the existing mobile communication network architecture, the core network is a key link connecting the radio access network and the external network, and undertakes important functions such as user authentication, authorization, charging and session management. The traditional core network design usually adopts a centralized user database storage mode. When a user initiates a call or short message request, the core network needs to send a query request to the user database to obtain the user's relevant information, and then continue the subsequent service process. This centralized data loading and query mode cannot timely and accurately process call or short message related communication data when facing large-scale concurrent access and complex and diverse service requirements, and a large amount of core network resources need to be invested during the non-peak period, resulting in low network resource utilization.

[0068] Therefore, in the embodiments of the present application, a communication data processing method and device based on user behavior prediction and a medium are provided. The present application obtains signaling flow data from the core network element after obtaining the start analysis signal, constructs a user behavior habit set according to the signaling flow data, then constructs preheating user service data according to the user behavior habit set and the first user service data, and sends the preheating user service data to the core network element at a first preset time period, so that the core network element performs communication service according to the user communication request and the preheating user service data, thereby reducing the number of times of obtaining user data from the user database, and further timely and accurately processing call or short message related communication data in multiple service scenarios and improving network resource utilization.

[0069] The communication data processing method based on user behavior prediction provided by the present application will be described in detail below in combination with specific drawings:

[0070] Figure 1 is an optional flowchart of the communication data processing method based on user behavior prediction provided by the embodiments of the present application, Figure 1 The method in can include but is not limited to including steps S110 to S150:

[0071] Step S110, obtaining a start analysis signal;

[0072] Step S120, obtaining signaling flow data from the core network element according to the start analysis signal;

[0073] Step S130, constructing a user behavior habit set according to the signaling flow data;

[0074] Step S140, constructing preheating user service data according to the user behavior habit set and the first user service data;

[0075] Step S150, sending the preheating user service data to the core network element at a first preset time period, so that the core network element performs communication service according to the user communication request and the preheating user service data.

[0076] It is understood that when the method of the embodiments of this application is applied... Figure 2 In the interactive scenario shown, when the User Habit Analysis Application Server (UHAAS) is working, UHAAS receives signaling stream data from the core network elements in real time. Upon reaching a predetermined time, UHAAS uses machine learning algorithms to analyze user behavior habits and categorizes them by time. Subsequently, UHAAS initiates a user data request to the user's home server (HSS) to obtain user service data and stores it in a pre-warmed user database. This data is then transmitted to the core network elements at preset time intervals, providing them with priority access for querying. This accelerates data retrieval efficiency and enables timely and accurate processing of call or SMS-related communication data across multiple service scenarios.

[0077] like Figure 3 As shown, the user habit analysis application server includes a central controller, a data acquisition module, a signaling database, a data analysis module, and a waste heat user database. The interaction process between the user habit analysis application server and core network elements is shown in Table 1.

[0078] Table 1

[0079]

[0080] In this embodiment of the application, the process of constructing a user behavior habit set based on signaling stream data includes, but is not limited to, the following steps:

[0081] Step S210: Obtain the service history data in the signaling stream data, as well as the service time node and first user number corresponding to each service history data;

[0082] Step S220: Calculate the service timestamp based on the second preset time period and service time node;

[0083] Step S230: Group the business history data according to the second user number and the first user number to obtain the grouping results;

[0084] Step S240: Filter the second user number according to the grouping results to obtain the third user number;

[0085] Step S250: Cluster the business history data corresponding to each third user number according to the service timestamp to obtain the user behavior habit set.

[0086] It can be understood that, since each signaling flow data contains service history data and the service time node and the communication number (the first user number) corresponding to each service history data, the embodiment can calculate the service timestamp of each service history data based on the service time node, thereby improving the efficiency of subsequent processing. At the same time, the service history data is filtered according to the telephone number (the second user number) currently required for user habit analysis, thereby filtering out user numbers that do not exist or rarely communicate, and obtaining user numbers (the third user number) that exist multiple times of communication, and then clustering the service history data corresponding to each third user number according to the service timestamp, thereby obtaining a set containing user behavior habits.

[0087] Specifically, when the second user number is filtered according to the grouping result, if the service times corresponding to the second user number are less than the number threshold according to the grouping result, the second user number is deleted; if the service times corresponding to the second user number are greater than or equal to the number threshold according to the grouping result, the second user number is taken as the third user number.

[0088] When the service history data corresponding to each third user number is clustered according to the service timestamp, the service history data corresponding to each third user number is clustered to obtain a clustering cluster according to the service timestamp; when the number of service history data in the clustering cluster is greater than or equal to the number threshold, the centroid time node of each clustering cluster is obtained, and the centroid time node is added to the corresponding user behavior habit set.

[0089] It can be understood that, after obtaining the user behavior habit set, the preheating user service data can be constructed according to the user behavior habit set and the first user service data. Specifically, the construction process can be that the second preset time period is divided into a plurality of first preset time periods, and when the centroid time node in the user behavior habit set is located in the first preset time period, the first user service data of the third user number corresponding to the centroid time node is obtained, and then the preheating user service data is constructed according to the third user number and the first user service data.

[0090] Exemplarily, as shown in Figure 4 , when the data analysis module in the user habit analysis application server generates the preheating user service data, the following steps are included but not limited to:

[0091] Step one, receiving the start analysis signal initiated by the central controller, and starting to enter the analysis process;

[0092] Step two, extracting the service time node of each signaling flow data, and classifying the signaling flow data into each user group according to the url of the calling number;

[0093] Step three, pre-processing service timestamp: pre-process all signaling stream data according to a second preset time period C, map the signaling stream data into the C time period, and calculate the service timestamp t=t%C;

[0094] Step four, traverse the service history data in the signaling stream data in each user group;

[0095] Step five, check whether the service number of a user number (second user number) is less than a threshold 1, if yes, discard the user number;

[0096] Step six, K-Means clustering: K-Means clustering is performed on the service history data according to the service timestamp; specifically, the user time data is converted into a triple: (Day, Hour, Minute); clustering is performed with the service timestamp as the key, K data are randomly selected as the centroid, K initial groups are obtained, and then each service history data is assigned to the K groups, and the grouping rule is to group the service history data to the group closest to the centroid:

[0097] i=argmin i d(t,t i ), i∈{1,2,3,...,K};

[0098] Wherein:

[0099]

[0100] Update the group centroid:

[0101]

[0102] Wherein, |s i | is the number of elements in group i;

[0103] Recycle the grouping and update the centroid until the centroid changes are less than the threshold, exit the clustering process, and obtain multiple clustering clusters;

[0104] Step seven, traverse each clustering cluster;

[0105] Step eight, check whether the size of the data in the clustering cluster is less than a threshold 2, if yes, discard the clustering cluster; otherwise, take the clustering cluster as a target cluster;

[0106] Step nine, obtain the centroid time node of the target cluster;

[0107] Step ten, add all centroid time nodes as labels to the user habit set respectively;

[0108] Step eleven, check if all target clusters have been traversed, if not, return to step eight to continue to traverse the next target cluster; if yes, execute step twelve;

[0109] Step twelve, check if the size of the user behavior habit set is 0, if yes, discard the user behavior habit set, otherwise, execute step thirteen;

[0110] Step thirteen, check if all business history data have been traversed, if not, return to continue step five to traverse the business history data;

[0111] Step fourteen, divide the second preset time period C into first preset time periods with length T;

[0112] Step fifteen, traverse each first preset time period;

[0113] Step sixteen, extract user service data in the current first preset time period from the user behavior habit set;

[0114] Step seventeen, construct the preheat user service data of the current first preset time period according to the extracted user service data;

[0115] Step eighteen, check if all first preset time periods have been traversed, if not, execute step sixteen.

[0116] From the above, it can be seen that the embodiment of the application can obtain a user behavior habit set according to the user's business history data analysis, and can further provide data support for fast query for subsequent real-time communication.

[0117] In the embodiment of the application, when the user habit analysis application server completes user behavior habit analysis and obtains preheat user service data, the preheat user service data is sent to the core network element in the first preset time period to update the preheat user service data in the core network element, to meet the real-time data requirement. It can be understood that, as shown in Figure 5 When the preheat data timer counts over time, the preheat user service data expected to initiate a service request in the next first preset time period T is queried and obtained, and the address information of the core network element is obtained, the preheat user service data of the next first preset time period T is sent to the core network element according to the address information to update the preheat user service data in the core network element, and then the preheat data timer is restarted for re-counting, and the preheat user service data is checked and updated again in the next period.

[0118] In the embodiment of the application, when the core network completes the update of the preheat user service data, as shown in Figure 6As shown, after receiving a user-initiated user communication request, the data in the pre-warmed user data area of the query network element is queried. Specifically, the pre-warmed user service data in the core network element is preferentially queried. When the pre-warmed user service data is queried from the pre-warmed user data area of the core network element, the communication service of the current number is controlled according to the pre-warmed user service data; when the pre-warmed user service data is not queried from the pre-warmed user data area of the core network element, the static data area data of all users stored in the memory of the core network element is queried, and the communication service of the current number is controlled according to the static data area data.

[0119] Through the above, the embodiment preferentially queries the pre-warmed user service data from the pre-warmed user data area of the core network element, thereby reducing the number of times of obtaining user data from the user database, effectively improving the timeliness and accuracy of communication data processing related to calls or messages in multiple scenarios, and improving the utilization of network resources.

[0120] In summary, the method of the embodiment of the application solves the corresponding technical problems through the following processing process:

[0121] By analyzing the user's historical communication behavior to pre-load service data, the user waiting time can be reduced, and the service response speed can be improved;

[0122] Through the dynamic management mechanism, it is ensured that the pre-warmed data area always contains the user data that is most likely to be requested, thereby reducing the query delay and improving the response speed of the service and the user experience;

[0123] By utilizing the close cooperation between UHAAS and the core network element, real-time analysis of user data and rapid updating of pre-warmed data are realized, thereby ensuring efficient use of network resources and improving service quality;

[0124] By mapping the user service time data into a specific time period and calculating the service timestamp, the user's future behavior pattern can be more accurately predicted, thereby helping network operators better understand and predict user behavior, and optimizing the query process;

[0125] By analyzing the user's historical service time data, the user's behavior pattern is identified and added to the user habit set as a label to guide the data loading of the pre-warmed data area, ensuring that the network can provide personalized services according to user habits;

[0126] By dividing the time period into smaller time periods and performing correlation analysis with user data, and analyzing the user behavior in each time period, the probability of the user initiating a request in a specific time period can be more accurately predicted, thereby optimizing the data loading of the pre-warmed data area;

[0127] By automatically updating the preheating user data area of the core network element according to the preset time interval, the real-time and accuracy of the preheating data are ensured, the manual intervention is reduced, and the efficiency and stability of the network service are improved.

[0128] Therefore, the method of the embodiment of the application can achieve the following technical effects:

[0129] First, by predicting user behavior, the data of high-probability service users is dynamically preloaded into the preheating data area of the network element, effectively reducing the query time, improving the response speed, ensuring that user requests can be quickly processed, and thus maintaining the stability and reliability of the service in a high-concurrency scenario.

[0130] Second, through real-time analysis of the user habit analysis application server (UHAAS), the characteristics of different user groups can be identified, and the data query process can be optimized according to these characteristics. For example, for commuters who often make calls during peak hours in the morning and evening, their data is preloaded into the preheating user storage area, and the data in the preheating user storage area is preferentially queried during communication, thereby reducing the risk of wasting the service quality of users by low-probability service users, and effectively improving the user experience.

[0131] Reference Figure 7 The embodiment of the application provides a communication data processing device based on user behavior prediction, and the device comprises:

[0132] The first module 710 is configured to acquire a start analysis signal.

[0133] The second module 720 is configured to acquire signaling flow data from the core network element according to the start analysis signal.

[0134] The third module 730 is configured to construct a user behavior habit set according to the signaling flow data.

[0135] The fourth module 740 is configured to construct preheating user service data according to the user behavior habit set and the first user service data.

[0136] The fifth module 750 is configured to send the preheating user service data to the core network element at a first preset time period, so that the core network element performs communication service according to the user communication request and the preheating user service data.

[0137] It can be understood that the contents in the above method embodiments are applicable to the device embodiments, the device embodiments specifically realize the same functions as the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0138] The embodiment of the present application further provides a computer device, which comprises a memory and a processor. The memory stores a computer program, and the processor implements the communication data processing method based on user behavior prediction when executing the computer program. The computer device can be any intelligent terminal, such as a tablet computer or a vehicle-mounted computer.

[0139] It can be understood that the content in the method embodiments is applicable to the device embodiments, the device embodiments specifically implement the functions of the method embodiments, and achieve the same beneficial effects as the method embodiments.

[0140] Please refer to Figure 8 , Figure 8 The hardware structure of the computer device of another embodiment is illustrated, and the computer device comprises:

[0141] The processor 810 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0142] The memory 820 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 820 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 820 and are called and executed by the processor 810 to implement the communication data processing method based on user behavior prediction.

[0143] The input / output interface 830 is used to realize information input and output.

[0144] The communication interface 840 is used to realize the communication interaction between the device and other devices. The communication can be realized in a wired manner (for example, a USB, a network cable, etc.) or in a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0145] The bus 850 transmits information between various components (for example, the processor 810, the memory 820, the input / output interface 830, and the communication interface 840) of the device.

[0146] The processor 810, the memory 820, the input / output interface 830, and the communication interface 840 are communicatively connected with each other through a bus 850.

[0147] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the communication data processing method based on user behavior prediction.

[0148] It can be understood that the contents in the above method embodiments are all applicable to the present storage medium embodiment, the present storage medium embodiment specifically implements the functions of the above method embodiments, and achieves the same beneficial effects as the above method embodiments.

[0149] The memory is a non-transitory computer readable storage medium, and can be used to store a non-transitory software program and a non-transitory computer executable program. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, for example, at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and the remote memory can be connected to the processor through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0150] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0151] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures, or combine certain steps, or different steps.

[0152] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0153] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0154] The terms "first", "second", "third", "fourth", and the like in the description and in the claims of this application, 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 use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on circuitry availability, based on stated preference or the like, and that "default" or other orderings are thus permissible. Further, the terms "comprise", "comprising", "include", "including", and the like, are specifically intended to be open-ended. That is, references to individual steps and the like do not suhstantially exclude the presence of two or more of a given step or its integral presence in the process, method, system, article, or apparatus having been made with a wider scope. The use of notation such as "first", "second", "third", etc. does not generally limit the areas, but can be used for clarity, and merely establishes the order unless otherwise stated below.

[0155] It should be understood that, in the application, "at least one" means one or more, and "multiple" means two or more. "And / or" is used to describe the relationship between associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that there are only A, only B, and A and B at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0156] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed objects can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0157] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the application.

[0158] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0159] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions used to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various other media that can store programs.

[0160] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not intended to limit the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A communication data processing method based on user behavior prediction, characterized in that, The method comprises the following steps: acquiring a start analysis signal; acquiring signaling flow data from a core network element according to the start analysis signal; constructing a user behavior habit set according to the signaling flow data; constructing preheating user service data according to the user behavior habit set and first user service data; sending the preheating user service data to the core network element in a first preset time period, so that the core network element performs communication service according to a user communication request and the preheating user service data; wherein the communication service according to the user communication request and the preheating user service data comprises: preferentially querying the preheating user service data from the core network element according to the user communication request; controlling the communication service of a current number according to the preheating user service data.

2. The method of claim 1, wherein, The construction of the user behavior habit set according to the signaling flow data comprises: acquiring service history data in the signaling flow data and a service time node and a first user number corresponding to each piece of service history data; calculating a service timestamp according to a second preset time period and the service time node; grouping the service history data according to a second user number and the first user number to obtain a grouping result; screening the second user number according to the grouping result to obtain a third user number; clustering the service history data corresponding to each third user number according to the service timestamp to obtain a user behavior habit set.

3. The method of claim 2, wherein, The screening of the second user number according to the grouping result to obtain a third user number comprises: determining that the service number corresponding to the second user number is less than a number threshold according to the grouping result, and deleting the second user number; determining that the service number corresponding to the second user number is greater than or equal to the number threshold according to the grouping result, and taking the second user number as the third user number.

4. The method of claim 2, wherein, The clustering of the service history data corresponding to each third user number according to the service timestamp to obtain a user behavior habit set comprises: clustering the service history data corresponding to each third user number according to the service timestamp to obtain a clustering cluster; when the number of service history data in the clustering cluster is greater than or equal to a number threshold, acquiring a centroid time node of each clustering cluster; adding the centroid time node to the corresponding user behavior habit set.

5. The method of claim 2, wherein, The construction of preheating user service data according to the user behavior habit set and first user service data comprises: dividing the second preset time period into a plurality of first preset time periods; when there is a centroid time node in the user behavior habit set located in the first preset time period, acquiring first user service data of the third user number corresponding to the centroid time node; constructing the preheating user service data according to the third user number and the first user service data.

6. The method of claim 1, wherein, The sending of the preheating user service data to the core network element in a first preset time period comprises: when the timer count is overdue, acquiring the preheating user service data of the next first preset time period; obtain address information of the core network element; send the preheat user service data of the next first preset time period to the core network element according to the address information.

7. A communication data processing apparatus based on user behavior prediction, characterized by, The apparatus comprises: a first module for obtaining a start analysis signal; a second module for obtaining signaling stream data from a core network element according to the start analysis signal; a third module for constructing a user behavior habit set according to the signaling stream data; a fourth module for constructing preheat user service data according to the user behavior habit set and first user service data; a fifth module for sending the preheat user service data to the core network element in a first preset time period, so that the core network element performs communication service according to a user communication request and the preheat user service data; wherein the performing communication service according to a user communication request and the preheat user service data comprises: preferentially querying the preheat user service data from the core network element according to the user communication request; controlling communication service of a current number according to the preheat user service data.

8. A computer apparatus, comprising: comprise: at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor implements the method in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the method in any one of claims 1 to 6.

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