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

Abstract

The invention 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, after the analysis starting signal is acquired, the signaling stream data is acquired from the core network element, the user behavior habit set is constructed according to the signaling stream data, and then the preheating user service data is constructed according to the user behavior habit set and the first user service data; and sending 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 reducing the number of times of obtaining user data from a user database; therefore, communication data related to conversation or short messages can be timely and accurately processed in a plurality of service scenes, and the utilization rate of network resources is improved.

Description

Technical Field

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

[0002] In the related technology, the core network usually adopts a centralized user database storage method. When a user initiates a call or SMS request, the core network needs to send a query request to the user database to obtain relevant information of the user before continuing the subsequent service process. This centralized data loading and query method cannot process call or SMS related communication data in a timely and accurate manner when faced with large-scale concurrent access and complex and diverse service requirements. In addition, a large amount of core network resources are required during non-peak hours, resulting in low network resource utilization.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

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

[0005] To achieve the above object, an embodiment of the present application provides a communication data processing method based on user behavior prediction, the method comprising the following steps:

[0006] Get the start analysis signal;

[0007] Acquiring 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 the first user service data;

[0010] The preheating user service data is sent to the core network element at a first preset time period, so that the core network element performs communication services according to the user communication request and the preheating user service data.

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

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

[0013] Calculate a service timestamp according to a second preset time period and the service time node;

[0014] Grouping the service history data according to the second user number and the first user number to obtain a grouping result;

[0015] Filter the second user number according to the grouping result to obtain a third user number;

[0016] The service history data corresponding to each of the third user numbers is clustered according to the service timestamp to obtain a user behavior habit set.

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

[0018] Determining, according to the grouping result, that the number of services corresponding to the second user number is less than a number threshold, and deleting the second user number;

[0019] Determine, according to the grouping result, that the number of services corresponding to the second user number is greater than or equal to the number threshold, and use the second user number as the third user number.

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

[0021] Clustering the service history data corresponding to each of the third user numbers according to the service timestamp to obtain a cluster;

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

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

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

[0025] dividing the second preset time period into a plurality of the first preset time periods;

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

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

[0028] In some embodiments, sending the preheating user service data to the core network element in a first preset time period includes:

[0029] When the timer times out, obtaining the preheating user service data for the next first preset time period;

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

[0031] The preheating user service data of the next first preset time period is sent to the core network element according to the address information.

[0032] In some embodiments, performing communication services according to the user communication request and the preheated user service data includes:

[0033] Prioritizing querying the preheating user service data from the core network element according to the user communication request;

[0034] The communication service of the current number is controlled according to the preheated user service data.

[0035] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a communication data processing device based on user behavior prediction, the device comprising:

[0036] The first module is used to obtain a start analysis signal;

[0037] The second module is used to obtain signaling flow data from the core network element according to the start analysis signal;

[0038] The third module is used to construct a user behavior habit set based on the signaling flow data;

[0039] A fourth module is used to construct preheating user service data according to the user behavior habit set and the first user service data;

[0040] The fifth module is used to send the preheating user service data to the core network element in a first preset time period, so that the core network element performs communication services according to the user communication request and the preheating user service data.

[0041] To achieve the above objective, another aspect of the present application provides a computer device, including:

[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 objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0046] The embodiments of the present application include at least the following beneficial effects: The present application provides a communication data processing method, device and medium based on user behavior prediction. The scheme obtains signaling flow data from the core network element after obtaining a start analysis signal, constructs a user behavior habit set according to the signaling flow data, and 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 in a first preset time period, so that the core network element performs communication services according to the user communication request and the preheating user service data, thereby reducing the number of times user data is obtained from the user database, and then can timely and accurately process call or text message related communication data in multiple service scenarios and improve network resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of a communication data processing method based on user behavior prediction provided in an embodiment of the present application;

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

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

[0050] Figure 4 It is an overall flow chart of the user habit analysis application server generating preheating user service data provided by the embodiment of the present application;

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

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

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

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

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

[0056] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, 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 embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0057] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, 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 those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0059] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations:

[0060] HSS (Home Subscriber Server) is a core network element in mobile communication networks. It is mainly responsible for storing and managing users' personal information and authentication data, and supports functions such as mobility management, call and session creation.

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

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

[0063] Clustering is a data analysis technique used to divide objects in a data set into multiple subsets based on their characteristics or attributes. These subsets are called clusters or categories. Objects in the same cluster have high similarity to each other, but have low similarity to objects in other clusters. The key in clustering is the unique identifier column in the database, which is a combination of one or more columns in the database table that uniquely identifies each row of data in the table. The centroid in clustering represents the "average position" of a cluster in the data set. 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] 5GC (5G Core Network) is the core of the 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 mobile networks, such as connectivity and mobility management, authentication and authorization, user data management, and policy management. 5G core network functions are completely software-based and designed to be cloud-native, which means 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 the GSM (Global System for Mobile Communications) network. MSISDN is similar to the PSTN (Public Switched Telephone Network) number in fixed networks and is used to uniquely identify mobile users in the public telephone network switching network numbering plan.

[0066] In related technologies, with the rapid development of Internet technology, mobile communication networks have undergone a transformation from the initial voice calls to today's high-speed data transmission, becoming an indispensable part of modern society. In particular, the popularization of 5G technology has not only significantly improved the speed of data transmission, but also provided strong support for many cutting-edge fields such as the Internet of Things and autonomous driving. However, even today when data services are increasingly dominant, traditional call and SMS services are still an important part of users' daily communications. Faced with the surge in the number of users and the diversification of service demands, how to effectively manage and optimize network resources and improve the service quality of call and SMS services has become a major challenge facing operators.

[0067] In the existing mobile communication network architecture, the core network, as the key link between the wireless access network and the external network, undertakes important functions such as user authentication, authorization, billing and session management. Traditional core network designs usually adopt a centralized user database storage method. When a user initiates a call or SMS request, the core network needs to send a query request to the user database to obtain relevant information about the user before continuing the subsequent service process. This centralized data loading and query method cannot process call or SMS related communication data in a timely and accurate manner when faced with large-scale concurrent access and complex and diverse service requirements. In addition, a large amount of core network resources are required during non-peak hours, resulting in low network resource utilization.

[0068] In view of this, a communication data processing method, device and medium based on user behavior prediction are provided in an embodiment of the present application. The present application obtains signaling flow data from a core network element after obtaining a start analysis signal, constructs a user behavior habit set according to the signaling flow data, and 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 services according to the user communication request and the preheating user service data, thereby reducing the number of times user data is obtained from a user database, and then can timely and accurately process call or text message related communication data in multiple service scenarios and improve network resource utilization.

[0069] The following is a detailed description of the communication data processing method based on user behavior prediction provided by the present application in conjunction with the specific drawings:

[0070] Figure 1 is an optional flowchart of a communication data processing method based on user behavior prediction provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S150:

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

[0072] Step S120: acquiring signaling flow data from a core network element according to a start analysis signal;

[0073] Step S130: construct 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: 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 services according to the user communication request and the preheating user service data.

[0076] It is understandable that when the method of the embodiment of the present application is applied to Figure 2 In the interactive scenario shown, when the User Habit Analysis Application Server (UHAAS) is working, UHAAS receives signaling flow data from the core network element in real time. When the scheduled time is reached, UHAAS uses machine learning algorithms to analyze the user's behavior habits and classify them by time. Then UHAAS initiates a user data application request to the User Home Server (HSS) to obtain user service data, and stores the user service data in the preheated user database, and then transmits it to the core network element at a preset time period, and provides it to the core network element for priority query, thereby speeding up data query efficiency and processing call or text message related communication data in a timely and accurate manner in multiple service scenarios.

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

[0078] Table 1

[0079]

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

[0081] Step S210: Acquire service history data in the signaling flow data and the service time node and the first user number corresponding to each piece of service history data;

[0082] Step S220, calculating a service timestamp according to a second preset time period and a service time node;

[0083] Step S230: grouping the service history data according to the second user number and the first user number to obtain a grouping result;

[0084] Step S240: Screen the second user number according to the grouping result to obtain a third user number;

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

[0086] It can be understood that, since each signaling flow data includes service history data and the service time node and communication number (first user number) corresponding to each service history data, the present 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 analyzed according to the telephone number (second user number) for which user habit analysis is currently required, so that user numbers with no communication times or almost no communication can be filtered out, and user numbers with multiple communications (third user numbers) are obtained, and then the service history data corresponding to each third user number is clustered according to the service timestamp, so that a set containing user behavior habits can be obtained.

[0087] Specifically, when the second user number is screened according to the grouping results, this embodiment can delete the second user number when it is determined according to the grouping results that the number of services corresponding to the second user number is less than the number threshold, and use the second user number as the third user number when it is determined according to the grouping results that the number of services corresponding to the second user number is greater than or equal to the number threshold.

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

[0089] It is understandable that, after obtaining the user behavior habit set, the present embodiment can construct the preheating user service data according to the user behavior habit set and the first user service data. Specifically, the construction process can be to divide the second preset time period into a plurality of first preset time periods, and when there is a centroid time node in the user behavior habit set that is within the first preset time period, obtain the first user service data of the third user number corresponding to the centroid time node, and then construct the preheating user service data according to the third user number and the first user service data.

[0090] For example, Figure 4 As shown, when the data analysis module in the user habit analysis application server generates preheating user service data, it includes but is not limited to the following steps:

[0091] Step 1: Receive the start analysis signal initiated by the central controller and start the analysis process;

[0092] Step 2: extract the service time node of each signaling flow data, and classify the signaling flow data into each user group according to the URL of the calling number;

[0093] Step 3: Preprocessing service timestamp: preprocessing all signaling flow data according to the second preset time period C, mapping the signaling flow data into the C time period, and calculating the service timestamp t=t%C;

[0094] Step 4: traverse the service history data in the signaling flow data in each user group;

[0095] Step 5: Check whether the service times of a certain user number (second user number) is less than a threshold value 1. If yes, discard the user number;

[0096] Step 6, K-Means clustering: Perform K-Means clustering on the business history data according to the service timestamp; specifically, convert the user time data into triples: (Day, Hour, Minute); cluster with the business service timestamp as the key, randomly select K data as the centroid, obtain K initial groups, and then assign each business history data to K groups. The grouping rule is to group the business history data into the group closest to the centroid:

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

[0098] in:

[0099]

[0100] Update group centroids:

[0101]

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

[0103] The clustering process is then exited by regrouping and updating the centroids in a cycle until the centroid changes are all less than the threshold, and multiple clusters are obtained.

[0104] Step 7: Traverse each cluster;

[0105] Step 8: Check whether the size of the data in the cluster is less than the threshold 2. If so, discard the cluster; otherwise, use the cluster as the target cluster;

[0106] Step 9: Obtain the centroid time node of the target cluster;

[0107] Step 10: Add all centroid time nodes as labels to the user habit set;

[0108] Step 11: Check whether all target clusters have been traversed. If not, return to step 8 to continue traversing the next target cluster; if yes, execute step 12;

[0109] Step 12: Check whether the size of the user behavior habit set is 0. If so, discard the user behavior habit set. Otherwise, execute step 13.

[0110] Step 13: Check whether all business history data has been traversed. If not, return to step 5 to traverse the business history data.

[0111] Step 14: Divide the second preset time period C into first preset time periods of length T;

[0112] Step 15: traverse each first preset time period;

[0113] Step 16: extracting user service data whose tags are included in the current first preset time period in the user behavior habit set;

[0114] Step 17: construct the preheating user service data of the first preset time period of this article according to the extracted user service data;

[0115] Step 18: Check whether all first preset time periods have been traversed. If not, proceed to step 16.

[0116] From the above content, it can be seen that the embodiment of the present application can obtain a user behavior habit set based on the user's business history data analysis, and then provide data support for rapid query for subsequent real-time communications.

[0117] In the embodiment of the present application, after the user habit analysis application server completes the user behavior habit analysis and obtains the preheating user service data, the preheating user service data is sent to the core network element at a first preset time period to update the preheating user service data in the core network element to meet the data real-time requirements. It can be understood that if Figure 5 As shown, when the preheating data timer counts up, the preheating user service data for the service request expected to be initiated within the next first preset time period T is queried and obtained, and the address information of the core network network element is obtained. According to the address information, the preheating user service data for the next first preset time period T is sent to the core network network element to update the preheating user service data in the core network network element, and then the preheating data timer is restarted to recount, waiting for the next period to check and update the preheating user service data again.

[0118] In the embodiment of the present application, after the core network completes the update of the preheating user service data, such as Figure 6As shown, after receiving a user communication request initiated by a user, the data in the preheating user data area of ​​the network element is queried. Specifically, this embodiment preferentially queries the preheating user service data in the core network element. When the preheating user service data is queried from the preheating user data area of ​​the core network element, the communication service of the current number is controlled according to the preheating user service data; when the preheating user service data is not queried from the preheating 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 content, this embodiment can reduce the number of times user data is obtained from the user database by preferentially querying the preheating user service data from the preheating user data area of ​​the core network network element, effectively improve the timeliness and accuracy of call or text message related communication data processing in various scenarios, and improve network resource utilization.

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

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

[0122] The dynamic management mechanism ensures that the preheated data area always contains the user data that is most likely to be requested, thereby reducing query latency and improving service response speed and user experience.

[0123] By using UHAAS to work closely with core network elements, real-time analysis of user data and rapid update of preheating data can be achieved, thereby ensuring efficient use of network resources and improving service quality;

[0124] By mapping user service time data to a specific time period and calculating the service timestamp, users’ future behavior patterns can be predicted more accurately, which in turn helps network operators better understand and predict user behavior and thus optimize the query process;

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

[0126] By dividing the time period into smaller time periods, performing correlation analysis with user data, and analyzing user behavior in each time period, the probability of users initiating requests in a specific time period can be more accurately predicted, thereby optimizing data loading in the preheating 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 is ensured, manual intervention is reduced, and the efficiency and stability of network services are improved.

[0128] It can be seen that the method of the embodiment of the present 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 network element preheating data area, effectively reducing query time, improving response speed, and ensuring that user requests can be processed quickly, thereby maintaining service stability and reliability in high-concurrency scenarios;

[0130] Second, through real-time analysis by the User Habit Analysis Application Server (UHAAS), the characteristics of different user groups can be identified and the data query process can be optimized based on these characteristics. For example, for commuters who often make calls during peak hours in the morning and evening, their data is pre-loaded into the preheated user storage area, and the data in the preheated user storage area is queried first during the communication process, thereby reducing the risk of user service quality being wasted by low-probability service users and effectively improving user experience.

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

[0132] The first module 710 is used to obtain a start analysis signal;

[0133] The second module 720 is used to obtain signaling flow data from the core network element according to the start analysis signal;

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

[0135] The fourth module 740 is used 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 used to send the preheating user service data to the core network element in a first preset time period, so that the core network element performs communication services according to the user communication request and the preheating user service data.

[0137] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0138] The embodiment of the present application also provides a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned communication data processing method based on user behavior prediction when executing the computer program. The computer device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0139] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0140] See also Figure 8 , Figure 8 The hardware structure of a computer device according to another embodiment is shown, and the computer device includes:

[0141] The processor 810 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0142] The memory 820 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 820 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 820, and the processor 810 calls and executes the communication data processing method based on user behavior prediction in the embodiment of this application;

[0143] Input / output interface 830, used to implement 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 through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

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

[0146] The processor 810 , the memory 820 , the input / output interface 830 and the communication interface 840 are connected to each other in communication within the device via a bus 850 .

[0147] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned communication data processing method based on user behavior prediction.

[0148] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0149] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0150] The embodiments described in the embodiments of the present application are intended 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 in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

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

[0152] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0154] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0155] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural 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, c can be single or multiple.

[0156] In the several embodiments provided in the present 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. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0157] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0160] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A communication data processing method based on user behavior prediction, characterized in that: The method comprises the following steps: Get the signal to start analysis; 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 the first user service data; The preheating user service data is sent to the core network element at a first preset time period, so that the core network element performs communication services according to the user communication request and the preheating user service data.

2. The method according to claim 1, characterized in that: The step of constructing a user behavior habit set according to the signaling flow data includes: Obtaining the service history data in the signaling flow data and the service time node and the first user number corresponding to each piece of the service history data; Calculate a service timestamp according to a second preset time period and the service time node; Grouping the service history data according to the second user number and the first user number to obtain a grouping result; Filter the second user number according to the grouping result to obtain a third user number; The service history data corresponding to each of the third user numbers is clustered according to the service timestamp to obtain a user behavior habit set.

3. The method according to claim 2, characterized in that The step of screening the second user number according to the grouping result to obtain a third user number includes: Determining, according to the grouping result, that the number of services corresponding to the second user number is less than a number threshold, and deleting the second user number; Determine, according to the grouping result, that the number of services corresponding to the second user number is greater than or equal to the number threshold, and use the second user number as the third user number.

4. The method according to claim 2, characterized in that: The clustering of the service history data corresponding to each of the third user numbers according to the service timestamp to obtain a user behavior habit set includes: Clustering the service history data corresponding to each of the third user numbers according to the service timestamp to obtain a cluster; When the number of business history data in the cluster is greater than or equal to the number threshold, obtaining the centroid time node of each cluster; The centroid time node is added to the corresponding user behavior habit set.

5. The method according to claim 2, characterized in that: The constructing the preheating user service data according to the user behavior habit set and the first user service data includes: dividing the second preset time period into a plurality of the first preset time periods; When the centroid time node of the user behavior habit concentration is located within the first preset time period, obtaining the first user service data of the third user number corresponding to the centroid time node; The warm-up user service data is constructed according to the third user number and the first user service data.

6. The method according to claim 1, characterized in that The sending the preheating user service data to the core network element in a first preset time period includes: When the timer times out, obtaining the preheating user service data for the next first preset time period; Obtaining address information of the core network element; The preheating user service data of the next first preset time period is sent to the core network element according to the address information.

7. The method according to claim 1, characterized in that The performing of communication service according to the user communication request and the preheating user service data comprises: Prioritizing querying the preheating user service data from the core network element according to the user communication request; The communication service of the current number is controlled according to the preheated user service data.

8. A communication data processing device based on user behavior prediction, characterized in that: The device comprises: The first module is used to obtain a start analysis signal; The second module is used to obtain signaling flow data from the core network element according to the start analysis signal; The third module is used to construct a user behavior habit set based on the signaling flow data; A fourth module is used to construct preheating user service data according to the user behavior habit set and the first user service data; The fifth module is used to send the preheating user service data to the core network element in a first preset time period, so that the core network element performs communication services according to the user communication request and the preheating user service data.

9. A computer device, characterized in that: include: 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 according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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