Route query method and device based on user habits, electronic equipment and medium

Abstract

The invention discloses a route query method and device based on user habits, electronic equipment and a medium, and can be applied to the technical field of communication. After the signaling data and the calling number and the called number corresponding to the signaling data are obtained from the core network element, the user habit information is analyzed according to the calling number, the called number and the signaling data, and then the call popularity of the user is calculated through the analytic hierarchy process and statistical analysis. Determining a target number from the called numbers according to the call popularity of the user, pre-querying a called route corresponding to the target number from a preset domain name system as a pre-processing route, and storing the pre-processing route to a user route prediction database, thereby enabling a core network to predict the user route after receiving the current call request. According to the method, the corresponding preprocessing route can be queried from the user route prediction database to serve as the current route, so that the route query times can be effectively reduced, the communication delay is reduced, and the consumption of network bandwidth and computing resources is reduced.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method and device for route query based on user habits, an electronic device and a medium. Background Art

[0002] In the related technology, the core network (CN), as a key component connecting user equipment and external networks, bears the important responsibility of managing and controlling communication connections between users, including phone calls and text message services. In order to ensure the smooth progress of each communication, the core network must be able to quickly and accurately determine the location of the called party and establish the correct communication path. This process is called routing query. At present, during the routing query process, each communication request requires multiple real-time queries to obtain the location information of the called user, which will increase the processing delay of the core network and further increase the communication delay. In addition, frequent query operations will also increase the consumption of network bandwidth and computing resources.

[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 route query method and device based on user habits, an electronic device and a medium, which can reduce communication delays and reduce the consumption of network bandwidth and computing resources.

[0005] To achieve the above purpose, one aspect of an embodiment of the present application proposes a route query method based on user habits, the method comprising the following steps:

[0006] Acquire signaling data and a calling number and a called number corresponding to the signaling data from a core network element;

[0007] Analyze user habit information according to the calling number, the called number and the signaling data;

[0008] Calculate the user's call popularity through hierarchical analysis and statistical analysis based on the user's habit information;

[0009] Determining a target number from the called number according to the user's call popularity;

[0010] Pre-querying the called route corresponding to the target number from a preset domain name system as a pre-processing route;

[0011] The pre-processed route is saved in a user route prediction database, so that the core network queries the corresponding pre-processed route from the user route prediction database as the current route according to the current call request.

[0012] In some embodiments, analyzing the user habit information according to the calling number, the called number and the signaling data includes:

[0013] Classify and integrate the signaling data according to the service type to obtain data to be processed;

[0014] Classifying the data to be processed according to the calling number to obtain a first data set;

[0015] Classifying the data in the first data set according to the called number to obtain a second data set;

[0016] The user habit information is obtained by statistically analyzing the second data set, and the user habit information includes call frequency, call duration, average call duration, latest call duration or latest call days.

[0017] In some embodiments, the calculating the user call popularity through hierarchical analysis and statistical analysis according to the user habit information includes:

[0018] Acquire a criterion layer judgment matrix corresponding to the user habit information, wherein the data in the criterion layer judgment matrix includes relative importance values ​​between the user habit information;

[0019] Calculating a normalized judgment matrix of the criterion layer judgment matrix, wherein the normalized judgment matrix includes normalized relative importance values ​​corresponding to relative importance values ​​between user habit information;

[0020] Calculating the average relative importance of each piece of user habit information according to the normalized relative importance value;

[0021] Calculating a normalized weight of each user habit information according to the average value of the relative importance;

[0022] Obtain the solution-level judgment matrix corresponding to each user’s habit information;

[0023] Calculating a plan-layer normalized value corresponding to each called number according to the plan-layer judgment matrix;

[0024] The user call heat is calculated based on the normalized weight and the solution layer normalized value.

[0025] In some embodiments, the method further comprises the following steps:

[0026] Calculating a maximum eigenvalue according to the normalized judgment matrix and the average value of the relative importance;

[0027] Calculating a consistency ratio according to the maximum eigenvalue and the total number of the user habit information;

[0028] The criterion layer judgment matrix is ​​adjusted according to the consistency ratio.

[0029] In some embodiments, the calculating the user call heat according to the normalized weight and the solution-level normalized value includes:

[0030] Calculating an intermediate average value according to the solution layer normalized value and the total number of user habit information, wherein the intermediate average value is used to represent the average value of each called number relative to each user habit information;

[0031] The user call heat is calculated based on the intermediate average value and the normalized weight.

[0032] In some embodiments, determining a target number from the called number according to the user call heat includes:

[0033] Sorting the called numbers according to the call popularity of the user;

[0034] According to the sorting result, several called numbers are selected from the called numbers as the target numbers.

[0035] In some embodiments, after the pre-processed route is saved in the user route prediction database, the method further includes the following steps:

[0036] When the preset timer times out, querying the pre-processed route in the user route prediction database;

[0037] The pre-queried route in the core network element is updated according to the queried pre-processed route.

[0038] To achieve the above purpose, another aspect of the embodiment of the present application provides a route query device based on user habits, the device comprising:

[0039] The first module is used to obtain signaling data and the calling number and called number corresponding to the signaling data from the core network element;

[0040] The second module is used to analyze user habit information according to the calling number, the called number and the signaling data;

[0041] The third module is used to calculate the user's call popularity through hierarchical analysis and statistical analysis based on the user habit information;

[0042] The fourth module is used to determine a target number from the called number according to the user's call heat;

[0043] A fifth module is used to pre-query the called route corresponding to the target number from a preset domain name system as a pre-processing route;

[0044] The sixth module is used to save the pre-processed route to the user route prediction database, so that the core network queries the corresponding pre-processed route from the user route prediction database as the current route according to the current call request.

[0045] To achieve the above object, another aspect of an embodiment of the present application provides an electronic device, including:

[0046] at least one processor;

[0047] at least one memory for storing at least one program;

[0048] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0049] 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.

[0050] The embodiments of the present application include at least the following beneficial effects: The present application provides a method and device for route query based on user habits, an electronic device and a medium. The scheme obtains signaling data and the calling number and called number corresponding to the signaling data from the core network network element, analyzes the user habit information according to the calling number, called number and signaling data, and then calculates the user call popularity through hierarchical analysis method and statistical analysis according to the user habit information. Then, after determining the target number from the called number according to the user call popularity, the called route corresponding to the target number is pre-queried from the preset domain name system as a pre-processed route, and the pre-processed route is saved to the user route prediction database, so that after receiving the current call request, the core network can query the corresponding pre-processed route from the user route prediction database as the current route, thereby effectively reducing the number of route queries, thereby reducing communication delays and reducing the consumption of network bandwidth and computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of a route query method based on user habits provided in an embodiment of the present application;

[0052] Figure 2 It is a schematic diagram of the interactive architecture of the route query method based on user habits provided in an embodiment of the present application;

[0053] Figure 3 It is a module schematic diagram of the interactive architecture provided in the embodiment of the present application;

[0054] Figure 4This is a flowchart of updating pre-processing routes in a core network provided by an embodiment of the present application;

[0055] Figure 5 This is a flow chart of performing a route query on a current user call request provided by an embodiment of the present application;

[0056] Figure 6 It is a structural diagram of a route query device based on user habits provided in an embodiment of the present application;

[0057] Figure 7 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0058] 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.

[0059] 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".

[0060] 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.

[0061] 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.

[0062] 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:

[0063] 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.

[0064] 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.

[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] The concept of DNS (Domain Name System) is the same as that in the traditional Internet. It is a service used to convert domain names into IP addresses. DNS is a distributed database system that enables users to access resources on the Internet through easy-to-remember domain names (such as www.example.com) without having to remember complex IP addresses.

[0067] RPQAS (Route pre query application server) is used to query the routing information corresponding to the user number.

[0068] In the related art, the core network (CN), as a key component connecting user equipment and external networks, bears the important responsibility of managing and controlling communication connections between users, including phone calls and SMS services. In order to ensure the smooth progress of each communication, the core network must be able to quickly and accurately determine the location of the called party and establish the correct communication path. The process is called routing query. Specifically, when a user tries to make a call or send a text message, the user equipment (such as a mobile phone) sends a request containing the calling number and the called number to the core network. After receiving the request, the core network first performs a preliminary verification to check the legitimacy and credit limit of the calling number to ensure the security and legitimacy of the communication. This verification step is the first step in the routing query process, which is intended to prevent unauthorized communication requests. Once the preliminary verification is passed, the core network will start the routing query process. This process involves multiple steps. First, the core network sends a query request to the Home Location Register (HLR) of the called number to obtain the current roaming location or home network of the called user. If the called user is in roaming state, the HLR will return a message pointing to the current visitor location register (VLR). Subsequently, the core network sends a query request to the VLR to obtain more detailed location information of the called user. Based on the acquired location information of the called user, the core network will establish a communication path from the calling user to the called user. Once the communication path is successfully established, the phone call or SMS service can proceed normally. During the whole process, the core network needs to interact with multiple network nodes to ensure that every step is accurate. The above-mentioned routing query method requires real-time query to obtain the location information of the called user for each communication, which will increase the processing delay of the core network and then increase the communication delay. In addition, frequent query operations will also increase the consumption of network bandwidth and computing resources.

[0069] In view of this, the embodiments of the present application provide a route query method and device based on user habits, an electronic device and a medium. After obtaining signaling data and the calling number and called number corresponding to the signaling data from the core network network element, the present application analyzes the user habit information according to the calling number, called number and signaling data, and then calculates the user call popularity through hierarchical analysis method and statistical analysis based on the user habit information. After determining the target number from the called number according to the user call popularity, the called route corresponding to the target number is pre-queried from the preset domain name system as a pre-processed route, and the pre-processed route is saved to the user route prediction database, so that after receiving the current call request, the core network can query the corresponding pre-processed route from the user route prediction database as the current route, thereby effectively reducing the number of route queries, thereby reducing communication delays and reducing the consumption of network bandwidth and computing resources.

[0070] Figure 1 is an optional flow chart of a route query method based on user habits provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S160:

[0071] Step S110: Acquire signaling data and a calling number and a called number corresponding to the signaling data from a core network element;

[0072] Step S120: analyzing user habit information according to the calling number, called number and signaling data;

[0073] Step S130: Calculate the user's call popularity through analytic hierarchy process and statistical analysis based on the user's habit information;

[0074] Step S140: determining a target number from the called numbers according to the user's call popularity;

[0075] Step S150, pre-querying a called route corresponding to the target number from a preset domain name system as a pre-processing route;

[0076] Step S160: Save the pre-processed route to the user route prediction database, so that the core network queries the corresponding pre-processed route from the user route prediction database as the current route according to the current call request.

[0077] It is understandable that this embodiment can be applied to Figure 2 In the route pre-query application server (RPQAS) in the interactive architecture shown. Among them, RPQAS can obtain signaling data and the user number (msisdn) corresponding to the signaling data from the core network element, and obtain user habit information based on the obtained signaling data and user number analysis, determine the target number based on the user habit information, and then pre-query the user data (User data) from the domain name system (DNS) to form a pre-processed route, so that when the core network element receives the current communication request, it can first match the pre-processed route to form the current route, thereby reducing the number of queries, thereby reducing communication delays and reducing network bandwidth and computing resource consumption.

[0078] It is understandable that if Figure 3 As shown in Table 1, RPQAS includes a central controller, a data collection module, a user data record database, a data analysis module, a route query module, and a user route prediction database. The interaction process between the various modules within RPQAS and the core network, the administrator terminal, and the DNS is shown in Table 1:

[0079] Table 1

[0080]

[0081]

[0082] In the embodiment of the present application, after acquiring the calling number, called number and signaling data, the process of analyzing the user habit information according to the calling number, called number and signaling data includes but is not limited to the following steps:

[0083] Step S210: classify and integrate the signaling data according to the service type to obtain data to be processed;

[0084] Step S220: classify the data to be processed according to the calling number to obtain a first data set;

[0085] Step S230: classify the data in the first data set according to the called number to obtain a second data set;

[0086] Step S240: Obtain user habit information based on statistical analysis of the second data set, wherein the user habit information includes call frequency, call duration, average call duration, most recent call duration, or most recent call days.

[0087] It can be understood that, in this embodiment, signaling data is classified according to service type, so as to integrate signaling data of the same service into one piece of data as data to be processed. Then, the data to be processed is classified according to the calling number to obtain a first data set, so that the calling number of any element in the first data set is the same user. Among them, the first data set is as follows:

[0088]

[0089] In the formula, G i Indicates all the data to be processed corresponding to the i-th calling number.

[0090] Then, the data in the first data set is divided again according to the called number to obtain the second data set, so that the calling number and the called number in the second data set are the same. The second data set is as follows:

[0091]

[0092] In the formula, it indicates that the i-th calling number corresponds to all the to-be-processed data corresponding to the j-th called number in the data set.

[0093] Based on the above segmentation process, the expression of the first data set is as follows:

[0094] G i =∪ j G i,j ;

[0095] The above formula indicates that the data in the data set corresponding to the i-th calling number is divided into sub-data sets corresponding to j called numbers.

[0096] Based on the second data set obtained above, statistical calculation is performed to obtain the call frequency, call duration, average call duration, latest call duration or latest call days representing the user's behavior habit information. The calculation formula of the call frequency is as follows:

[0097]

[0098] In the formula, call_count(j) represents the call frequency of the jth called number.

[0099] The calculation formula for call duration is as follows:

[0100]

[0101] In the formula, total_duration(j) represents the call duration between the j-th called number and the i-th calling number.

[0102] The average call duration is calculated as follows:

[0103]

[0104] In the formula, total_duration(j) represents the total call duration of the j-th called number within a certain period of time, call_cout(j) represents the number of calls made by the j-th called number within a certain period of time, and avg_duration(j) represents the average call duration of the j-th called number.

[0105] The calculation formula for the latest call duration is as follows:

[0106]

[0107] In the formula, last_call_time(j) is the duration of the most recent call between the j-th called number and the i-th calling number.

[0108] The calculation formula for the number of days of recent calls is as follows:

[0109]

[0110] In the formula, OneDay represents the number of seconds in a day (86400s), T-last_call_time(j) represents the duration of the most recent call to the j-th called number, and last_call_days_ago(j) represents the number of days since the most recent call to the j-th called number.

[0111] In the embodiment of the present application, the process of calculating the user's call popularity through hierarchical analysis and statistical analysis based on the user habit information includes but is not limited to the following steps:

[0112] Step S310: Obtain a criterion layer judgment matrix corresponding to the user habit information, wherein the data in the criterion layer judgment matrix includes relative importance values ​​between the user habit information;

[0113] Step S320, calculating a normalized judgment matrix of the criterion layer judgment matrix, wherein the normalized judgment matrix includes normalized relative importance values ​​corresponding to relative importance values ​​between user habit information;

[0114] Step S330, calculating the average relative importance of each user's habit information according to the normalized value of relative importance;

[0115] Step S340, calculating the normalized weight of each user's habit information according to the average value of relative importance;

[0116] Step S350, obtaining a solution layer judgment matrix corresponding to each user habit information;

[0117] Step S360: Calculate the plan layer normalized value corresponding to each called number according to the plan layer judgment matrix;

[0118] Step S310: Calculate the user call heat according to the normalized weight and the solution layer normalized value.

[0119] It can be understood that the value in the criterion layer judgment matrix is ​​a i,j , used to indicate the importance of any user habit information i compared to user habit information j. For example, as shown in Table 2, the importance of each value is different:

[0120] Table 2

[0121]

[0122] For example, a i,j =3 means that user habit information i is slightly more important than user habit information j.

[0123] Specifically, the values ​​in the criterion layer judgment matrix can be preset by the administrator and Figure 3 The Y4 interface in the RPQAS is transmitted.

[0124] After obtaining the criterion layer judgment matrix, the normalized judgment matrix of the criterion layer judgment matrix is ​​calculated by the following formula:

[0125]

[0126] In the formula, A′ represents the normalized judgment matrix, a i,j represents the relative importance value of user habit information i compared with user habit information j in the criterion layer judgment matrix A, and n represents the total number of user habit information.

[0127] The average relative importance is calculated using the following formula:

[0128]

[0129] In the formula, w i represents the average relative importance of the i-th user’s habit information, a i,j ′ represents the normalized relative importance value of user habit information i compared with user habit information j in the normalized judgment matrix.

[0130] The normalized weight of user habit information is calculated by the following formula:

[0131]

[0132] In the formula, W i Represents the normalized weight of the i-th user’s habit information.

[0133] It is understandable that, after calculating the normalized judgment matrix and the average value of the relative importance, this embodiment performs a consistency check on the values ​​in the criterion layer judgment matrix. If the check fails, the current criterion layer judgment matrix is ​​not received, and the administrator is prompted to reset the criterion layer judgment matrix and recalculate the corresponding normalized judgment matrix and the average value of the relative importance. Specifically, during the consistency check, the maximum eigenvalue is calculated by the following formula:

[0134]

[0135] In the formula, λ max represents the maximum eigenvalue, n represents the total number of user habit information, A′ represents the normalized judgment matrix, and w i Represents the average relative importance of the i-th user’s habit information.

[0136] The consistency ratio is then calculated using the following formula:

[0137]

[0138] In the formula, CR represents the consistency ratio; represents the consistency index; RI represents the random consistency index, which is a constant and can be set to 1.12 in this embodiment.

[0139] Specifically, if CR>0.1, it means that the value verification in the criterion layer judgment matrix has passed, otherwise it means that the verification has failed and the administrator needs to reset the value.

[0140] In the embodiment of the present application, the scheme-level judgment matrix corresponding to each user habit information is obtained. Among them, the scheme-level judgment matrix corresponding to the call frequency is B1, the scheme-level judgment matrix corresponding to the call duration is B2, the scheme-level judgment matrix corresponding to the average call duration is B3, the scheme-level judgment matrix corresponding to the most recent call duration is B4, and the scheme-level judgment matrix corresponding to the most recent call days is B5.

[0141] Specifically, the values ​​in the solution layer judgment matrix B1 are calculated using the following formula:

[0142]

[0143] In the formula, b1 jk Indicates the ratio of the call frequency of the j-th called number to the call frequency of the k-th called number.

[0144] The values ​​in the solution-level judgment matrix B2 are calculated using the following formula:

[0145]

[0146] In the formula, b2 jk Indicates the ratio of the call duration of the j-th called number to the call duration of the k-th called number.

[0147] The values ​​in the solution-level judgment matrix B3 are calculated using the following formula:

[0148]

[0149] In the formula, b3 jk Indicates the ratio of the average call duration of the j-th called number to the average call duration of the k-th called number.

[0150] The values ​​in the solution-level judgment matrix B4 are calculated using the following formula:

[0151]

[0152] In the formula, b4 jk Indicates the ratio of the most recent call duration of the j-th called number to the most recent call duration of the k-th called number.

[0153] The values ​​in the solution-level judgment matrix B5 are calculated using the following formula:

[0154]

[0155] In the formula, b5jk It represents the ratio of the number of days since the last call of the j-th called number to the number of days since the last call of the k-th called number.

[0156] Then, the values ​​in each solution layer judgment matrix are normalized using the following formula:

[0157]

[0158] In the formula, bi jk ′ represents the normalized value of the solution layer in the i-th solution layer judgment matrix, and m represents the total number of called numbers.

[0159] After calculating the solution layer normalized value of the solution judgment matrix, this embodiment calculates the middle average value representing each called number relative to each user habit information in combination with the total number of user habit information, and then calculates the user call heat by the middle average value and the normalized weight. The calculation formula of the user call heat is as follows:

[0160] h j =∑W i si j ;

[0161] In the formula, W represents the user call popularity of the jth called number; i Represents the normalized weight of the i-th user’s habit information; Represents the median average value of the j-th called number.

[0162] After calculating the user's call popularity, this embodiment sorts the called numbers according to the user's call popularity, and selects several called numbers from the called numbers as target numbers according to the sorting result. For example, the top ten called numbers can be selected as target numbers. Then, Figure 3 The Y3 interface in the query route from the DNS and stores it in the user prediction route database.

[0163] In the embodiments of the present application, Figure 4 As shown, the updating process of the pre-processing route in the core network includes but is not limited to the following steps:

[0164] Step 1: When the pre-query route timer times out, the route pre-query application server starts updating the user called route pre-query data flow in the core network element;

[0165] Step 2: The central controller queries the top 10 users and their routes that are most likely to make calls after analyzing the historical behavior of each user. It also queries the addresses of each core network element and prepares to update the pre-query data of the called route in the user data area of ​​the core network element;

[0166] Step 3: According to the queried user data, the central controller updates the pre-queried route data in the user data area of ​​the core network element to the latest pre-queried route;

[0167] Step 4: The central controller restarts the pre-query routing timer and waits for the next cycle to count and analyze the user called pre-query routing data again.

[0168] It is understandable that after completing the latest update process of the pre-query route in the core network element, such as Figure 5 As shown, the process of performing routing query on the current user call request includes but is not limited to the following steps:

[0169] Step 1: After receiving the user request, the core network element queries the user data. The user data includes basic user data, such as msisdn, imsi, and predicted called route, etc.

[0170] Step 2: After the core network element obtains the user data, it extracts the data corresponding to the user's pre-queried route and matches whether the current called party is included in this service. If it matches, it directly forwards the signaling according to the route provided in the predicted called party route to establish the call. If the match fails, it re-queries the DNS and queries the called party route in the to header field.

[0171] From the above content, it can be seen that the method provided by this application has the following beneficial effects:

[0172] First, this embodiment introduces the route pre-query application server (RPQAS) and the hierarchical analysis method, which can predict the user's communication behavior and cache the routing information of the called users with high frequency contact in advance, thereby effectively reducing the number and time of real-time queries and significantly reducing communication delays, especially during network peak hours, which is crucial to improving user experience.

[0173] Second, this embodiment can effectively improve the resource utilization of the core network through the pre-query and cache mechanism. In the traditional solution, frequent query operations consume a lot of network bandwidth and computing resources, while this embodiment reduces these query operations, allowing the core network to use more resources for other important tasks, such as real-time traffic monitoring and network maintenance, thereby improving the overall resource utilization efficiency.

[0174] Third, the dynamic adjustment of the cache strategy in this embodiment can better adapt to the communication needs of users, so that the routing information in the cache can be updated in time according to changes in user communication behavior, ensuring that the cache content is always up to date.

[0175] Fourth, this embodiment can effectively respond to changes in network conditions and improve service quality through multi-level analysis and pre-query. When the network is congested or fails, the core network can quickly obtain routing information from the cache to ensure the continuity and stability of communication, thereby improving user experience and overall service quality.

[0176] Reference Figure 6 , the embodiment of the present application provides a route query device based on user habits, the device comprising:

[0177] The first module 610 is used to obtain signaling data and a calling number and a called number corresponding to the signaling data from a core network element;

[0178] The second module 620 is used to analyze user habit information based on the calling number, called number and signaling data;

[0179] The third module 630 is used to calculate the user's call popularity through hierarchical analysis and statistical analysis based on the user's habit information;

[0180] The fourth module 640 is used to determine a target number from the called number according to the user's call heat;

[0181] The fifth module 650 is used to pre-query the called route corresponding to the target number from the preset domain name system as a pre-processing route;

[0182] The sixth module 660 is used to save the pre-processed route to the user route prediction database, so that the core network queries the corresponding pre-processed route from the user route prediction database as the current route according to the current call request.

[0183] 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.

[0184] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned route query method based on user habits when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0185] 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.

[0186] See also Figure 7 , Figure 7The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0187] The processor 710 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;

[0188] The memory 720 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 720 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 720, and the processor 710 calls and executes the routing query method based on user habits in the embodiment of this application;

[0189] Input / output interface 730, used to implement information input and output;

[0190] Communication interface 740, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0191] bus 750 , which transmits information between the various components of the device (e.g., processor 710 , memory 720 , input / output interface 730 , and communication interface 740 );

[0192] The processor 710 , the memory 720 , the input / output interface 730 , and the communication interface 740 are connected to each other in communication within the device via the bus 750 .

[0193] The 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, the above-mentioned routing query method based on user habits is implemented.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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 route query method based on user habits, characterized in that: The method comprises the following steps: Acquire signaling data and a calling number and a called number corresponding to the signaling data from a core network element; Analyze user habit information according to the calling number, the called number and the signaling data; Calculate the user's call popularity through hierarchical analysis and statistical analysis based on the user's habit information; Determining a target number from the called number according to the user's call popularity; Pre-querying the called route corresponding to the target number from a preset domain name system as a pre-processing route; The pre-processed route is saved in a user route prediction database, so that the core network queries the corresponding pre-processed route from the user route prediction database as the current route according to the current call request.

2. The method according to claim 1, characterized in that The analyzing the user habit information according to the calling number, the called number and the signaling data includes: Classify and integrate the signaling data according to the service type to obtain data to be processed; Classifying the data to be processed according to the calling number to obtain a first data set; Classifying the data in the first data set according to the called number to obtain a second data set; The user habit information is obtained by statistically analyzing the second data set, and the user habit information includes call frequency, call duration, average call duration, latest call duration or latest call days.

3. The method according to claim 1, characterized in that The calculating the user call popularity through hierarchical analysis and statistical analysis according to the user habit information includes: Acquire a criterion layer judgment matrix corresponding to the user habit information, wherein the data in the criterion layer judgment matrix includes relative importance values ​​between the user habit information; Calculating a normalized judgment matrix of the criterion layer judgment matrix, wherein the normalized judgment matrix includes normalized relative importance values ​​corresponding to relative importance values ​​between user habit information; Calculating the average relative importance of each piece of user habit information according to the normalized relative importance value; Calculating a normalized weight of each user habit information according to the average value of the relative importance; Obtain the solution-level judgment matrix corresponding to each user’s habit information; Calculating a plan-layer normalized value corresponding to each called number according to the plan-layer judgment matrix; The user call heat is calculated based on the normalized weight and the solution layer normalized value.

4. The method according to claim 3, characterized in that The method further comprises the following steps: Calculating a maximum eigenvalue according to the normalized judgment matrix and the average value of the relative importance; Calculating a consistency ratio according to the maximum eigenvalue and the total number of the user habit information; The criterion layer judgment matrix is ​​adjusted according to the consistency ratio.

5. The method according to claim 3, characterized in that: The calculating the user call heat according to the normalized weight and the solution layer normalized value includes: Calculating an intermediate average value according to the solution layer normalized value and the total number of user habit information, wherein the intermediate average value is used to represent the average value of each called number relative to each user habit information; The user call heat is calculated based on the intermediate average value and the normalized weight.

6. The method according to claim 1, characterized in that The step of determining a target number from the called number according to the user call heat includes: Sorting the called numbers according to the call popularity of the user; According to the sorting result, several called numbers are selected from the called numbers as the target numbers.

7. The method according to claim 1, characterized in that After the preprocessed route is saved in the user route prediction database, the method further comprises the following steps: When the preset timer times out, querying the pre-processed route in the user route prediction database; The pre-queried route in the core network element is updated according to the queried pre-processed route.

8. A route query device based on user habits, characterized in that: The device comprises: The first module is used to obtain signaling data and the calling number and called number corresponding to the signaling data from the core network element; The second module is used to analyze user habit information according to the calling number, the called number and the signaling data; The third module is used to calculate the user's call popularity through hierarchical analysis and statistical analysis based on the user habit information; The fourth module is used to determine a target number from the called number according to the user's call heat; A fifth module is used to pre-query the called route corresponding to the target number from a preset domain name system as a pre-processing route; The sixth module is used to save the pre-processed route to the user route prediction database, so that the core network queries the corresponding pre-processed route from the user route prediction database as the current route according to the current call request.

9. An electronic 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.

Images