Business recommendation method and device based on big data and artificial intelligence

Abstract

The invention provides a business recommendation method and device based on big data and artificial intelligence. The method comprises the following steps: firstly, by taking a mobile phone number of a user as a collection condition, collecting an ordered telecommunication service of the user through big data, and establishing a user information database; afterwards, on the basis of privacy protection measures, statistics is carried out on telecommunication services used by the users through big data, use characteristic indexes of the telecommunication services used are calculated, and user characteristic index levels are generated; then, on the basis of the database, the influence level of the user in the preset time is calculated through the user characteristic index level, and a comparison factor is generated; and finally, based on the comparison factor, comparing with a preset standard, and deciding to recommend the telecommunication service to the user according to a comparison result. According to the embodiment of the invention, the application of big data and artificial intelligence in the aspect of telecommunication service recommendation is realized, so that the recommended service is ensured to accurately match the user demand, and the success rate of service recommendation and the user acceptance are improved.

Description

Technical Field

[0001] The present application relates to the technical field of telecommunication service recommendation, and in particular to a service recommendation method and device based on big data and artificial intelligence. Background Art

[0002] The rapid development of artificial intelligence technology, especially breakthroughs in big data and algorithms, is leading to changes in all walks of life. Artificial intelligence algorithms can quickly process and analyze large amounts of data, helping companies provide more accurate and personalized services. Especially in the field of telecommunications service recommendations, the application of artificial intelligence makes service recommendations more intelligent. Through in-depth analysis of user behavior, communication records, preferences and usage habits, AI can quickly identify user needs and recommend telecommunications services that are more in line with their personal characteristics and usage needs. However, there are still many shortcomings in the success rate of service recommendations and user acceptance.

[0003] In order to solve the above problems, the prior art improves the success rate of recommending telecommunication services to a user based on the closeness of the user and his contacts and the preference of the user's contacts for telecommunication services. However, in actual use, the system focuses more on the related contacts of the service recommendation user and uses other related persons as information indicators of the service recommendation user, which is easy to cause incorrect influence on the service recommendation user, that is, it is impossible to clarify the actual needs of the service recommendation user, thereby reducing the success rate of service recommendation. Summary of the invention

[0004] This application shows a business recommendation method and device based on big data and artificial intelligence.

[0005] In a first aspect, the present application shows a business recommendation method based on big data and artificial intelligence, the method comprising:

[0006] Step 1: Using the user's mobile phone number as a collection condition, collect the user's subscribed telecommunications services through big data and establish a user information database;

[0007] Step 2: Based on the privacy protection measures, statistics are collected on the use of telecommunication services by users through big data, and characteristic indicators of the use of telecommunication services are calculated to generate user characteristic indicator levels;

[0008] Step 3: Based on the user information database, the influence level of the user within a preset time is calculated by the user characteristic index level to generate a comparison factor;

[0009] Step 4: Based on the comparison factor, the comparison is performed with a preset standard, and a recommendation of telecommunication services to the user is determined according to the comparison result.

[0010] In an exemplary embodiment of the present disclosure, the subscription telecommunication services of users are collected through big data, including:

[0011] Using the user's mobile phone number as the collection condition, the subscription of telecommunications services and the duration of use of the subscription of telecommunications services are counted through big data to establish a personal information file;

[0012] Based on artificial intelligence algorithms, parallel information files are established by associating information with the same user personal information but different user mobile phone numbers;

[0013] By integrating the personal information files and parallel information files, a user information database is generated.

[0014] In an exemplary embodiment of the present disclosure, statistics on the use of telecommunication services by users are collected through big data, including:

[0015] Collect statistics on users' use of telecommunications services through information encryption to determine the types of telecommunications services used;

[0016] Based on the type of telecommunication service used, the corresponding frequency of use, duration of use, duration of use, and usage fee are determined through an artificial intelligence algorithm to establish usage characteristic indicators for the telecommunication service used.

[0017] In an exemplary embodiment of the present disclosure, calculating the usage characteristic index of using the telecommunication service includes:

[0018] Based on the preset usage frequency statistical interval, the usage frequency index level is generated by setting the impact level of the usage frequency;

[0019] Based on the preset usage time statistical interval, the usage time indicator level is generated by setting the impact level of the usage time;

[0020] Based on the preset duration value, when the duration is less than the preset duration value, the duration statistical value is the duration; when the duration is not less than the preset duration value, the duration statistical value is the preset duration value;

[0021] The user characteristic index level is generated by integrating the usage frequency index level, the usage duration index level, and the duration statistics.

[0022] In an exemplary embodiment of the present disclosure, calculating the influence level of the user within a preset time by using the user characteristic indicator level includes:

[0023] Based on artificial intelligence algorithms, the usage impact factor is generated by calculating the usage frequency within the user's usage time;

[0024] Based on the usage impact factor, a continuous impact factor is generated by calculating the continuous impact level of the user within the continuous duration;

[0025] Based on artificial intelligence algorithms, the service costs are generated by counting the user's usage fees during the usage period;

[0026] Based on the service cost, a cost impact factor is generated by calculating the cost impact level of the user during the duration of the service time;

[0027] The comparison factor is generated by integrating the continuous impact factor, the usage impact factor, and the cost impact factor.

[0028] In an exemplary embodiment of the present disclosure, by comparing with a preset standard, and deciding to recommend a telecommunication service to a user according to the comparison result, the method includes:

[0029] Based on an artificial intelligence algorithm, a dividing line for sending telecommunication service recommendation information to users is established by comparing the user information database with the user characteristic index level, and a preset standard is generated;

[0030] The preset standards include preset usage impact factor standards, preset continuous impact factor standards, and preset cost impact factor standards.

[0031] In an exemplary embodiment of the present disclosure, by comparing with a preset standard, and deciding to recommend a telecommunication service to a user according to the comparison result, the method includes:

[0032] If the continuous impact factor is greater than the preset continuous impact factor standard, recommending telecommunication services to the user;

[0033] If the continuous impact factor is not greater than the preset continuous impact factor standard, no telecommunication service is recommended to the user;

[0034] If the usage impact factor is greater than a preset usage impact factor standard, recommending telecommunication services to the user;

[0035] If the usage impact factor is not greater than the preset usage impact factor standard, no telecommunication service is recommended to the user;

[0036] If the cost impact factor is greater than the preset cost impact factor standard, recommending telecommunication services to the user;

[0037] If the cost impact factor is not greater than the preset cost impact factor standard, no telecommunication service is recommended to the user.

[0038] In a second aspect, the present application shows a business recommendation device based on big data and artificial intelligence, the device comprising:

[0039] The user information module is used to collect the user's subscribed telecommunications services through big data based on the user's mobile phone number and establish a user information database;

[0040] An information encryption collection module is used to collect statistics on the use of telecommunication services by users through big data and calculate the use characteristic indicators of the telecommunication services;

[0041] Data analysis module, used to calculate the impact level within the time preset by the user;

[0042] The telecommunications service recommendation module is used to compare with preset standards and decide to recommend telecommunications services to users based on the comparison results.

[0043] In a third aspect, the present application shows an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0044] In a fourth aspect, the present application illustrates a non-temporary computer-readable storage medium, which, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a method as described in any of the above aspects.

[0045] In a fifth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method described in any of the above aspects.

[0046] The technical solution provided by this application may have the following beneficial effects:

[0047] Based on the embodiment of the present disclosure, first, the user's mobile phone number is used as a collection condition, and the user's subscribed telecommunications services are collected through big data to establish a user information database. Afterwards, based on privacy protection measures, the user's use of telecommunications services is counted through big data, and the usage characteristic indicators of the telecommunications services are calculated to generate user characteristic indicator levels. Then, based on the artificial intelligence algorithm, the user's impact level within the preset time is calculated through the user characteristic indicator level to generate a comparison factor. Finally, based on the comparison factor, it is compared with the preset standard, and the recommendation of telecommunications services to the user is determined based on the comparison result. Therefore, the embodiment of the present disclosure provides a solution for recommending telecommunications services based on big data and artificial intelligence, which can perform digital intelligent analysis and recommendation on encrypted collected information. The application of big data and artificial intelligence in telecommunications service recommendation is realized to ensure that the recommended services accurately match user needs, thereby improving the success rate of service recommendations and user acceptance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flowchart of the steps of a business recommendation method based on big data and artificial intelligence in this application;

[0049] Figure 2 This is a decision logic flow chart of a business recommendation method based on big data and artificial intelligence in this application;

[0050] Figure 3 This is a structural block diagram of a business recommendation device based on big data and artificial intelligence in this application;

[0051] Figure 4 This is a user information module workflow diagram of a business recommendation device based on big data and artificial intelligence in the present application;

[0052] Figure 5 This is a workflow diagram of an information encryption collection module of a business recommendation device based on big data and artificial intelligence in the present application;

[0053] Figure 6 This is a workflow diagram of a data analysis module of a business recommendation device based on big data and artificial intelligence in the present application;

[0054] Figure 7 is a block diagram of an electronic device of the present application;

[0055] Figure 8 It is a block diagram of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] Reference Figure 1 , shows a flowchart of a business recommendation method based on big data and artificial intelligence in the present application, which can be applied to electronic devices, wherein the method specifically may include the following steps:

[0058] Step S101, using the user's mobile phone number as a collection condition, collecting the user's subscribed telecommunications services through big data and establishing a user information database.

[0059] Step S102: Based on the privacy protection measures, statistics are collected on the use of telecommunication services by users through big data, and characteristic indicators of the use of telecommunication services are calculated to generate user characteristic indicator levels.

[0060] Step S103, based on the user information database, the influence level of the user within a preset time is calculated by using the user characteristic indicator level to generate a comparison factor.

[0061] Step S104: Based on the comparison factor, the comparison is performed with a preset standard, and a recommendation of a telecommunication service to the user is determined according to the comparison result.

[0062] This service recommendation method based on big data and artificial intelligence uses artificial intelligence to establish a database of encrypted collected information, and then conducts a digital intelligent analysis of the impact of different services on users over a period of time by establishing a characteristic index level. Finally, based on the analyzed comparison factors, telecommunications services are recommended to users, which will make the services more in line with the actual needs of users. At the same time, recommending telecommunications services to users in a targeted and directional manner can also improve user acceptance and increase the success rate of service recommendations.

[0063] Below, as Figure 2 As shown, a business recommendation method based on big data and artificial intelligence in an embodiment of the present disclosure will be further explained.

[0064] In step S101, the user's mobile phone number may be used as a collection condition, the user's subscribed telecommunications services may be collected through big data, and a user information database may be established.

[0065] In some optional embodiments of this example, the user's mobile phone number is used as a collection condition, and the subscribed telecommunications services under the mobile phone number are screened through big data. Each mobile phone number corresponds to a user, and the mobile phone number will be used as the starting point for information collection to ensure that each user's telecommunications service information is accurately recorded.

[0066] Afterwards, based on the telecommunications services subscribed, the usage time of the telecommunications services is counted (in this example, the usage time is in months) and a personal information file is established.

[0067] Then, for different mobile phone numbers of users with the same personal information, parallel information files are established, and all parallel information files of the same user (same personal information, different mobile phone numbers) are linked through artificial intelligence algorithms.

[0068] As described above, a user information database of the user is constructed starting from each mobile phone number under the user.

[0069] Specifically, suppose a user has three mobile phone numbers, recorded as A, B, and C, and mobile phone number A has three telecom services, mobile phone number B has two telecom services, and mobile phone number C has four telecom services. In the user information database, they are recorded as: A1, A2, A3, B1, B2, C1, C2, C3, C4, and the nine data are interrelated to facilitate accurate service recommendation and management.

[0070] In step S102, based on privacy protection measures, statistics on the use of telecommunication services by users can be collected through big data, and usage characteristic indicators of the use of telecommunication services can be calculated to generate user characteristic indicator levels.

[0071] In some optional embodiments of this example, the system also needs to collect the user's telecommunications services and determine the types of telecommunications services used by the user. These types of telecommunications services usually include specific packages or value-added services used by the user, which may not be exactly the same as the telecommunications services subscribed by the user. For example, the user may have subscribed to a basic data package, but actually used additional international roaming data, home broadband packages, or short-term value-added services.

[0072] After that, the system automatically identifies the characteristic indicators such as usage frequency P, usage duration t, duration T, usage fee M, etc. corresponding to the type of telecommunications service used through intelligent data collection technology, and classifies and counts them. (For example, taking a mobile phone number as the starting point, if the mobile phone number uses four types of telecommunications services, the types of telecommunications services D used are recorded as D1, D2, D3, and D4 respectively) In this way, the real needs of users can be fully understood, thus laying the foundation for accurate recommendations.

[0073] In some optional embodiments of this example, it is also necessary to divide the influence levels of the characteristic indicators according to preset statistical intervals to construct user characteristic indicator levels.

[0074] Specifically, if "month" is used as the statistical interval and the unit of the usage frequency P is set to "times", the usage frequency index level is divided into Y P , Y P Including: Level 1, the monthly usage frequency is less than or equal to 30 times (i.e., P≤30 times per month); Level 2, the monthly usage frequency is greater than 30 times and less than or equal to 60 times (i.e., 30 times per month<P≤60 times); Level 3, the monthly usage frequency is greater than 60 times and less than or equal to 90 times (i.e., 60 times per month<P≤90 times); Level 4, the monthly usage frequency is greater than 90 times and less than or equal to 120 times (i.e., 90 times per month<P≤120 times); Level 5, the monthly usage frequency is greater than 120 times (i.e., 120 times per month<P).

[0075] If "month" is used as the statistical interval and the unit of usage duration t is set to "minute", the usage duration indicator level is Y t , Y t Including: Level 1, monthly usage time is less than or equal to 30 minutes (i.e., t≤30 minutes per month); Level 2, monthly usage time is greater than 30 minutes and less than or equal to 120 minutes (i.e., 30 minutes per month<t≤120 minutes); Level 3, monthly usage time is greater than 120 minutes and less than or equal to 270 minutes (i.e., 120 minutes per month<t≤270 minutes); Level 4, monthly usage time is greater than 270 minutes and less than or equal to 480 minutes (i.e., 270 minutes per month<t≤480 minutes); Level 5, monthly usage time is greater than 480 minutes (i.e., 480 minutes per month<t).

[0076] The duration statistics are different from the above two indicators. The statistical interval is 24 months (that is, the preset duration is 24 months), and the unit of duration T is month. From the statistical time, if the continuous use time of telecommunication service type D is less than 24 months, the statistics will start from the use time of telecommunication service type D (that is, the duration statistics equals duration T); if the continuous use time of telecommunication service type D is greater than or equal to 24 months, the statistics will be counted 24 months forward from the statistical time (that is, the duration statistics equals the preset duration).

[0077] Of course, in the process of collecting user telecommunications service usage data, in order to ensure the security of user data, the system will use multiple encryption technologies to protect the data to prevent data leakage or access by unauthorized personnel.

[0078] Preferably, the system will also set access rights to allow only authorized personnel or modules to access this data. By establishing a complete privacy protection mechanism, the system not only protects the security of users' personal information, but also enhances users' trust in the recommendation system. In addition, the system will regularly review and update privacy protection measures in accordance with relevant laws and regulations to ensure that it always complies with industry standards and legal requirements.

[0079] In step S103, based on the user information database, the influence level of the user within a preset time period may be calculated by using the user characteristic indicator level to generate a comparison factor.

[0080] In some optional embodiments of this example, based on the constructed user characteristic index level, the corresponding usage impact factor is determined by comparing and analyzing the types of telecommunication services subscribed to the mobile phone number and the types of telecommunication services used. At the same time, it is also necessary to determine whether the type of telecommunication service used D has been subscribed, and whether the usage fee M exceeds the subscription fee of the corresponding type of telecommunication service subscribed.

[0081] Among them, through the formula: Calculate the impact factor.

[0082] Afterwards, by the formula: Calculate the sustained impact factor.

[0083] X 使 To use the impact factor, Y t Y is the usage duration indicator level, P is the frequency of use indicator level, X 总 is the continuous impact factor, (X1+X2+X3...+X n ) is the usage impact factor for each month within the duration statistical interval, and n is the duration statistical value.

[0084] And, through the formula: Calculate the cost impact factor.

[0085] M 平均 is the average monthly usage fee (i.e., the cost impact factor) during the continuous use of telecommunication service type D, (M1+M2+M3...+M n ) is the monthly usage fee within the statistical period.

[0086] These comparison factors enable the system to more accurately assess the importance of each telecom service to users and further predict their possible future needs. If the comparison factor of a telecom service is high, it means that the service plays an important role in the user's daily life, and the system will give priority to recommending related value-added services or upgrades to corresponding packages.

[0087] At the same time, the comparison factor can also help telecom companies optimize product design and adjust pricing strategies to meet the personalized needs of users. Through this intelligent analysis, the system can more effectively provide tailored business recommendations for each user.

[0088] In step S104, the comparison factor may be compared with a preset standard, and a recommendation of a telecommunication service to the user may be determined according to the comparison result.

[0089] In some optional embodiments of this example, a dividing line for sending telecommunication service recommendation information to users is established by comparing the user information database with the user characteristic index level. For example, when judging whether to recommend telecommunication services to users based on the usage impact factor and the continuous impact factor, "1" can be used as the preset usage impact factor standard and the preset continuous impact factor standard. 使When the value is greater than 1, the system determines that a new telecom service needs to be recommended to the user, triggering the recommendation mechanism. The mechanism will select the appropriate telecom service type for recommendation based on the user's subscription history and usage behavior. For example, for a user with high data usage and low voice demand, the system may recommend a package that prioritizes data traffic; while for a user with more calls, a voice package may become the focus of the recommendation.

[0090] When X 使 When ≤1, the system determines that there is no need to recommend new telecommunications services to users, and the system will avoid making irrelevant recommendations to reduce annoyance to users.

[0091] Similarly, when X 总 >1, the system determines that it is necessary to recommend new telecommunications services to users, triggering the recommendation mechanism. 总 When ≤1, the system determines that there is no need to recommend new telecommunication services to the user.

[0092] In addition, artificial intelligence technology is also needed to determine whether the subscribed telecommunications services of the current mobile phone number are the same as the types of telecommunications services used. If the types of telecommunications services used are the same as the subscribed telecommunications services, the types of telecommunications services used have been subscribed; if the types of telecommunications services used are different from the subscribed telecommunications services, the types of telecommunications services used have not been subscribed.

[0093] If the telecommunications service type has been subscribed, determine whether the average monthly usage fee during the continuous use of telecommunications service type D exceeds the subscription fee of the corresponding telecommunications service type (that is, determine whether the fee impact factor is greater than the preset fee impact factor standard G).

[0094] When M 平均 > G, the system determines that it is necessary to recommend new telecommunications services to users, triggering the recommendation mechanism. 平均 When ≤G, the system determines that there is no need to recommend new telecommunication services to the user.

[0095] If the telecommunications service type has not been subscribed, determine whether the average monthly usage fee exceeds 10 yuan per month during the continuous use of telecommunications service type D (that is, determine whether the fee impact factor is greater than 10).

[0096] When M 平均 >10, the system determines that it is necessary to recommend new telecommunications services to users and triggers the recommendation mechanism. 平均 When ≤10, the system determines that there is no need to recommend new telecommunication services to the user.

[0097] And, preferably, it can also be set that when M1, M2, M3, M n When any of the items is greater than 20, the system determines that a new telecommunications service needs to be recommended to the user, triggering the recommendation mechanism.

[0098] It should be noted that, for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by the present application.

[0099] Reference Figure 3 , shows a structural block diagram of a business recommendation device 300 based on big data and artificial intelligence of the present application, the device comprising:

[0100] The user information module 301 is used to collect the user's subscribed telecommunications services through big data based on the user's mobile phone number and establish a user information database;

[0101] The information encryption collection module 302 is used to collect statistics on the use of telecommunication services by users through big data and calculate the use characteristic indicators of the telecommunication services;

[0102] Data analysis module 303, used to calculate the impact level within the time preset by the user;

[0103] The telecommunication service recommendation module 304 is used to compare with preset standards and decide to recommend telecommunication services to the user according to the comparison result.

[0104] In some optional embodiments of this example, Figure 4 As shown, the user information module 301 is used to collect and order telecommunication services starting from the subordinate mobile phone numbers; and to aggregate the user personal information of the subordinate mobile phone numbers into a user information database.

[0105] In some optional embodiments of this example, Figure 5 As shown, the information encryption collection module 302 is used to take the subordinate mobile phone number as the starting point, collect the types of telecommunication services used by the mobile phone number through a confidentiality agreement, and collect related characteristic indicators such as frequency of use, duration of use, duration of service, and telecommunication service usage fees.

[0106] In some optional embodiments of this example, Figure 6 As shown, the data analysis module 303 is used to compare and analyze the information collected by the information encryption collection module 302 and the user information module 301, and provide recommendation basis to the telecommunication service recommendation module 304.

[0107] As for other specific parts of the device embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0108] Optionally, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0109] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0110] Figure 7 800 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0111] Reference Figure 7 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0112] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0113] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0114] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0115] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0116] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0117] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0118] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0119] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0120] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0121] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of an electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0122] Figure 819 is a block diagram of a computer-readable storage medium 1900 shown in the present application. For example, the computer-readable storage medium 1900 may be provided as a server.

[0123] Reference Figure 8 , the computer-readable storage medium 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as an application, that can be executed by the processing component 1922. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0124] The computer readable storage medium 1900 may also include a power supply component 1926 configured to perform power management of the computer readable storage medium 1900, a wired or wireless network interface 1950 configured to connect the computer readable storage medium 1900 to a network, and an input / output (I / O) interface 1958. The computer readable storage medium 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0125] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0127] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0128] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0129] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0130] In the 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 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.

[0131] The units described 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.

[0132] 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.

[0133] If the functions are implemented in the form of software functional units and sold or used as independent products, they 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 the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0134] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A business recommendation method based on big data and artificial intelligence, characterized in that: The method comprises: Step 1: Using the user's mobile phone number as a collection condition, collect the user's subscribed telecommunications services through big data and establish a user information database; Step 2: Based on the privacy protection measures, statistics are collected on the use of telecommunication services by users through big data, and characteristic indicators of the use of telecommunication services are calculated to generate user characteristic indicator levels; Step 3: Based on the user information database, the influence level of the user within a preset time is calculated by the user characteristic index level to generate a comparison factor; Step 4: Based on the comparison factor, the comparison is performed with a preset standard, and a recommendation of telecommunication services to the user is determined according to the comparison result.

2. The method according to claim 1, characterized in that The telecommunications services subscribed by users are collected through big data, including: Using the user's mobile phone number as the collection condition, the subscription of telecommunications services and the duration of use of the subscription of telecommunications services are counted through big data to establish a personal information file; Based on artificial intelligence algorithms, parallel information files are established by associating information with the same user personal information but different user mobile phone numbers; By integrating the personal information files and parallel information files, a user information database is generated.

3. The method according to claim 1, characterized in that: Statistics on users’ telecommunication services are collected through big data, including: Collect statistics on users' use of telecommunications services through information encryption to determine the types of telecommunications services used; Based on the type of telecommunication service used, the corresponding frequency of use, duration of use, duration of use, and usage fee are determined through an artificial intelligence algorithm to establish usage characteristic indicators for the telecommunication service used.

4. The method according to claim 3, characterized in that Calculating the usage characteristic index of the telecommunication service includes: Based on the preset usage frequency statistical interval, the usage frequency index level is generated by setting the impact level of the usage frequency; Based on the preset usage time statistical interval, the usage time indicator level is generated by setting the impact level of the usage time; Based on the preset duration value, when the duration is less than the preset duration value, the duration statistical value is the duration; when the duration is not less than the preset duration value, the duration statistical value is the preset duration value; The user characteristic index level is generated by integrating the usage frequency index level, the usage duration index level, and the duration statistics.

5. The method according to claim 4, characterized in that The influence level of the user within the preset time is calculated by the user characteristic indicator level, including: Based on artificial intelligence algorithms, the usage impact factor is generated by calculating the usage frequency within the user's usage time; Based on the usage impact factor, a continuous impact factor is generated by calculating the continuous impact level of the user within the continuous duration; Based on artificial intelligence algorithms, the service costs are generated by counting the user's usage fees during the usage period; Based on the service cost, a cost impact factor is generated by calculating the cost impact level of the user during the duration of the service time; The comparison factor is generated by integrating the continuous impact factor, the usage impact factor, and the cost impact factor.

6. The method according to claim 5, characterized in that By comparing with the preset standards, the telecom services recommended to users are determined based on the comparison results, including: Based on an artificial intelligence algorithm, a dividing line for sending telecommunication service recommendation information to users is established by comparing the user information database with the user characteristic index level, and a preset standard is generated; The preset standards include preset usage impact factor standards, preset continuous impact factor standards, and preset cost impact factor standards.

7. The method according to claim 6, characterized in that By comparing with the preset standards, the telecom services recommended to users are determined based on the comparison results, including: If the continuous impact factor is greater than the preset continuous impact factor standard, recommending telecommunication services to the user; If the continuous impact factor is not greater than the preset continuous impact factor standard, no telecommunication service is recommended to the user; If the usage impact factor is greater than a preset usage impact factor standard, recommending telecommunication services to the user; If the usage impact factor is not greater than the preset usage impact factor standard, no telecommunication service is recommended to the user; If the cost impact factor is greater than the preset cost impact factor standard, recommending telecommunication services to the user; If the cost impact factor is not greater than the preset cost impact factor standard, no telecommunication service is recommended to the user.

8. A business recommendation device based on big data and artificial intelligence, characterized in that: include: The user information module is used to collect the user's subscribed telecommunications services through big data based on the user's mobile phone number and establish a user information database; An information encryption collection module is used to collect statistics on the use of telecommunication services by users through big data and calculate the use characteristic indicators of the telecommunication services; Data analysis module, used to calculate the impact level within the time preset by the user; The telecommunications service recommendation module is used to compare with preset standards and decide to recommend telecommunications services to users based on the comparison results.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and 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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