Intelligent multi-network sensing system and method, storage medium and computer equipment
Through an intelligent multi-network perception system, AI large-scale models are used to analyze network information, generate prediction information, and realize intelligent network selection and optimization of terminal devices, solving the problems of network performance and multi-network switching in complex environments, and improving the stability and efficiency of network connections.
Patent Information
- Application Number
- CN202411875620.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-19
- Publication Date
- 2025-04-01
AI Technical Summary
The prior art is difficult to achieve efficient network performance and multi-network switching in complex environments in IoT terminal devices, resulting in high costs, long project cycles and network blind spots that are difficult to solve.
Design an intelligent multi-network perception system, including Internet of Things terminals, information acquisition plug-ins, multi-network perception platforms and data analysis platforms, use AI large models to analyze network information, generate prediction information, and realize intelligent network selection and optimization of terminal devices.
By accurately drawing network coverage maps, optimizing network blind spots, realizing intelligent network selection, improving network connectivity stability and efficiency, and reducing operation and maintenance costs.
Smart Images
Figure CN119743764A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of terminal wireless communication technology, and in particular to an intelligent multi-network perception system, method, storage medium and computer equipment. Background Art
[0002] In the wave of rapid development of the Internet of Things industry, IoT terminals have spread to every corner of the world, which has brought unprecedented challenges to the operator network. In order to ensure the network performance of terminal equipment in various complex environments, the traditional approach is to require detailed detection of the network status of the target environment before project deployment, or to use terminal equipment that supports multi-network switching (that is, integrating multiple operator SIM cards and performing intelligent switching between operators based on actual network conditions). However, these methods are not only costly, but also rely on the intervention of operators to solve network blind spots, which undoubtedly prolongs the project cycle and increases uncertainty. Summary of the invention
[0003] The present invention provides an intelligent multi-network perception system, method, storage medium and computer device for optimizing network connection experience and improving network resource utilization, which can solve at least one of the above technical problems.
[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:
[0005] An intelligent multi-network perception system, including an Internet of Things terminal, an information collection plug-in, an Internet of Things base station, a core network, a multi-network perception platform, an information storage database and a data analysis platform;
[0006] There are multiple IoT terminals, which are deployed in various environments and use mobile IoT networks for communication. There are multiple information collection plug-ins, which are embedded in each IoT terminal and used to collect network information of the IoT base stations around the current IoT terminal, and report the network information to the multi-network perception platform via the core network. The multi-network perception platform summarizes and reports each network information to the information storage database. The information storage database is connected to the data analysis platform. The data analysis platform has a built-in AI large model for generating prediction information based on the analysis and processing of network information. The data analysis platform sends the prediction information to the IoT terminal via the multi-network perception platform.
[0007] Furthermore, the information collection plug-in calls the communication module / communication chip capability scan of the IoT terminal through an AT command / API interface to obtain the network signal strength information of the surrounding IoT base stations and each frequency band;
[0008] The information collection plug-in calls the communication module / communication chip capability of the IoT terminal through AT instructions / API interface to perform actual network capability detection;
[0009] The information collection plug-in establishes a communication connection with the multi-network perception platform, reports the collected data information to the multi-network perception platform, and receives feedback information issued by the multi-network perception platform;
[0010] The information collection plug-in synchronizes information with the Internet of Things terminal and accepts the management and control of the Internet of Things terminal. The synchronized information at least includes the working status information of the Internet of Things terminal, the working status information of the information collection plug-in, the control instructions of the information collection plug-in and the notification information of the multi-network perception platform.
[0011] Furthermore, the information collection plug-in is software that can be integrated and installed inside the Internet of Things terminal, and is used to monitor the working status of the Internet of Things terminal and perform idle time statistics at time intervals of a period of T. When the information collection plug-in finds that the Internet of Things terminal is idle in a certain period of time for several consecutive days, several idle time periods within a day are selected for network scanning and reporting of network information, and the working status in the prediction information fed back by the multi-network perception platform is synchronized to the Internet of Things terminal.
[0012] Furthermore, the IoT terminal can stop the operation of the information collection plug-in during a certain period of time according to its own needs to ensure that its own business is not affected, and then spontaneously start the plug-in after the current business ends.
[0013] Furthermore, the Internet of Things terminal can actively enable the network scanning function of the information collection plug-in.
[0014] Furthermore, the network information acquired by the multi-network perception platform includes at least device information IMEI, base station ID, signal strength information of each frequency band, location information and actual network quality, and the prediction information issued by the multi-network perception platform covers at least multiple dimensions of time, space and network frequency band.
[0015] Furthermore, the data analysis platform uses the analysis and learning capabilities of the AI big model to analyze and learn network information. The resulting predictive information can automatically determine the optimal operator network selection and feed it back to the multi-network perception platform, which will then send it to the corresponding IoT terminal.
[0016] An intelligent multi-network perception method, implemented using the intelligent multi-network perception system, comprises the following steps:
[0017] S1. The information collection plug-in monitors the working status of the IoT terminal and confirms the idle time period of the IoT terminal;
[0018] S2. The information collection plug-in calls the communication module capability to scan the network signal strength of each frequency band of the surrounding IoT base stations during the idle time period of the IoT terminal, and integrates the data into network information by combining the positioning information, base station ID information, timestamp and device IMEI information. The network information is reported to the multi-network perception platform via the core network;
[0019] S3, the multi-network perception platform processes the received network information and files it into the information storage database for use by the AI big model in the data analysis platform for big data analysis and training, generates prediction information, and the data analysis platform feeds the prediction information back to the multi-network perception platform;
[0020] S4, the information collection plug-in receives the prediction information issued by the multi-network perception platform through the core network, and synchronously applies it to the Internet of Things terminal;
[0021] S5, repeat S1 to cycle the steps.
[0022] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the above-mentioned intelligent multi-network perception method.
[0023] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned intelligent multi-network perception method.
[0024] The beneficial effects of the present invention are embodied in:
[0025] 1. Network coverage optimization: Through integrated network signal data collection, the network coverage map in the current environment can be accurately drawn to help operators identify and optimize network blind spots.
[0026] 2. Intelligent network selection: Use AI big models to learn network data to achieve intelligent network selection for terminal devices and improve the stability and efficiency of network connections.
[0027] 3. Data-driven decision-making: Based on big data analysis, it provides detailed network performance reports to support operators and customers in making more scientific network choices and service deployments. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.
[0029] Figure 1 It is a structural block diagram of the intelligent multi-network perception system according to an embodiment of the present invention.
[0030] Figure 2 It is a flowchart of an intelligent multi-network perception method according to an embodiment of the present invention.
[0031] Figure 3 It is a structural block diagram of a computer device according to an embodiment of the present invention.
[0032] The components in the attached figure are marked as: 1. Internet of Things terminal; 2. Information collection plug-in; 3. Internet of Things base station; 4. Core network; 5. Multi-network perception platform; 6. Information storage database; 7. Data analysis platform. DETAILED DESCRIPTION
[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. In the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
[0034] It should be noted that the meaning of "and / or" appearing in the full text includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or solutions that satisfy both A and B. In addition, "multiple" means more than two. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.
[0035] See also Figure 1 , an embodiment of the present invention provides an intelligent multi-network perception system, including an Internet of Things terminal 1, an information collection plug-in 2, an Internet of Things base station 3, a core network 4, a multi-network perception platform 5, an information storage database 6 and a data analysis platform 7;
[0036] There are multiple IoT terminals 1, which are deployed in various environments and use mobile IoT network communication. There are multiple information collection plug-ins 2, which are embedded in each IoT terminal 1 and are used to collect network information of the IoT base station 3 around the current IoT terminal 1, and report the network information to the multi-network perception platform 5 via the core network 4. The multi-network perception platform 5 summarizes and reports each network information to the information storage database 6. The information storage database 6 is connected to the data analysis platform 7. The data analysis platform 7 has a built-in AI large model for generating prediction information based on the analysis and processing of network information, and the data analysis platform 7 sends the prediction information to the IoT terminal 1 via the multi-network perception platform 5.
[0037] This system deploys IoT terminals as anchor points to accurately collect information such as signal quality, geographic location, and time in the environment, and aggregates this data to a centralized platform. It uses advanced big data technology and artificial intelligence models to analyze and process this information, thereby accurately identifying the best operator network options for different locations and time periods. Based on this, the system can provide users with customized recommendations for the best network selection solution, ensuring that users always enjoy efficient and stable network services.
[0038] In addition, because the system can monitor the operating status of the operator's network in real time, it can provide real-time feedback to the operator's network. This helps operators to optimize the network and improve services more effectively, thereby further improving the quality of network services and user experience. In this way, the system not only optimizes the user's network selection process, but also provides strong technical support for the operator's network management and service upgrades.
[0039] In this embodiment, the information collection plug-in 2 calls the communication module / communication chip capability scan of the Internet of Things terminal 1 through the AT command / API interface to obtain the surrounding Internet of Things base stations 3 and the network signal strength information of each frequency band;
[0040] The information collection plug-in 2 calls the communication module / communication chip capability of the Internet of Things terminal 1 through AT instructions / API interface to perform actual network capability detection (ping test to obtain delay and packet loss rate);
[0041] The information collection plug-in 2 establishes a communication connection with the multi-network perception platform 5, reports the collected data information (including information such as device IMEI, timestamp, location information, base station ID, signal frequency band, signal strength, etc.) to the multi-network perception platform 5, and receives feedback information (such as network usage suggestions, etc.) issued by the multi-network perception platform 5;
[0042] The information collection plug-in 2 synchronizes information with the Internet of Things terminal 1 and accepts the management and control of the Internet of Things terminal 1. The synchronized information at least includes the working status information of the Internet of Things terminal 1, the working status information of the information collection plug-in 2, the control instructions of the information collection plug-in 2 (such as start, stop, scan) and the notification information of the multi-network perception platform 5.
[0043] In this embodiment, the information collection plug-in 2 is software that can be integrated and installed inside the Internet of Things terminal 1, and is used to monitor the working status of the Internet of Things terminal 1, and perform idle time statistics at time intervals of a period of T (T≤10min). When the information collection plug-in 2 counts that the Internet of Things terminal 1 is idle in a certain period of time for several consecutive days, it selects several idle time periods in a day (determined by considering the business, power consumption, etc. of the equipment) to perform network scanning and report network information, and synchronizes the working status in the prediction information fed back by the multi-network perception platform 5 to the Internet of Things terminal 1.
[0044] In this embodiment, the IoT terminal 1 can stop the operation of the information collection plug-in 2 in a certain period of time according to its own needs to ensure that its own business is not affected, and then spontaneously start the plug-in after the current business ends;
[0045] In the above embodiment, the Internet of Things terminal 1 can also actively enable the network scanning function of the information collection plug-in 2.
[0046] Through the above implementation, the Internet of Things terminal 1 can be transformed without affecting its original service as much as possible, so as to collect the signal strength information of each frequency band of the environment where the Internet of Things terminal 1 is located.
[0047] In this embodiment, the network information acquired by the multi-network perception platform 5 includes at least device information IMEI, base station ID, signal strength information of each frequency band, location information and actual network quality (ping delay, packet loss rate). The prediction information sent by the multi-network perception platform 5 covers at least multiple dimensions of time, space and network frequency band.
[0048] When enough information from the IoT terminals 1 is gathered on the multi-network perception platform 5, a network coverage report can be generated in multiple dimensions such as time, space, and network frequency bands through big data analysis, which can be used by operators to optimize their own services and by customers to optimize their own business.
[0049] In this embodiment, the data analysis platform 7 uses the analysis and learning capabilities of the AI big model to analyze and learn network information. The resulting prediction information can automatically determine the optimal operator network selection and feed back to the multi-network perception platform 5, which is then sent down by the multi-network perception platform 5 to the corresponding IoT terminal 1.
[0050] The data analysis platform 7 uses an AI big model to analyze and learn the network information of multiple IoT terminals 1, and realizes a service robot that can automatically determine the optimal operator network selection, thereby realizing automatic network selection optimization for network equipment, and can also provide customers with operator network selection suggestions based on relevant information of the customer's equipment deployment plan.
[0051] See also Figure 2 The embodiment of the present invention further provides an intelligent multi-network perception method, which is implemented by the intelligent multi-network perception system and includes the following steps:
[0052] S1, the information collection plug-in 2 monitors the working status of the Internet of Things terminal 1 and confirms the idle time period of the Internet of Things terminal 1;
[0053] S2, the information collection plug-in 2 calls the communication module capability during the idle time period of the IoT terminal 1 to scan the network signal strength of each frequency band of the surrounding IoT base stations 3, and integrates the data into network information by combining the positioning information, base station ID information, timestamp and device IMEI information, and the network information is reported to the multi-network perception platform 5 via the core network 4;
[0054] S3, the multi-network perception platform 5 processes the received network information and files it into the information storage database 6 for use by the AI big model in the data analysis platform 7 to perform big data analysis and training, generate prediction information, and the data analysis platform 7 feeds back the prediction information to the multi-network perception platform 5;
[0055] S4, the information collection plug-in 2 receives the prediction information issued by the multi-network perception platform 5 via the core network 4, and synchronously applies it to the Internet of Things terminal 1;
[0056] S5, repeat S1 to cycle the steps.
[0057] Through the above method, the multi-network perception platform can regularly generate network coverage heat maps of each operator and related report information through big data analysis for customers to view and call; the multi-network perception platform can also provide intelligent network selection services and network selection solution recommendations through AI big models, and intelligent network selection management and control services to help customer terminals select operator networks; the multi-network perception platform can also communicate with the IoT terminal through the information collection plug-in integrated in the IoT terminal, realize the linkage between the platform and the terminal equipment, and thus automatically realize the adjustment and optimization of the terminal network.
[0058] In this application, the IoT terminal 1 specifically refers to an IoT device that has been deployed in various environments and uses a mobile IoT network to communicate. It can be a device specially deployed for this solution, or it can be a reused deployed device. The deployment environment can be a work or production environment such as office buildings, laboratories, factories, hospitals, schools, etc. The deployment environment is diverse and complex.
[0059] In this application, the AI big model is a model in the existing technology that uses machine learning, big data analysis, neural network deep learning and other learning methods to analyze and process sample data, and is used in the field of computer networks.
[0060] In the present application, the prediction information generated by the data analysis platform 7 is fed back to the customer in various forms such as network reports and network suggestions, or is directly output to the Internet of Things terminal 1.
[0061] Below, this application will be further explained in conjunction with an embodiment: Smart Park Upgrade and Reconstruction
[0062] The implementation plan is as follows:
[0063] 1. Deploy IoT terminals with network information collection capabilities:
[0064] For the park environment, select IoT terminals in key locations to integrate information collection plug-ins or directly deploy independent information collection terminal devices to ensure that the collected information can cover various scenarios in the park.
[0065] 2. Establish a multi-network perception platform:
[0066] Receive, store and analyze network data from terminal devices, generate a "heat map" of network coverage of each operator in the park through big data analysis technology, and then use AI big models to learn network data to provide the best network selection plan for terminal devices.
[0067] The implementation results are as follows:
[0068] Operators: The network coverage “heat map” provided by the platform helps operators identify coverage blind spots, optimize base station configuration, and improve network service quality.
[0069] Client: When enterprises deploy new equipment or upgrade old equipment, they can choose the operator network service that best suits their business needs based on the network performance report provided by the platform.
[0070] In this way, through the implementation of this solution, the network service experience of the smart park can be improved, the network resource utilization rate can be increased, and the deployment and operation costs can be reduced.
[0071] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned intelligent multi-network perception method.
[0072] See also Figure 3 An embodiment of the present invention further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned intelligent multi-network perception method.
[0073] The embodiment of the present invention also provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the steps of the above-mentioned intelligent multi-network perception method.
[0074] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above-mentioned intelligent multi-network perception method.
[0075] It should be noted that those skilled in the art can understand that all or part of the steps implemented in the embodiments of the present invention can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using hardware, it can be implemented in whole or in part in the form of purchasing standard parts or modified parts. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).
[0076] In summary, the present invention aims to realize a highly intensive platform, transform the deployed IoT terminals into "tentacles", and "draw" the network coverage of multiple operators in a certain park, city or even region into a "network map", thereby providing an all-round "God's perspective" for the optimization of operators' network services, selection of customer operator networks, and management of multi-network terminals. Through this innovative approach, the network connection experience is optimized, the utilization rate of network resources is improved, and the operation and maintenance costs are significantly reduced, which promotes the multi-network integration of the IoT industry and ensures the sustainable and healthy development of the industry.
[0077] It should be understood that the examples and implementation modes described herein are for illustrative purposes only and are not intended to limit the present invention. Those skilled in the art may make various modifications or changes based on the examples and implementation modes. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. An intelligent multi-network perception system, characterized in that: It includes an Internet of Things terminal (1), an information collection plug-in (2), an Internet of Things base station (3), a core network (4), a multi-network perception platform (5), an information storage database (6) and a data analysis platform (7); The Internet of Things terminals (1) are provided in a plurality and are respectively deployed in various environments and use a mobile Internet of Things network for communication. The information collection plug-ins (2) are provided in a plurality and are respectively embedded in each of the Internet of Things terminals (1) for collecting network information of the Internet of Things base stations (3) around the current Internet of Things terminal (1), and reporting the network information to the multi-network perception platform (5) via the core network (4). The multi-network perception platform (5) aggregates and reports each network information to the information storage database (6). The information storage database (6) is connected to the data analysis platform (7). The data analysis platform (7) has a built-in AI large model for generating prediction information based on the analysis and processing of network information. The data analysis platform (7) sends the prediction information to the Internet of Things terminal (1) via the multi-network perception platform (5).
2. The intelligent multi-network perception system according to claim 1, characterized in that: The information collection plug-in (2) calls the communication module / communication chip capability scan of the Internet of Things terminal (1) through an AT command / API interface to obtain the surrounding Internet of Things base stations (3) and network signal strength information of each frequency band; The information collection plug-in (2) calls the communication module / communication chip capability of the Internet of Things terminal (1) through an AT command / API interface to perform actual network capability detection; The information collection plug-in (2) establishes a communication connection with the multi-network perception platform (5), reports the collected data information to the multi-network perception platform (5), and receives feedback information issued by the multi-network perception platform (5); The information collection plug-in (2) synchronizes information with the Internet of Things terminal (1) and accepts the management and control of the Internet of Things terminal (1), and the synchronized information at least includes the working status information of the Internet of Things terminal (1), the working status information of the information collection plug-in (2), the control instructions of the information collection plug-in (2) and the notification information of the multi-network perception platform (5).
3. The intelligent multi-network perception system according to claim 2, characterized in that: The information collection plug-in (2) is software that can be integrated and installed inside the Internet of Things terminal (1), and is used to monitor the working status of the Internet of Things terminal (1) and perform idle time statistics at time intervals of a period of T. When the information collection plug-in (2) finds that the Internet of Things terminal (1) is idle during a certain period of time for several consecutive days, it selects several idle time periods within a day to perform network scanning and report network information, and synchronizes the working status in the prediction information fed back by the multi-network perception platform (5) to the Internet of Things terminal (1).
4. The intelligent multi-network perception system according to claim 3, characterized in that: The Internet of Things terminal (1) can stop the operation of the information collection plug-in (2) at a certain time period according to its own needs to ensure that its own business is not affected, and will automatically start the plug-in after the current business is completed.
5. The intelligent multi-network perception system according to claim 3, characterized in that: The Internet of Things terminal (1) can actively enable the network scanning function of the information collection plug-in (2).
6. The intelligent multi-network perception system according to claim 1, characterized in that: The network information acquired by the multi-network perception platform (5) includes at least device information IMEI, base station ID, signal strength information of each frequency band, location information and actual network quality. The prediction information sent by the multi-network perception platform (5) covers at least multiple dimensions of time, space and network frequency band.
7. The intelligent multi-network perception system according to claim 1, characterized in that: The data analysis platform (7) uses the analysis and learning capabilities of the AI big model to analyze and learn network information. The generated prediction information can automatically determine the optimal operator network selection and feed back to the multi-network perception platform (5), which is then sent down by the multi-network perception platform (5) to the corresponding Internet of Things terminal (1).
8. An intelligent multi-network perception method, implemented by the intelligent multi-network perception system as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: S1, the information collection plug-in (2) monitors the working status of the Internet of Things terminal (1) and confirms the idle time period of the Internet of Things terminal (1); S2, the information collection plug-in (2) calls the communication module capability to scan the network signal strength of each frequency band of the surrounding Internet of Things base stations (3) during the idle time of the Internet of Things terminal (1), and integrates the data into network information by combining the positioning information, base station ID information, timestamp and device IMEI information, and the network information is reported to the multi-network perception platform (5) via the core network (4); S3, the multi-network perception platform (5) processes the received network information and files it into the information storage database (6) for use by the AI big model in the data analysis platform (7) to perform big data analysis and training, generate prediction information, and the data analysis platform (7) feeds the prediction information back to the multi-network perception platform (5); S4, the information collection plug-in (2) receives the prediction information sent by the multi-network perception platform (5) via the core network (4), and synchronously applies it to the Internet of Things terminal (1); S5, repeat S1 to cycle the steps.
9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor executes the steps of the intelligent multi-network perception method as claimed in claim 8.
10. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the intelligent multi-network perception method as claimed in claim 8.