A network search method based on PLMN priority self-learning

Abstract

This invention relates to the field of network communication technology, specifically to a network search method based on PLMN priority self-learning. The method comprises the following steps: S1: Performing a network search, connecting to a preset PLMN and network standard RAT priority list, and determining whether it is the first time searching the network upon startup; S2: Searching the network according to the preset PLMN and network standard RAT priority list, pre-setting the corresponding PLMN and network standard RAT records; S3: Searching the network according to the updated PLMN and network standard RAT priority list, pre-setting the corresponding PLMN and network standard RAT records. This invention can sequentially search the network based on the preset PLMN and network standard RAT priority list, sorted by priority. It offers high flexibility in network searching and can dynamically update the PLMN and network standard RAT priority list based on information from each successful network search.

Description

Technical Field

[0001] This invention relates to the field of network communication technology, and more specifically to a network search method based on PLMN priority self-learning. Background Technology

[0002] Currently, when the communication module first powers on, it attempts to register a network using PLMN+RAT (the highest level). For subsequent power-on network searches, it searches historical network registration information (PLMN+RAT). If historical registration fails, it attempts to register using the same method as the initial power-on search. If all RAT capabilities corresponding to the current PLMN fail to register, the terminal's network registration fails. During operation, the following situations may occur: In the case of the terminal's first power-on, there are many possible combinations of existing PLMNs and RAT network standards (5G>4G>3G>2G) supported by the terminal. In this situation, searching each network sequentially will result in two scenarios: The network found may be the one corresponding to the highest network standard RAT under the current PLMN, thus quickly finding the target network. If the network is not found, or if the RAT corresponding to the network is ranked low, the network search time will be longer. In the worst case, it may be the last one, resulting in a very long network search time. At the same time, the current area may not contain the network corresponding to the PLMN+RAT. In this case, the terminal device cannot search for networks one by one, which not only consumes a lot of network search time, but also ultimately fails. In addition, if the terminal is not powered on for the first time, there will still be network search failures due to historical PLMN+RAT information. Then, the network search will be performed in the order of PLMN+RAT (5G>4G>3G>2G), which is the same as the first time it is powered on. In both cases, it will consume a lot of network search time, and the system real-time performance is poor. Summary of the Invention

[0003] The technical problem to be solved by this invention is that existing network search methods for devices are not flexible enough, and in complex application scenarios, the network search time is often too long. They also have a high degree of randomness, which is not conducive to the stable communication requirements of devices and results in a poor user experience.

[0004] To solve the above technical problems, the present invention adopts the following technical solution: a network search method based on PLMN priority self-learning, comprising the following steps:

[0005] S1: Perform network search, connect to the preset PLMN and network type RAT priority list, and determine whether it is the first time to search for the network upon power-on. If it is the first time to search for the network upon power-on, proceed to step S2; otherwise, proceed to step S3.

[0006] S2: According to the preset PLMN and network type RAT priority list, the corresponding PLMN and network type RAT records are preset to search for networks and determine whether the network registration is successful. When the network registration is successful, the corresponding PLMN and network type RAT records in the PLMN and network type RAT priority list are updated. When the network registration fails, the next priority PLMN and network type RAT records are switched to search for networks. This process is repeated until the network registration is successful.

[0007] S3: Following the updated PLMN and network standard RAT priority list, pre-set the corresponding PLMN and network standard RAT records to search for networks, and determine whether network registration is successful. If network registration is successful, update the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list. If network registration fails, switch to the next priority PLMN and network standard RAT records to search for networks, and repeat this process until network registration is successful.

[0008] When this invention is working, it can sequentially search for PLMN and network standard RAT records according to the priority list of preset PLMN and network standard RAT, which are sorted by priority. The network search is highly flexible, and the PLMN and network standard RAT priority list can be dynamically updated according to the information of each successful network search. This allows for customized adjustment of several PLMN and network standard RAT records based on actual conditions, making it widely applicable and versatile.

[0009] Preferably, step S1 further includes the following steps: when the network registration is successful or fails, receiving feedback information and updating the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list, and reordering several PLMN and network standard RAT records in the PLMN and network standard RAT priority list according to a preset sorting rule.

[0010] Preferably, in step S1, when receiving feedback information and updating the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list upon successful or failed network registration, the PLMN and network standard RAT records in the priority list are reordered according to a preset sorting rule. The following steps are adopted: when successful network registration, the registration count of the corresponding PLMN and network standard RAT record is incremented by one; when it is not the first power-on and the network search fails, the registration count of the corresponding PLMN and network standard RAT record is decremented by one. According to a preset dynamic sorting cycle, several PLMN and network standard RAT records are periodically sorted in descending order according to the registration count of each PLMN and network standard RAT record.

[0011] Preferably, each PLMN and network standard RAT record in the priority list is assigned a weight for different application scenarios.

[0012] Preferably, step S1 further includes the following steps: when performing network search, information is collected simultaneously. The collected information includes at least one of the following: current environment information, current network information, and current device information. The current application scenario is determined based on the collected information. Several PLMN and network standard RAT records are reordered based on the application scenario and the weight of each PLMN and network standard RAT record.

[0013] Preferably, step S2 further includes the following steps: when network registration fails, network registration failure information is recorded synchronously. The network registration failure information includes at least one of the following: current environment information, current network information, and current device information. The weights of the corresponding PLMN and network type RAT records in the corresponding application scenario are adjusted based on the network registration failure information.

[0014] Preferably, in step S1, based on the weights of several PLMN and network-standard RAT records in various application scenarios in the PLMN and network-standard RAT priority list, cluster analysis is performed on the weights of several PLMN and network-standard RAT records in the corresponding application scenarios, and several record sets are divided according to the preset correlation degree.

[0015] Preferably, step S2 further includes the following step: when continuous network registration fails, depending on whether some PLMN and network standard RAT records in the preset number of PLMN and network standard RAT records of the failed network registration exist in the same record set, if the number of PLMN and network standard RAT records in the same record set exceeds a preset threshold, all PLMN and network standard RAT records in the record set are temporarily removed from the PLMN and network standard RAT priority list.

[0016] Preferably, step S1 further includes the following step: based on the characteristics of several record sets, establishing a graph database that includes the relationship between any two record sets in the several record sets.

[0017] Preferably, step S2 further includes the following steps: when continuous network registration fails, depending on whether some PLMN and network standard RAT records in the preset number of failed network registrations exist in the same record set, if the number of PLMN and network standard RAT records in the same record set exceeds a preset threshold, another record set with a corresponding relationship to the record set is obtained from the graph database, and the network is searched sequentially based on several PLMN and network standard RAT records in the record set, and this process is repeated until successful network registration.

[0018] The beneficial technical effects of this invention include:

[0019] This invention can sequentially search for PLMN and network standard RAT records according to their priority in a preset PLMN and network standard RAT priority list. It has high flexibility in searching for networks and can dynamically update the PLMN and network standard RAT priority list based on the information of each successful network search. This allows for customized adjustments to several PLMN and network standard RAT records based on actual conditions, making it widely applicable and versatile.

[0020] Other features and advantages of the present invention will be disclosed in detail in the following detailed description and accompanying drawings. Attached Figure Description

[0021] The invention will be further described below with reference to the accompanying drawings:

[0022] Figure 1 This is a flowchart illustrating the workflow of a network search method based on PLMN priority self-learning. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be explained and described below with reference to the accompanying drawings. However, the following embodiments are only preferred embodiments of the present invention and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments in the implementation methods without creative effort are all within the protection scope of the present invention.

[0024] In the following description, terms such as “inner,” “outer,” “upper,” “lower,” “left,” and “right” are used only to indicate orientation or positional relationship for the convenience of describing the embodiments and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0025] Example 1:

[0026] Please see Figure 1This embodiment discloses a network search method based on PLMN priority self-learning, including the following steps:

[0027] S1: Perform network search, connect to the preset PLMN and network type RAT priority list, and determine whether it is the first time to search for the network upon power-on. If it is the first time to search for the network upon power-on, proceed to step S2; otherwise, proceed to step S3.

[0028] S2: According to the preset PLMN and network type RAT priority list, the corresponding PLMN and network type RAT records are preset to search for networks and determine whether the network registration is successful. When the network registration is successful, the corresponding PLMN and network type RAT records in the PLMN and network type RAT priority list are updated. When the network registration fails, the next priority PLMN and network type RAT records are switched to search for networks. This process is repeated until the network registration is successful.

[0029] S3: Following the updated PLMN and network standard RAT priority list, pre-set the corresponding PLMN and network standard RAT records to search for networks, and determine whether network registration is successful. If network registration is successful, update the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list. If network registration fails, switch to the next priority PLMN and network standard RAT records to search for networks, and repeat this process until network registration is successful.

[0030] When this embodiment is working, it can sequentially search for PLMN and network standard RAT records according to the priority list of preset PLMN and network standard RAT, which are sorted by priority. The network search is highly flexible, and the PLMN and network standard RAT priority list can be dynamically updated according to the information of each successful network search. This allows for customized adjustment of several PLMN and network standard RAT records based on the actual situation, making it widely applicable and versatile.

[0031] Preferably, step S1 further includes the following steps: when the network registration is successful or fails, receiving feedback information and updating the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list, and reordering several PLMN and network standard RAT records in the PLMN and network standard RAT priority list according to a preset sorting rule.

[0032] In practical implementation, the registration count is updated. In step S1, when successful or unsuccessful network registration, the system receives feedback information and updates the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list. This involves reordering several PLMN and network standard RAT records in the priority list according to a preset sorting rule. Specifically, when successful network registration, the registration count of the corresponding PLMN and network standard RAT record is incremented by one. When it is not the first power-on and network search fails, the registration count of the corresponding PLMN and network standard RAT record is incremented. The registration count of a PLMN and network standard RAT record is decremented by one. For example, a record with PLMN 46000 and network standard RAT 5G has 16 registration counts. When it successfully registers again, the registration count is incremented to 17. Conversely, if registration fails, the registration count is decremented to 15. This, combined with the registration counts of other PLMN and network standard RAT records, allows for priority sorting of several PLMN and network standard RAT records. At the same time, according to a preset dynamic sorting cycle, several PLMN and network standard RAT records are periodically sorted in descending order based on the registration count of each PLMN and network standard RAT record.

[0033] Example 2:

[0034] This embodiment provides a network search method based on PLMN priority self-learning. The similarities with other embodiments will not be repeated. The differences will be explained in detail below.

[0035] In this embodiment, each PLMN and network standard RAT record in the priority list is assigned a weight for different application scenarios.

[0036] In specific implementation, step S1 also includes the following steps: when performing network search, information is collected simultaneously. The collected information includes at least one of the following: current environment information, current network information, and current device information. The current application scenario is determined based on the collected information. Based on the application scenario and the weight of each PLMN and network standard RAT record, several PLMN and network standard RAT records are reordered. At this time, the sorting of several PLMN and network standard RAT records can be further optimized according to the application scenario, thereby maximizing the success rate of network search with fewer search attempts.

[0037] As a further improvement to this embodiment, after the factory release with manually preset thresholds, in order to further improve the adaptability in different environments, step S2 also includes the following steps: when network registration fails, network registration failure information is recorded synchronously. The network registration failure information includes at least one of the following: current environment information, current network information, and current device information. By adjusting the weights of the corresponding PLMN and network type RAT records in the corresponding application scenario through the network registration failure information, personalized adjustments can be achieved in different environments without manual adjustment. The accuracy is high, and the success rate of network search can be further improved with fewer network search attempts.

[0038] Example 3:

[0039] This embodiment provides a network search method based on PLMN priority self-learning. The similarities with other embodiments will not be repeated. The differences will be explained in detail below.

[0040] Preferably, in step S1, based on the weights of several PLMN and network-standard RAT records in various application scenarios in the PLMN and network-standard RAT priority list, cluster analysis is performed on the weights of several PLMN and network-standard RAT records in the corresponding application scenarios, and several record sets are divided according to the preset correlation degree.

[0041] During operation, step S2 further includes the following steps: when consecutive network registration fails, depending on whether some PLMN and network standard RAT records in the preset number of failed network registrations exist in the same record set, if the number of PLMN and network standard RAT records in the same record set exceeds a preset threshold, all PLMN and network standard RAT records in that record set are temporarily removed from the PLMN and network standard RAT priority list. This can remove PLMN and network standard RAT records with a high probability of network search failure, and preferably preset PLMN and network standard RAT records with a high probability of network search success can further improve the probability of network search success.

[0042] As a further improvement to this embodiment, step S1 also includes the following step: based on the characteristics of several record sets, a graph database is established that includes the relationship between any two record sets in the several record sets.

[0043] In specific implementation, step S2 further includes the following steps: when consecutive network registration fails, depending on whether some PLMN and network standard RAT records in the preset number of failed network registrations exist in the same record set, if the number of PLMN and network standard RAT records in the same record set exceeds a preset threshold, another record set with a corresponding relationship to the record set is obtained from the graph database, and the network is searched sequentially based on several PLMN and network standard RAT records in the record set. This process is repeated until successful network registration. By switching record sets, the probability of successful network search can be increased as much as possible in a harsh network environment.

[0044] The beneficial technical effects of this embodiment include: the present invention can sequentially search for PLMN and network standard RAT records according to the priority sorting of the preset PLMN and network standard RAT priority list, which has high flexibility in network search and can dynamically update the PLMN and network standard RAT priority list according to the information of each successful network search, so that several PLMN and network standard RAT records can be customized and adjusted according to the actual situation, which has a wide range of applications and good versatility.

[0045] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Those skilled in the art should understand that the present invention includes, but is not limited to, the contents described in the accompanying drawings and the specific embodiments above. Any modifications that do not depart from the functional and structural principles of the present invention will be included within the scope of the claims.

Claims

1. A network search method based on PLMN priority self-learning, characterized in that, Includes the following steps: S1: Perform network search, connect to the preset PLMN and network type RAT priority list, and determine whether it is the first time to search for the network upon power-on. If it is the first time to search for the network upon power-on, proceed to step S2; otherwise, proceed to step S3. In step S1, when successfully registering a network or when registration fails, the system receives feedback information and updates the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list. The system reorders several PLMN and network standard RAT records in the PLMN and network standard RAT priority list according to a preset sorting rule. The steps are as follows: when registration is successful, the registration count of the corresponding PLMN and network standard RAT record is incremented by one; when it is not the first power-on and the network search fails, the registration count of the corresponding PLMN and network standard RAT record is decremented by one. According to a preset dynamic sorting cycle, several PLMN and network standard RAT records are periodically sorted in descending order based on the registration count of each PLMN and network standard RAT record. Each PLMN and network standard RAT record in the priority list is assigned a weight in different application scenarios. Based on the weights of several PLMN and network-based RAT records in various application scenarios in the priority list of PLMN and network-based RAT, cluster analysis is performed on the weights of several PLMN and network-based RAT records in the corresponding application scenarios, and several record sets are divided according to the preset correlation degree. S2: According to the preset PLMN and network type RAT priority list, the corresponding PLMN and network type RAT records are preset to search for networks and determine whether the network registration is successful. When the network registration is successful, the corresponding PLMN and network type RAT records in the PLMN and network type RAT priority list are updated. When the network registration fails, the next priority PLMN and network type RAT records are switched to search for networks. This process is repeated until the network registration is successful. In step S2, the following steps are also included: when continuous network registration fails, depending on whether some PLMN and network standard RAT records in the preset number of PLMN and network standard RAT records of the failed network registration exist in the same record set, when the number of PLMN and network standard RAT records in the same record set exceeds a preset threshold, all PLMN and network standard RAT records in the record set are temporarily removed from the PLMN and network standard RAT priority list; S3: Following the updated PLMN and network standard RAT priority list, pre-set the corresponding PLMN and network standard RAT records to search for networks, and determine whether network registration is successful. If network registration is successful, update the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list. If network registration fails, switch to the next priority PLMN and network standard RAT records to search for networks, and repeat this process until network registration is successful.

2. The network search method based on PLMN priority self-learning according to claim 1, characterized in that: In step S1, when successful or unsuccessful network registration, the system receives feedback information and updates the corresponding PLMN and network standard RAT records in the PLMN and network standard RAT priority list. It then reorders several PLMN and network standard RAT records in the priority list according to a preset sorting rule. The steps are as follows: when successful network registration, the registration count of the corresponding PLMN and network standard RAT record is incremented by one; when it is not the first power-on and network search fails, the registration count of the corresponding PLMN and network standard RAT record is decremented by one. Following a preset dynamic sorting cycle, several PLMN and network standard RAT records are periodically sorted in descending order based on their registration count.

3. The network search method based on PLMN priority self-learning according to claim 1, characterized in that: Step S1 also includes the following steps: when performing network search, information is collected simultaneously. The collected information includes at least one of the following: current environment information, current network information, and current device information. The current application scenario is determined based on the collected information. Several PLMN and network standard RAT records are reordered based on the application scenario and the weight of each PLMN and network standard RAT record.

4. The network search method based on PLMN priority self-learning according to claim 1, characterized in that: Step S2 also includes the following steps: when network registration fails, network registration failure information is recorded synchronously. The network registration failure information includes at least one of the following: current environment information, current network information, and current device information. The weights of the corresponding PLMN and network type RAT records in the corresponding application scenario are adjusted based on the network registration failure information.

5. The network search method based on PLMN priority self-learning according to claim 1, characterized in that: Step S1 also includes the following step: based on the characteristics of several record sets, establish a graph database that includes the relationship between any two record sets in the several record sets.

6. The network search method based on PLMN priority self-learning according to claim 5, characterized in that: Step S2 further includes the following steps: when consecutive network registration fails, depending on whether some PLMN and network standard RAT records in the preset number of failed network registrations exist in the same record set, if the number of PLMN and network standard RAT records in the same record set exceeds a preset threshold, another record set with a corresponding relationship to the record set is obtained from the graph database, and the network is searched sequentially based on several PLMN and network standard RAT records in the record set, and this process is repeated until successful network registration.

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