Artificial intelligence-based base station clock information synchronization method and system, and related device

Abstract

The present disclosure provides an artificial intelligence-based base station clock information synchronization method and system and related equipment, relating to the field of communication technology. The method comprises: collecting clock state data and / or network performance data of each operator base station in a first time period; fusing the clock state data and / or network performance data of each operator base station in the first time period to construct a cross-operator base station clock synchronization feature matrix; inputting the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model to output base station clock offset trend information of each operator base station in a second time period, the second time period being a time period occurring after the first time period; and performing a clock information synchronization operation on each operator base station according to the base station clock offset trend information of each operator base station in the second time period. The present disclosure can effectively improve the service quality of the co-construction and sharing network and reduce the decline in service experience caused by the unsynchronized base station clocks.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a method, system and related equipment for synchronizing base station clock information based on artificial intelligence. Background Technology

[0002] Clock synchronization ensures that all nodes in a system are in sync with time, thereby guaranteeing the normal operation of the system and the correct transmission of data. In the field of communications, base station clock synchronization is particularly important.

[0003] However, in scenarios where base stations are co-built and shared by different operators, the clock sources of base stations from different operators (such as cesium atomic clocks and hydrogen atomic clocks) may have large clock frequency deviations between base stations due to hardware differences or calibration errors (such as clock frequency deviations exceeding ±0.05ppb). This can lead to various problems such as signal interference and data transmission errors, which can significantly affect network performance and user experience.

[0004] Existing base station clock synchronization schemes are designed only for single-operator networks, lack the ability to fuse and analyze clock information from multiple operators, and fixed threshold alarm mechanisms (such as alarms triggered when clock frequency deviation > 0.1ppb) cannot adapt to dynamic network environments, easily leading to missed or false alarms.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This disclosure provides a base station clock information synchronization method, system, and related equipment based on artificial intelligence, which at least to some extent overcomes the technical problem that the base station clock synchronization schemes provided in related technologies are difficult to apply to base station clock synchronization between base stations of different operators.

[0007] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0008] According to one aspect of this disclosure, an artificial intelligence-based base station clock information synchronization method is provided. The method includes: collecting clock status data and / or network performance data of each operator's base station during a first time period; fusing the clock status data and / or network performance data of each operator's base station during the first time period to construct a cross-operator base station clock synchronization feature matrix; inputting the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model to output base station clock offset trend information of each operator's base station during a second time period, wherein the second time period is a time period occurring after the first time period; and performing clock information synchronization operations on each operator's base station based on the base station clock offset trend information of each operator's base station during the second time period.

[0009] In some embodiments, clock information synchronization is performed on each operator base station based on the base station clock offset trend information of each operator base station in the second time period, including: outputting alarm information based on the base station clock offset trend information of each operator base station in the second time period, wherein the alarm information includes: alarm information of multiple alarm levels, each alarm level corresponding to a different clock information synchronization strategy; selecting the corresponding clock information synchronization strategy based on the alarm information, and performing clock information synchronization operation on each operator base station.

[0010] In some embodiments, before outputting alarm information based on the base station clock offset trend information of each operator's base station in the second time period, the method further includes: performing cooperative anomaly detection on the cross-operator base station clock synchronization feature matrix to obtain a cooperative anomaly detection result indicating whether each operator's base station has a cooperative anomaly; wherein, outputting alarm information based on the base station clock offset trend information of each operator's base station in the second time period includes: outputting alarm information based on the base station clock offset trend information of each operator's base station in the second time period and the cooperative anomaly detection result.

[0011] In some embodiments, the isolated forest algorithm is used to perform collaborative anomaly detection on the cross-operator base station clock synchronization feature matrix to obtain collaborative anomaly detection results for whether each operator's base station has collaborative anomalies.

[0012] In some embodiments, the clock skew prediction model is obtained by training a long short-term memory network model.

[0013] According to another aspect of this disclosure, a network management device is also provided, comprising: a data acquisition module for acquiring clock status data and / or network performance data of each operator's base station during a first time period; and a data processing module for fusing the clock status data and / or network performance data of each operator's base station during the first time period to construct a cross-operator base station clock synchronization feature matrix, inputting the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model, outputting base station clock offset trend information of each operator's base station during a second time period, and then performing clock information synchronization operation on each operator's base station based on the base station clock offset trend information of each operator's base station during the second time period, wherein the second time period is a time period occurring after the first time period.

[0014] According to another aspect of this disclosure, a communication system is also provided, comprising: multiple operator base stations, a clock status acquisition agent module, and a network management device; wherein, the clock status acquisition agent module is used to acquire clock status data and / or network performance data of each operator base station within a first time period; the network management device communicates with the clock status acquisition agent module and is used to fuse the clock status data and / or network performance data of each operator base station acquired by the clock status acquisition agent module within the first time period to construct a cross-operator base station clock synchronization feature matrix, and input the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model, outputting base station clock offset trend information of each operator base station in a second time period, and then performing clock information synchronization operation on each operator base station according to the base station clock offset trend information of each operator base station in the second time period, wherein the second time period is a time period occurring after the first time period.

[0015] According to another aspect of this disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the AI-based base station clock information synchronization method described above by executing the executable instructions.

[0016] According to another aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the artificial intelligence-based base station clock information synchronization method described in any one of the preceding claims.

[0017] According to another aspect of this disclosure, a computer program product is also provided, comprising: a computer program or instructions that, when executed by a processor, implement the artificial intelligence-based base station clock information synchronization method described in any one of the preceding claims.

[0018] The AI-based base station clock information synchronization method, system, and related equipment provided in the embodiments of this disclosure collect clock status data and / or network performance data of base stations from different operators, and use a pre-trained clock offset prediction model to predict the clock offset trend information of base stations from different operators. By performing clock information synchronization operations on base stations from different operators in advance, the service quality of the co-built and shared network can be effectively improved, and the service experience degradation caused by base station clock asynchrony can be reduced.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0021] Figure 1 This diagram illustrates a communication system architecture according to an embodiment of the present disclosure.

[0022] Figure 2 A flowchart of a base station clock information synchronization method based on artificial intelligence is shown in an embodiment of this disclosure;

[0023] Figure 3 A flowchart of an optional artificial intelligence-based base station clock information synchronization method is shown in an embodiment of this disclosure;

[0024] Figure 4 A flowchart of an optional artificial intelligence-based base station clock information synchronization method is shown in an embodiment of this disclosure;

[0025] Figure 5 This diagram illustrates a specific implementation system architecture of an artificial intelligence-based base station clock information synchronization method according to an embodiment of this disclosure.

[0026] Figure 6 The flowchart illustrates a specific implementation of an artificial intelligence-based base station clock information synchronization method in an embodiment of this disclosure.

[0027] Figure 7 This diagram illustrates a network management device according to an embodiment of the present disclosure;

[0028] Figure 8 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0029] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0030] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] To facilitate understanding, before introducing the embodiments of this disclosure, the following explanations are provided for several terms involved in the embodiments of this disclosure:

[0032] Base station co-construction and sharing: Different operators collaborate in the construction of mobile communication networks, jointly investing in, building, and operating base stations (such as shared base stations in 4G / 5G networks) to optimize resource allocation and reduce costs. In 4G / 5G network construction, some operators jointly build 4G / 5G base stations in certain areas, reducing redundant construction and improving resource utilization by sharing base station resources.

[0033] Clock synchronization involves coordinating clock information among multiple devices or systems to ensure that their times are consistent or within acceptable error ranges. This synchronization is crucial for systems requiring precise timestamps or coordinated operations, such as communication networks, distributed computing systems, industrial control systems, and financial trading systems. In the communications field, clock synchronization is particularly important. For example, in 4G / 5G networks, base stations require precise clock synchronization to ensure the accuracy and stability of signal transmission. If base stations are not clock-synchronized, it can lead to signal interference, data transmission errors, and other problems, thus affecting network performance and user experience.

[0034] Clock State Agent (CSA): A software or hardware component used to monitor and collect clock state data from base stations. Typically deployed in communication networks, it is responsible for collecting various clock state data from base stations in real time (such as clock source, synchronization status, frequency offset, time deviation, etc.) and reporting this data to the network management system or clock synchronization server for clock synchronization status analysis, fault diagnosis, and network optimization.

[0035] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0036] Figure 1 A schematic diagram of an exemplary communication system architecture is shown, in which the AI-based base station clock information synchronization method described in this disclosure can be applied. Figure 1 As shown, the system architecture includes: multiple operator base stations 10, clock status acquisition agent module 20, and network management equipment 30.

[0037] Among them, the clock status acquisition agent module 20 is used to collect clock status data and / or network performance data of each operator's base station in the first time period.

[0038] The network management device 30 communicates with the clock status acquisition agent module 20 to fuse the clock status data and / or network performance data of each operator's base station 10 collected by the clock status acquisition agent module in the first time period, construct a cross-operator base station clock synchronization feature matrix, and input the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model to output the base station clock offset trend information of each operator's base station in the second time period. Then, based on the base station clock offset trend information of each operator's base station in the second time period, clock information synchronization operation is performed on each operator's base station. The second time period is the time period that occurs after the first time period.

[0039] In practical implementation, one or more clock status acquisition agent modules 20 can be deployed; each clock status acquisition agent module 20 can be used to collect clock status data and / or network performance data of the same or different operator base stations. Figure 1 The diagram illustrates a scenario where operators X and Y share a base station network. Due to hardware differences or calibration errors, the clock sources 40 of the base stations from different operators (such as atomic clock group A of operator X, atomic clock group B of operator X, and atomic clock group B of operator Y) may exhibit clock frequency deviations. In this embodiment, these base station clock sources may be cesium atomic clocks, hydrogen atomic clocks, etc.

[0040] In some embodiments, the clock skew prediction model may be obtained by training a long short-term memory network model.

[0041] Optionally, in some embodiments, the network management device 30 is further configured to: output alarm information based on the base station clock offset trend information of each operator base station 10 in the second time period, wherein the alarm information includes: alarm information of multiple alarm levels, each alarm level corresponding to a different clock information synchronization strategy; select the corresponding clock information synchronization strategy according to the alarm information, and perform clock information synchronization operation on each operator base station 10.

[0042] Furthermore, in some embodiments, the network management device 30 is also used to: perform collaborative anomaly detection on the cross-operator base station clock synchronization feature matrix to obtain collaborative anomaly detection results for whether each operator's base station has collaborative anomalies; and output alarm information based on the base station clock offset trend information of each operator's base station in the second time period and the collaborative anomaly detection results.

[0043] For example, in some embodiments, the network management device 30 may use the isolated forest algorithm to perform collaborative anomaly detection on the clock synchronization feature matrix of cross-operator base stations, and obtain the collaborative anomaly detection result of whether there is a collaborative anomaly in each operator's base station.

[0044] In this embodiment of the disclosure, the terminal may also be referred to as UE (User Equipment). In specific implementations, the terminal may be a mobile phone, tablet computer, laptop computer, personal digital assistant (PDA), mobile internet device (MID), wearable device, or in-vehicle device, etc. It should be noted that this embodiment of the disclosure does not limit the specific type of terminal device. As an example, Figure 1 The end users shown are mobile phone users.

[0045] Optionally, Figure 1 The base station shown can also be an access network device such as a relay or access point. The base station in this embodiment can be a base station of any network standard, such as a 3G base station, a 4G base station, a 5G base station, a 6G base station, or a later version base station. It should be noted that this embodiment does not limit the specific type of base station.

[0046] Those skilled in the art will know that Figure 1 The number of base stations, clock status acquisition agent modules, and network management devices shown in this embodiment is merely illustrative. Any number of base stations, clock status acquisition agent modules, and network management devices can be used as needed. This disclosure does not limit the number of such components.

[0047] Under the above system architecture, this disclosure provides a base station clock information synchronization method based on artificial intelligence, which can be executed by any electronic device with computing power.

[0048] In some embodiments, the AI-based base station clock information synchronization method provided in this disclosure can be executed by the network management device of the above-described system architecture; in other embodiments, the AI-based base station clock information synchronization method provided in this disclosure can be implemented by the network management device, clock status acquisition agent module, and base station in the above-described system architecture through interaction.

[0049] Figure 2 This diagram illustrates a flowchart of an artificial intelligence-based base station clock information synchronization method according to an embodiment of the present disclosure, as follows: Figure 2 As shown, the base station clock information synchronization method based on artificial intelligence provided in this embodiment includes the following steps:

[0050] S202, collect clock status data and / or network performance data of each operator's base station during the first time period.

[0051] In this embodiment, the various operator base stations can belong to the same operator, different operators, or a combination of both. Clearly, the AI-based base station clock information synchronization method provided in this embodiment can be applied to the clock information synchronization of any network device. This network device can be an access network device or a core network device. When the network device is an access network device, it can be a base station or other access network devices.

[0052] It should be noted that the aforementioned first time period can be a time period occurring before the current time, or it can be a time period starting from the current time. In this embodiment of the disclosure, the clock status data can be any type of data used to describe or reflect the operating status, synchronization accuracy, stability, and abnormal conditions of the base station clock itself (such as clock source type, clock synchronization deviation, clock frequency deviation, etc.); the network performance data can be any type of data used to describe or reflect the impact of base station clock synchronization on the quality of service of the communication network or the quality of service of the business (such as data transmission rate, data transmission latency, etc.).

[0053] S204, fuses the clock status data and / or network performance data of each operator's base station in the first time period to construct a cross-operator base station clock synchronization feature matrix.

[0054] After collecting clock status data and / or network performance data of each operator's base station in the first time period, the collected clock status data and / or network performance data can be fused to obtain a cross-operator base station clock synchronization matrix. This cross-operator base station clock synchronization matrix can reflect the clock status data and / or network performance data of each operator's base station.

[0055] S206, input the cross-operator base station clock synchronization feature matrix into the pre-trained clock offset prediction model, and output the base station clock offset trend information of each operator's base station in the second time period, where the second time period is the time period that occurs after the first time period.

[0056] It should be noted that the second time period mentioned above is a period that occurs after the first time period. By inputting the cross-operator base station clock synchronization matrix of each operator's base station in the first time period into a pre-trained clock offset prediction model, the base station clock offset trend information of each operator's base station in the second time period can be predicted.

[0057] Optionally, in some embodiments, the clock skew prediction model is obtained by training a long short-term memory network model.

[0058] S208, based on the clock offset trend information of each operator's base station in the second time period, performs clock information synchronization operation on each operator's base station.

[0059] By predicting the clock offset trend of each operator's base station in the second time period, clock information synchronization can be performed on operator base stations with large clock offsets.

[0060] Alternatively, in some embodiments, such as Figure 3 As shown in the embodiments of this disclosure, the AI-based base station clock information synchronization method can perform clock information synchronization operations on various operator base stations through the following steps:

[0061] S302 outputs alarm information based on the base station clock offset trend information of each operator's base station in the second time period. The alarm information includes multiple alarm levels, each alarm level corresponding to a different clock information synchronization strategy.

[0062] S304 selects the appropriate clock information synchronization strategy based on the alarm information and performs clock information synchronization operation on each operator's base station.

[0063] In the above embodiments, the alarm level is determined based on the base station clock offset trend information of each operator's base station in the second time period, the corresponding alarm level is output, and a clock synchronization strategy for the corresponding alarm level is selected to perform clock information synchronization operations on each operator's base station. It should be noted that the specific type of alarm level is not limited in this embodiment, and those skilled in the art can set different alarm levels according to actual conditions. For example, one alarm level is that the predicted clock deviation causes the handover failure rate to exceed a preset threshold (such as 5%); another alarm level is that the predicted clock deviation may affect carrier aggregation.

[0064] Through the above embodiments, alarm information of different alarm levels can be triggered for different alarm levels, and clock information synchronization strategies of different alarm levels can be used to synchronize clock information.

[0065] Furthermore, in some embodiments, the AI-based base station clock information synchronization method provided in this disclosure can also output alarm information through the following steps:

[0066] S402, perform collaborative anomaly detection on the clock synchronization feature matrix of cross-operator base stations, and obtain the collaborative anomaly detection results of whether there is collaborative anomaly in each operator's base station;

[0067] S404 outputs alarm information based on the base station clock offset trend information and collaborative anomaly detection results of each operator's base station in the second time period.

[0068] It should be noted that, in addition to the failure of a single base station, there may also be failures in the coordination between base stations. For example, the clock deviations of base station A and base station B are within the allowable deviation range, but the coordinated operation of base station A and base station B may cause network or service anomalies. Therefore, in this embodiment of the disclosure, coordination anomaly detection is performed by cross-operator base station clock synchronization feature matrix to obtain the coordination anomaly detection results of whether each operator base station has coordination anomalies, and then, based on the coordination anomaly detection results, it is determined whether to trigger the corresponding alarm information.

[0069] Optionally, in some embodiments, the Isolation Forest algorithm can be used to perform collaborative anomaly detection on the clock synchronization feature matrix of cross-operator base stations, obtaining collaborative anomaly detection results for whether collaborative anomalies exist in each operator's base stations. The Isolation Forest algorithm is an efficient anomaly detection method, especially suitable for processing high-dimensional data and large-scale datasets.

[0070] Figure 5 This diagram illustrates a specific system architecture for a base station clock information synchronization method based on artificial intelligence, as shown in the embodiments of this disclosure. Figure 5 As shown, it specifically includes:

[0071] 1) Data Acquisition Layer: Clock status acquisition agent modules deployed at each operator's base station collect the following data in real time: ① Atomic clock frequency deviation value of this base station (compared with satellite time synchronization or master clock source); ② Time synchronization error between cross-operator base stations (measured via air interface or X2 / S1 interface signaling); ③ Service quality indicators (such as user call MOS value, handover success rate, carrier aggregation failure rate).

[0072] 2) Data Fusion Layer: Cleans, aligns, and correlates multi-source data (based on timestamps and base station geographical locations), and performs data fusion through cross-carrier clock synchronization data centers.

[0073] 3) AI analysis layer: including clock skew prediction model and anomaly detection model.

[0074] Among them, the clock offset prediction model is based on the LSTM neural network. It takes historical frequency deviation, temperature, humidity and other environmental parameters as input to predict the clock offset trend in the future (such as 1 hour); the anomaly detection model adopts the Isolation Forest algorithm to identify the collaborative anomaly patterns of clock data from multiple operators.

[0075] 4) Alarm and optimization layer: ① Dynamically generate alarm levels (e.g., Level 1 alarm: prediction deviation will lead to a handover failure rate >5%; Level 2 alarm: deviation may affect carrier aggregation); ② Recommend optimization strategies: switch master clock source, adjust synchronization compensation parameters or trigger satellite timing calibration.

[0076] Figure 6 This diagram illustrates a specific implementation flowchart of an artificial intelligence-based base station clock information synchronization method according to an embodiment of the present disclosure, as follows: Figure 6 As shown, it specifically includes:

[0077] S602, multi-dimensional data acquisition: The CSA agent collects the atomic clock frequency deviation every 5 seconds, compares it with the clocks of adjacent operator base stations, and reports it to the fusion layer.

[0078] S604, data association and feature extraction, constructing a cross-operator clock synchronization feature matrix, including: clock deviation timing change rate, adjacent base station deviation correlation and the mapping relationship between service quality indicators and deviation.

[0079] S606, AI Model Inference and Alarm: The prediction model outputs the probability of future clock offset and the weight of its impact on services. For example, if the predicted frequency deviation exceeds 0.08ppb within 30 minutes and the handover failure rate of the associated base station rises to 3%, a Level 1 alarm is triggered. The anomaly detection model identifies atypical deviation patterns (such as multiple base stations simultaneously experiencing small offsets) and alerts potential systemic faults.

[0080] S608, dynamic policy execution: When a level 1 alarm is triggered, it automatically sends a command to the operation and maintenance system to switch to the backup satellite timing source and start the compensation algorithm; when a level 2 alarm is triggered, manual intervention is possible to calibrate or adjust the synchronization link weight.

[0081] Below, we will take the scenario of co-constructing base stations in densely populated urban areas as an example to illustrate a specific case.

[0082] Assume that operator X's base station A and operator Y's base station B are co-built and shared, geographically adjacent, with a handover boundary, and using cesium atomic clocks and hydrogen atomic clocks respectively. The CSA agent detects a continuous increase in the frequency deviation of base station A (0.06ppb→0.09ppb), while the handover failure rate of the associated base station B rises from 1% to 4%. The AI ​​model predicts that the deviation will reach 0.12ppb after 30 minutes, triggering a level 1 alarm, automatically switching to BeiDou time synchronization and compensating for the deviation, restoring the handover failure rate to 1.5%.

[0083] As can be seen from the above, the AI-based base station clock information synchronization scheme provided in this embodiment collects base station clock deviations (such as atomic clock frequency deviations and cross-network synchronization errors) and network operation index data (such as service quality indicators of base stations of each operator) from multiple operators. Through AI analysis (using AI models to predict base station clock offset trend information), it generates dynamic alarm levels by associating the impact on service quality. This enables the prediction and early warning of clock deviations of base stations of different operators in a co-construction and sharing scenario.

[0084] This solution can be applied, but is not limited to, early warning and prediction of clock asynchrony in shared operator base stations, and has a good market application scale. In multi-operator mobile communication network co-construction and sharing scenarios, the mobile communication base stations of the co-constructing and sharing operators are geographically adjacent and interspersed. Mobile communication users travel between base stations of different operators, and there are needs for base station handover and carrier aggregation in various service uses. Using the patented technology solution of this invention, the intelligent operation capability of the co-constructed and shared network can be effectively improved, and the user experience degradation caused by base station clock asynchrony can be reduced.

[0085] Analysis revealed that in the scenario of co-construction and sharing of 4G / 5G base stations by operators, the clock sources of base stations from different operators (such as cesium atomic clocks and hydrogen atomic clocks) may have clock frequency deviations (such as exceeding ±0.05ppb) due to hardware differences or calibration errors, causing the following problems: ① Damage to user services: jitter in cross-operator user calls, failure of carrier aggregation, and frequent dropped calls during base station handover; ② Low coordination efficiency: existing synchronization monitoring relies on manual inspection or fixed threshold alarms, which cannot dynamically identify early slight clock offsets, resulting in delayed handling of problems.

[0086] Traditional clock synchronization solutions (such as GPS / BeiDou time synchronization and 1588v2 protocol) have the following limitations in co-construction and sharing scenarios: ① If the master and slave clock sources come from different operators, the slave clock synchronization link is prone to failure when the master clock is abnormal; ② The operator clock status data has not been jointly analyzed, making it difficult to locate cross-network synchronization problems.

[0087] It is evident that current base station clock synchronization monitoring systems are designed only for single-operator networks and lack the capability for multi-operator clock data fusion and analysis. Furthermore, the fixed threshold alarm mechanism (e.g., triggering an alarm when the frequency deviation > 0.1 ppb) is unsuitable for dynamic network environments and is prone to missed or false alarms. An AI-based base station clock information synchronization method can provide an AI-based multi-operator base station co-construction and sharing clock synchronization monitoring and alarm system. By collecting multi-source clock and network performance data in real time, it uses machine learning models to predict clock offset trends, triggers dynamic alarms in advance, and recommends optimization strategies, ensuring the collaborative service quality of co-construction and sharing base stations.

[0088] It should be noted that the acquisition, storage, use, and processing of data in this disclosed technical solution comply with the relevant provisions of national laws and regulations. The various types of data, such as personal identity data, operational data, and behavioral data related to individuals, customers, and groups, obtained in the embodiments of this disclosure have all been authorized.

[0089] Based on the same inventive concept, this disclosure also provides a network management device, as described in the following embodiments. Since the principle by which this network management device solves the problem is similar to that of the above-described method embodiments, the implementation of this network management device embodiment can refer to the implementation of the above-described method embodiments, and repeated details will not be elaborated further.

[0090] Figure 7 This diagram illustrates a network management device according to an embodiment of the present disclosure, such as... Figure 7 As shown, the device includes a data acquisition module 701 and a data processing module 702.

[0091] The data acquisition module 701 is used to collect clock status data and / or network performance data of each operator's base station during the first time period; the data processing module 702 is used to fuse the clock status data and / or network performance data of each operator's base station during the first time period, construct a cross-operator base station clock synchronization feature matrix, input the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model, output the base station clock offset trend information of each operator's base station during the second time period, and then perform clock information synchronization operation on each operator's base station based on the base station clock offset trend information of each operator's base station during the second time period. The second time period is the period that occurs after the first time period.

[0092] In some embodiments, the data processing module 702 is further configured to: output alarm information based on the base station clock offset trend information of each operator base station in the second time period, wherein the alarm information includes: alarm information of multiple alarm levels, each alarm level corresponding to a different clock information synchronization strategy; select the corresponding clock information synchronization strategy based on the alarm information, and perform clock information synchronization operation on each operator base station.

[0093] Furthermore, in some embodiments, the data processing module 702 is also used to: perform cooperative anomaly detection on the cross-operator base station clock synchronization feature matrix to obtain cooperative anomaly detection results for whether each operator base station has cooperative anomalies; wherein, based on the base station clock offset trend information of each operator base station in the second time period, alarm information is output, including: based on the base station clock offset trend information of each operator base station in the second time period and the cooperative anomaly detection results, alarm information is output.

[0094] In some embodiments, the data processing module 702 is further configured to use the isolated forest algorithm to perform collaborative anomaly detection on the clock synchronization feature matrix of cross-operator base stations, and obtain collaborative anomaly detection results for whether each operator base station has collaborative anomalies.

[0095] In some embodiments, the clock skew prediction model described above is obtained by training a long short-term memory network model.

[0096] It should be noted that the examples and application scenarios implemented by the modules in the above device embodiments and the corresponding steps in the method embodiments are the same, but are not limited to the content disclosed in the above method embodiments. It should also be noted that the above modules, as part of the device, can be executed in a computer system such as a set of computer-executable instructions.

[0097] Those skilled in the art will understand that various aspects of this disclosure can be implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to herein as a "circuit", "module" or "system".

[0098] Based on the same inventive concept, this disclosure also provides an electronic device, which includes: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any of the above-described artificial intelligence-based base station clock information synchronization methods by executing the executable instructions. Since the principle by which this electronic device solves the problem is similar to that of the above-described method embodiments, the implementation of this electronic device embodiment can refer to the implementation of the above-described method embodiments, and repeated details will not be elaborated further.

[0099] The following reference Figure 8 To describe an electronic device 800 according to such an embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0100] like Figure 8As shown, the electronic device 800 is manifested in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, and a bus 830 connecting different system components (including storage unit 820 and processing unit 810).

[0101] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 810 can perform the following steps of the above method embodiments: collecting clock status data and / or network performance data of each operator's base station in a first time period; fusing the clock status data and / or network performance data of each operator's base station in the first time period to construct a cross-operator base station clock synchronization feature matrix; inputting the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model to output base station clock offset trend information of each operator's base station in a second time period, wherein the second time period is the time period occurring after the first time period; and performing clock information synchronization operation on each operator's base station according to the base station clock offset trend information of each operator's base station in the second time period.

[0102] Storage unit 820 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include a read-only memory (ROM) 8203.

[0103] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0104] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0105] Electronic device 800 can also communicate with one or more external devices 840 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 800, and / or with any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0106] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0107] Based on the same inventive concept, this disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the above-described artificial intelligence-based base station clock information synchronization methods. Since the principle by which this computer-readable storage medium embodiment solves the problem is similar to that of the above-described method embodiments, the implementation of this computer-readable storage medium embodiment can refer to the implementation of the above-described method embodiments, and repeated details will not be elaborated further.

[0108] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0109] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0110] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0111] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0112] Based on the same inventive concept, this disclosure also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements any one of the artificial intelligence-based base station clock information synchronization methods described in the above method embodiments. Since the principle by which this computer program product embodiment solves the problem is similar to that of the above method embodiments, the implementation of this computer program product embodiment can refer to the implementation of the above method embodiments, and repeated details will not be elaborated further.

[0113] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0114] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0115] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0116] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. An artificial intelligence-based base station clock information synchronization method, characterized by, The method comprises the following steps: collecting clock state data and / or network performance data of each operator base station in a first time period; fusing the clock state data and / or network performance data of each operator base station in the first time period to construct a cross-operator base station clock synchronization feature matrix; inputting the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model to output base station clock offset trend information of each operator base station in a second time period, wherein the second time period is a time period occurring after the first time period; performing clock information synchronization operation on each operator base station according to the base station clock offset trend information of each operator base station in the second time period; wherein the cross-operator base station clock synchronization feature matrix comprises clock deviation time series change rate, adjacent base station deviation correlation and mapping relationship between service quality index and deviation; wherein the method further comprises: performing collaborative anomaly detection on the cross-operator base station clock synchronization feature matrix to obtain collaborative anomaly detection results of whether each operator base station has collaborative anomaly; and outputting alarm information according to the base station clock offset trend information of each operator base station in the second time period and the collaborative anomaly detection results. 2.The AI-based base station clock information synchronization method of claim 1, wherein, performing clock information synchronization operation on each operator base station according to the base station clock offset trend information of each operator base station in the second time period, comprising: outputting alarm information according to the base station clock offset trend information of each operator base station in the second time period, wherein the alarm information comprises alarm information of multiple alarm levels, and each alarm level corresponds to a different clock information synchronization strategy; selecting a corresponding clock information synchronization strategy according to the alarm information to perform clock information synchronization operation on each operator base station. 3.The AI-based base station clock information synchronization method of claim 1, wherein, The collaborative anomaly detection result of whether each operator base station has collaborative anomaly is obtained by performing collaborative anomaly detection on the cross-operator base station clock synchronization feature matrix using an isolation forest algorithm.

4. The method of claim 1 to 3, wherein, The clock offset prediction model is obtained by training a long short-term memory network model.

5. A network management device, characterized by comprising: The method comprises the following steps: a data collection module for collecting clock state data and / or network performance data of each operator base station in a first time period; a data processing module for fusing the clock state data and / or network performance data of each operator base station in the first time period to construct a cross-operator base station clock synchronization feature matrix, and inputting the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model to output base station clock offset trend information of each operator base station in a second time period, and then performing clock information synchronization operation on each operator base station according to the base station clock offset trend information of each operator base station in the second time period, wherein the second time period is a time period occurring after the first time period; wherein the cross-operator base station clock synchronization feature matrix comprises clock deviation time series change rate, adjacent base station deviation correlation and mapping relationship between service quality index and deviation; The data processing module is further configured to perform collaborative anomaly detection on the cross-operator base station clock synchronization feature matrix to obtain collaborative anomaly detection results of whether each operator base station has a collaborative anomaly, and output alarm information according to the base station clock offset trend information of each operator base station in the second time period and the collaborative anomaly detection results.

6. A communication system characterized by The method comprises: a plurality of operator base stations, a clock state collection agent module, and a network management device; The clock state collection agent module is configured to collect clock state data and / or network performance data of each operator base station in a first time period. The network management device is in communication with the clock state collection agent module and is configured to fuse the clock state data and / or network performance data of each operator base station in the first time period collected by the clock state collection agent module, construct a cross-operator base station clock synchronization feature matrix, input the cross-operator base station clock synchronization feature matrix into a pre-trained clock offset prediction model, and output base station clock offset trend information of each operator base station in a second time period, and then perform a clock information synchronization operation on each operator base station according to the base station clock offset trend information of each operator base station in the second time period, the second time period being a time period occurring after the first time period. The cross-operator base station clock synchronization feature matrix comprises a clock deviation time series change rate, a neighboring base station deviation correlation, and a mapping relationship between a quality of service indicator and a deviation. The network management device is further configured to perform collaborative anomaly detection on the cross-operator base station clock synchronization feature matrix to obtain collaborative anomaly detection results of whether each operator base station has a collaborative anomaly, and output alarm information according to the base station clock offset trend information of each operator base station in the second time period and the collaborative anomaly detection results.

7. An electronic device, comprising: The method comprises: a processor; and a memory for storing executable instructions of the processor; The processor is configured to execute the executable instructions to perform the method for synchronizing base station clock information based on artificial intelligence according to any one of claims 1 to 4.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for synchronizing base station clock information based on artificial intelligence according to any one of claims 1 to 4.

9. A computer program product, comprising: The computer program or instructions are executed by the processor to implement the method for synchronizing base station clock information based on artificial intelligence according to any one of claims 1 to 4.

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