Early warning method and device for wireless group fault, electronic equipment and storage medium

Abstract

The invention discloses a wireless group obstacle early warning method and device, electronic equipment and a storage medium. The method comprises the following steps: when it is monitored that a communication fault exists in a preset area, network element attribute data of a plurality of target wireless network elements in the preset area are obtained, and the plurality of target wireless network elements are used for representing wireless network elements which have failed between a preset historical moment and a current moment in the preset area; determining a target similarity degree of the plurality of target wireless network elements; performing wireless group obstacle prediction on the preset area based on the target similarity degree to obtain a prediction result, the prediction result being used for representing whether the wireless group obstacle will occur in the preset area at a preset future moment; and when the prediction result is that the wireless group fault will occur in the preset area at the preset future moment, generating wireless group fault early warning information based on the network element attribute data. According to the method and the device, the technical problems of difficulty in early warning of the wireless group fault and low efficiency of judging the wireless group fault in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of communications, and in particular to a method, device, electronic equipment and storage medium for early warning of wireless group failure. Background Art

[0002] A wireless group failure may refer to a failure in which a large number of wireless network elements in a contiguous geographical area are out of service at the same time or successively in a short period of time, i.e., a failure that makes it impossible to provide wireless services. Since a wireless group failure involves a large number of wireless network elements, and these wireless network elements are in adjacent contiguous geographical areas, when these wireless network elements are out of service at the same time, users in the area cannot rely on wireless network elements in the current location or in adjacent locations to provide wireless network services, which has a significant impact on the wireless communications of wireless users in this geographical area.

[0003] At present, in the related technologies, it is usually only after wireless group failures have occurred or have developed to a large extent that alarm information can be discovered and generated, and the timeliness is low, or it is necessary to rely on human judgment to determine whether wireless network elements in a certain area have successively generated out-of-service alarms. The subjective judgment process is more dependent on human experience, which makes it difficult to provide early warning of wireless group failures in the related technologies, and the efficiency of wireless group failure judgment is low.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic device and storage medium for early warning of wireless group failures, so as to at least solve the technical problems in the related art that it is difficult to give early warning of wireless group failures and the efficiency of wireless group failure judgment is low.

[0006] According to one aspect of an embodiment of the present application, a method for early warning of a wireless group failure is provided, comprising: in the case where a communication failure is detected in a preset area, obtaining network element attribute data of multiple target wireless network elements in the preset area, wherein the multiple target wireless network elements are used to represent wireless network elements in the preset area that have failed between a preset historical moment and a current moment; determining a target proximity of the multiple target wireless network elements, wherein the target proximity is used to characterize a spatial aggregation degree between the multiple target wireless network elements; predicting a wireless group failure in the preset area based on the target proximity to obtain a prediction result, wherein the prediction result is used to indicate whether a wireless group failure will occur in the preset area at a preset future moment, the preset future moment being after the current moment; in the case where the prediction result is that a wireless group failure will occur in the preset area at a preset future moment, generating wireless group failure early warning information based on the network element attribute data, wherein the wireless group failure early warning information includes: a result of locating the cause of the failure of the multiple target wireless network elements.

[0007] According to another aspect of an embodiment of the present invention, a wireless group failure warning device is also provided, including: an acquisition module, used to acquire network element attribute data of multiple target wireless network elements in a preset area when a communication failure is detected in the preset area, wherein the multiple target wireless network elements are used to represent wireless network elements in the preset area that have failed between a preset historical moment and a current moment; a determination module, used to determine the target proximity of the multiple target wireless network elements, wherein the target proximity is used to characterize the spatial aggregation degree between the multiple target wireless network elements; a prediction module, used to predict wireless group failures in the preset area based on the target proximity to obtain a prediction result, wherein the prediction result is used to indicate whether a wireless group failure will occur in the preset area at a preset future moment, and the preset future moment is after the current moment; a generation module, used to generate wireless group failure warning information based on the network element attribute data when the prediction result is that a wireless group failure will occur in the preset area at a preset future moment, wherein the wireless group failure warning information includes: the fault cause location result of the faults of the multiple target wireless network elements.

[0008] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is running.

[0009] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0010] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0011] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0012] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0013] In an embodiment of the present invention, in response to monitoring the presence of a communication failure in a preset area, first, network element attribute data of multiple target wireless network elements that have already failed in the preset area are obtained, and then, the target proximity of the multiple target wireless network elements in the preset area can be determined, and the target proximity is used to characterize the spatial aggregation degree of the multiple target wireless network elements. Then, based on the target proximity, wireless group failure prediction is performed on the multiple target wireless network elements to obtain a prediction result, and the prediction result is used to indicate whether a wireless group failure will occur in the preset area at a preset future time; finally, when the prediction result is that a wireless group failure will occur in the preset area at a preset future time, wireless group failure warning information is generated, and fault analysis is performed on the multiple target wireless network elements to obtain the fault cause location results of the multiple target wireless network elements. It is easy to notice that, in response to monitoring the existence of a communication failure in a preset area, the present application obtains network element attribute data of multiple target wireless network elements that have already failed in the preset area, and determines whether a wireless group failure will occur in the preset area at a preset future moment by analyzing the spatial aggregation degree of multiple target wireless network elements that have already failed in the preset area between a preset historical moment and a current moment. In the case that a wireless group failure will occur in the preset area at a preset future moment, wireless group failure warning information and fault cause location results can be analyzed and obtained. Based on the analysis of the spatial aggregation degree, the correlation between wireless network elements can be judged more accurately, thereby better predicting the occurrence trend of wireless group failures, the trend of wireless group failures can be discovered earlier, and wireless group failure warning information can be generated in time. Corresponding measures can be taken before the failure occurs to avoid further expansion of the failure. Combined with fault analysis, the cause of the fault can be quickly located, the efficiency of fault handling can be improved, and the stability and reliability of the network can be effectively improved, thereby solving the technical problems in related technologies that it is difficult to provide early warning of wireless group failures and the low efficiency of wireless group failure judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0015] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a method for early warning of wireless group failure according to an embodiment of the present application;

[0016] Figure 2 is a flow chart of a method for early warning of wireless group failure according to an embodiment of the present invention;

[0017] Figure 3 is a schematic diagram of an optional early warning system for wireless group failure according to an embodiment of the present invention;

[0018] Figure 4 is a schematic diagram of an optional elastic periodic polling process according to an embodiment of the present invention;

[0019] Figure 5 is a schematic diagram of a process for determining the degree of proximity of targets in an optional indoor scene type according to an embodiment of the present invention;

[0020] Figure 6 is a schematic diagram of a process for determining target proximity in an optional outdoor scene type according to an embodiment of the present invention;

[0021] Figure 7 is a schematic diagram of a target warning coefficient determination process in an optional indoor scene type according to an embodiment of the present invention;

[0022] Figure 8 is a schematic diagram of a target warning coefficient determination process in an optional outdoor scene type according to an embodiment of the present invention;

[0023] Fig. 9 is a schematic diagram of an optional fault location analysis according to an embodiment of the present invention;

[0024] Fig.10 2 is a schematic diagram of a wireless group fault warning device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] In order to solve the problems existing in the related art, the embodiment of the present application provides a method for predicting wireless group failure, which can be run on Figure 1 In the computer terminal shown, the computer terminal is explained below.

[0028] The wireless group failure prediction method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for predicting wireless group failures. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0029] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0030] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for predicting wireless group failures in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned method for predicting wireless group failures. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0031] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] The display may be, for example, a touch screen liquid crystal display (LCD), which may enable the account to interact with the account interface of the computer terminal 10 .

[0033] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0034] In the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a method for predicting wireless group failures. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] Figure 2 FIG. 1 is a flow chart of a method for early warning of a wireless group failure according to an embodiment of the present invention. Figure 2As shown, the method comprises the following steps:

[0036] Step S202: when a communication failure is detected in a preset area, network element attribute data of a plurality of target wireless network elements in the preset area are obtained.

[0037] The multiple target wireless network elements are used to represent wireless network elements in a preset area that have experienced failures between a preset historical moment and a current moment.

[0038] The above-mentioned wireless network elements may refer to various devices and components in the wireless communication system, which are the basic devices and components that constitute the wireless communication network, and may include but are not limited to base stations, wireless access points, wireless sensors, antenna systems, etc. These devices are the basic components of the wireless communication network and are used to realize the transmission and reception of wireless signals. The functions of the wireless network elements may include signal processing, spectrum management, access control, resource allocation, mobile switching, interference management, etc. Through these functions, the wireless network elements can achieve the requirements of coverage, capacity, quality and reliability of the wireless communication network, ensuring that users maintain smooth communication.

[0039] The above-mentioned preset area can be a pre-set geographical area, which can be a district or county, a community street, a plurality of nearby communities, etc. The preset area can be divided according to actual needs and is not limited here.

[0040] The above-mentioned preset historical moment may refer to a historical moment before the current moment, for example, it may be 10 minutes before the current moment, 30 minutes before the current moment, etc., or it may be a historical moment such as 22:00 yesterday, that is, the preset historical moment to the current moment may be a dynamically changing time period, and the preset historical moment may be determined according to actual needs, and is not limited here.

[0041] The above-mentioned wireless group failure may refer to a large number of wireless network elements in a contiguous geographical area, which may be macro base stations or indoor radio remote units / radio units (Radio Remote Unit, referred to as RRU) that simultaneously or successively produce out-of-service failures in a short period of time. The present application can provide early warning of the impending wireless group failure condition, so as to handle the fault in advance and avoid the expansion of the wireless group failure condition.

[0042] The above-mentioned network element attribute data may refer to various parameters and status information of wireless network elements, including but not limited to device information, fault information and historical data, among which device information may include device model, device serial number, manufacturer, version number, etc.; fault information may include device fault type, fault occurrence time, fault duration, etc.; historical data may include device historical operation data, historical fault records, etc. By obtaining network element attribute data, the cause of faults of multiple target wireless network elements can be analyzed, which facilitates timely troubleshooting and improves the stability and reliability of the communication network.

[0043] In an optional embodiment, in response to monitoring that there is a communication failure in a preset area, which may be one or several communication failures, etc., at this time, it is still impossible to determine whether the preset area will enter a wireless group failure state. First, the network element attribute data of multiple target wireless network elements in the preset area can be obtained. Specifically, the state and attribute data of the wireless network elements can be monitored in real time by a network element monitoring system deployed in the preset area. The network element monitoring system can collect various attribute data of multiple target wireless network elements such as wireless base stations and wireless transmission equipment, which may include but are not limited to power, signal strength, connection status, transmission rate and other information. The operator can also monitor and manage the communication network by deploying a network management system, and can obtain information of each wireless network element in the preset area, including historical data and real-time status. The network element attribute data of multiple target wireless network elements can be easily obtained through the network management system. Sensor technology can also be used to monitor the operating status of wireless network elements in real time by deploying sensor equipment in the preset area. The sensor can collect various data of the wireless network elements and transmit the data to the monitoring center through the communication network for further analysis and processing. Using data mining and analysis technology, the collected network element attribute data of multiple target wireless network elements can be processed and analyzed. By establishing models and algorithms, wireless network elements that have failed can be identified and alarms can be sent to relevant personnel in a timely manner. In actual applications, when obtaining network element attribute data of multiple target wireless network elements in a preset area, artificial intelligence and machine learning technologies can be used to improve the accuracy and efficiency of monitoring and early warning. By establishing monitoring models and prediction algorithms, automatic identification and early warning of communication failures can be achieved, reducing manual intervention and improving response speed. In addition, historical data can be deeply mined in combination with big data technology to analyze the laws and trends of communication failures, providing reference for future prediction and prevention. By analyzing a large amount of data, the information and laws hidden behind the data can be discovered, providing operators with more accurate early warning and fault handling solutions. Through the automated monitoring and early warning system, fault points can be quickly identified and handled, reducing manual intervention and maintenance costs, and improving fault handling efficiency.

[0044] Step S204: determining the target similarity of the multiple target wireless network elements.

[0045] The target proximity degree is used to characterize the spatial aggregation degree between multiple target wireless network elements.

[0046] The above-mentioned target proximity may refer to a parameter used to characterize the spatial clustering characteristics of multiple target wireless network elements. Since the multiple wireless network elements that have failed between the preset historical moment and the current moment may change dynamically, the specific number of wireless network elements and the specific network elements may change, that is, the multiple target wireless network elements may change dynamically, and the corresponding target proximity may also change. Therefore, the spatial clustering characteristics may specifically at least characterize the quantity characteristics and spatial distance characteristics of the multiple target wireless network elements.

[0047] In an optional embodiment, performing a wireless group fault warning can realize the discovery and location of a large number of wireless network elements' out-of-service faults at an early stage, so as to take timely measures to repair them, thereby greatly reducing the impact on user experience and network stability. The warning system can monitor and analyze multiple target wireless network elements in a preset area through a series of technical means, so as to timely discover potential group fault problems. Specifically, the target proximity of multiple target wireless network elements in a preset area can be determined. First, the wireless network elements in the preset area can be monitored and data collected. This can be achieved through a network management system or a special monitoring tool. The monitoring data may include information such as the operating status, signal strength, and load conditions of the wireless network elements. Then, based on the data of multiple wireless network elements that have failed between the preset historical moment and the current moment, the target proximity of these target wireless network elements is calculated. The calculation of the target proximity can be achieved in a variety of ways. The proximity between multiple target wireless network elements can be evaluated by calculating the distance between the wireless network elements. This can be evaluated by indicators such as the physical distance between the wireless network elements, signal strength, and load conditions. By evaluating the proximity between the target wireless network elements, it is possible to find out whether there are certain spatial clustering characteristics, thereby judging whether there is a possibility of wireless group failure. In the above process, by evaluating the proximity between the target wireless network elements, it is possible to more accurately judge whether there is a wireless group failure problem, which helps to reduce the false alarm rate and improve the accuracy of the early warning system. By obtaining the proximity of multiple target wireless network elements in a preset area, it can help the early warning system to more accurately detect wireless group failure problems and take measures to repair them in advance, thereby improving the reliability of the network and the user experience level.

[0048] Step S206, performing wireless group obstacle prediction on the preset area based on the target proximity degree to obtain a prediction result.

[0049] The prediction result is used to indicate whether a wireless group failure will occur in a preset area at a preset future time, and the preset future time is after the current time.

[0050] The above-mentioned preset future moment may refer to a historical moment after the current moment, for example, it may be 1 minute or 30 minutes after the current moment, or it may be a future moment such as 10 am tomorrow. The preset future moment may be determined according to actual needs and is not limited here.

[0051] In an optional embodiment, the early warning of wireless group failure predicts whether a large-scale wireless network failure may occur by analyzing the target proximity of multiple target wireless network elements in a preset area. The design of this early warning system can help operators to discover potential problems in a timely manner, take corresponding measures to avoid or reduce the occurrence of failures, and improve the stability and reliability of the network. When implementing this step, first, the performance data of multiple target wireless network elements in the preset area can be collected, including information such as signal strength, data transmission rate, connection status, etc. Then, the proximity between multiple target wireless network elements is calculated based on these data, and the similarity between them can be evaluated using distance measurement methods such as Euclidean distance or correlation analysis. By analyzing these data, the target proximity can be obtained. Then, wireless group failure prediction can be performed on multiple target wireless network elements based on the target proximity. Machine learning algorithms such as support vector machines, neural networks or decision trees can be used to establish a prediction model, and the possible wireless group failure situations in the future can be predicted by learning and analyzing historical data. The prediction result can be a binary classification, that is, whether a wireless group failure occurs, or a continuous value, that is, the possibility of the occurrence of a wireless group failure. The appropriate prediction method is selected according to the actual situation. Through the above process, we can find signs of wireless group failures in advance and take timely measures to avoid the expansion of the failure and the scope of impact. Secondly, we can reduce the impact of the failure on user experience and service quality, and improve the reliability and stability of the network. By analyzing and predicting a large amount of wireless network metadata, we can help operators better plan network resources and optimize network structure, and improve network performance and efficiency.

[0052] Step S208: When the prediction result shows that a wireless group failure will occur in the preset area at a preset future time, wireless group failure warning information is generated based on the network element attribute data.

[0053] The wireless group fault warning information includes: the fault cause location results of multiple target wireless network elements having faults.

[0054] In an optional embodiment, generating wireless group fault warning information and performing fault analysis on multiple target wireless network elements can achieve timely issuing of warnings and in-depth analysis of wireless network elements that may have faults when the prediction results show that a wireless group fault will occur in a preset area at a preset future time, thereby locating the cause of the fault and taking corresponding maintenance measures to minimize the impact of the fault on network performance. Specifically, first, when generating wireless group fault warning information, the system can determine whether a wireless group fault will occur in the preset area at a preset future time based on the prediction results. If so, the system can automatically generate warning information and send it to relevant operation and maintenance personnel or systems. The warning information can include the fault cause location results, warning levels, warning time, warning objects, and other information of the faults of multiple target wireless network elements, so that the operation and maintenance personnel can take corresponding countermeasures in time. Then, when performing fault analysis on multiple target wireless network elements, the system can first monitor and analyze these wireless network elements. By monitoring the performance indicators, log information, alarm information, etc. of the wireless network elements, the system can have a comprehensive understanding of the working status of the wireless network elements. Then, the system can infer and locate the possible fault causes of the wireless network elements based on the existing fault location algorithm. These fault location algorithms can be combined with historical data of wireless network elements, surrounding environmental factors, equipment status and other information for analysis in order to accurately find the cause of the fault. Finally, the system can take corresponding maintenance measures based on the fault cause location results. These maintenance measures may include remote restart, parameter adjustment, equipment replacement, etc. At the same time, the system can also monitor and feedback the implementation of maintenance measures to ensure that the problem is effectively solved. Through the implementation of the above steps, the system can issue early warning information in time before the early warning group fault occurs, helping operation and maintenance personnel to quickly locate the cause of the fault and take effective measures, thereby minimizing the impact of the fault on network performance. At the same time, the system can also improve the accuracy and efficiency of fault location, reduce operation and maintenance costs, and improve the stability and reliability of the network.

[0055] In an embodiment of the present invention, in response to monitoring the presence of a communication failure in a preset area, first, network element attribute data of multiple target wireless network elements that have already failed in the preset area are obtained, and then, the target proximity of the multiple target wireless network elements in the preset area can be determined, and the target proximity is used to characterize the spatial aggregation degree of the multiple target wireless network elements. Then, based on the target proximity, wireless group failure prediction is performed on the multiple target wireless network elements to obtain a prediction result, and the prediction result is used to indicate whether a wireless group failure will occur in the preset area at a preset future time; finally, when the prediction result is that a wireless group failure will occur in the preset area at a preset future time, wireless group failure warning information is generated, and fault analysis is performed on the multiple target wireless network elements to obtain the fault cause location results of the multiple target wireless network elements. It is easy to notice that, in response to monitoring the existence of a communication failure in a preset area, the present application obtains network element attribute data of multiple target wireless network elements that have already failed in the preset area, and determines whether a wireless group failure will occur in the preset area at a preset future moment by analyzing the spatial aggregation degree of multiple target wireless network elements that have already failed in the preset area between a preset historical moment and a current moment. In the case that a wireless group failure will occur in the preset area at a preset future moment, wireless group failure warning information and fault cause location results can be analyzed and obtained. Based on the analysis of the spatial aggregation degree, the correlation between wireless network elements can be judged more accurately, thereby better predicting the occurrence trend of wireless group failures, the trend of wireless group failures can be discovered earlier, and wireless group failure warning information can be generated in time. Corresponding measures can be taken before the failure occurs to avoid further expansion of the failure. Combined with fault analysis, the cause of the fault can be quickly located, the efficiency of fault handling can be improved, and the stability and reliability of the network can be effectively improved, thereby solving the technical problems in related technologies that it is difficult to provide early warning of wireless group failures and the low efficiency of wireless group failure judgment.

[0056] In the above embodiment of the present application, a wireless group obstacle prediction is performed on a preset area based on the target proximity degree to obtain a prediction result, including: when the target proximity degree is less than a first proximity degree threshold, determining the prediction result is that a wireless group obstacle will not occur in the preset area at a preset future time, and repeating the step of determining the target proximity degrees of multiple target wireless network elements; when the target proximity degree is greater than or equal to the first proximity degree threshold, determining the prediction result is that a wireless group obstacle will occur in the preset area at a preset future time.

[0057] The first proximity threshold mentioned above may refer to a preset threshold used to determine whether a wireless group failure will occur in a preset area at a preset future time. It may be determined according to actual needs and is not limited here.

[0058] In an optional embodiment, during the early warning process of wireless group failure, the target proximity can be monitored to determine in real time whether the target proximity at the current moment is less than the first proximity threshold. When the target proximity is less than the first proximity threshold, the step of determining the target proximity of multiple target wireless network elements can be repeated to achieve continuous monitoring of the target proximity change process. When the target proximity is greater than or equal to the first proximity threshold, it can be determined that the prediction result is that a wireless group failure will occur in the preset area at a preset future moment. When the target proximity is greater than or equal to the first proximity threshold, it can be determined that the prediction result is that the preset area has a tendency to enter a wireless group failure, which can help network operation and maintenance personnel take measures in advance to avoid the occurrence of wireless group failures, thereby improving the stability and reliability of the network. The early determination of the prediction results can help network operation and maintenance personnel respond in time to ensure the normal operation of the network, so as to effectively reduce the impact of wireless group failures on the network and improve the overall performance and service quality of the network.

[0059] In the above embodiment of the present application, the step of determining the target proximity of multiple target wireless network elements is repeatedly executed, including: when the target proximity is less than a second proximity threshold, based on a first polling cycle, the step of determining the target proximity of multiple target wireless network elements is repeatedly executed, wherein the second proximity threshold is less than the first proximity threshold; when the target proximity is greater than or equal to the second proximity threshold, based on a second polling cycle, the step of determining the target proximity of multiple target wireless network elements is repeatedly executed, wherein the second polling cycle is less than the first polling cycle.

[0060] The first polling cycle mentioned above may be a pre-set larger polling cycle, that is, a smaller polling frequency may be achieved, for example, it may be 5 minutes, etc. The first polling cycle may be determined according to actual needs and is not limited here.

[0061] The second polling period mentioned above may be a pre-set smaller polling period, that is, a larger polling frequency may be achieved, for example, it may be 30 seconds, etc. The second polling period may be determined according to actual needs and is not limited here.

[0062] In an optional embodiment, the wireless group fault warning predicts the wireless group that may have a service failure by monitoring the proximity between the target wireless network elements, so as to take timely measures to intervene, thereby reducing the occurrence rate and impact range of the failure. In the prediction process, a proximity threshold can be set to determine whether the distance between the target wireless network elements is close, so as to determine whether to perform the next step of monitoring. Specifically, when the target proximity is less than the second proximity threshold, the system can monitor the proximity between the target wireless network elements through the first polling cycle. At this time, the target proximity is low and relatively safe. A larger polling cycle can be used for polling to take into account the monitoring of the change in the target proximity while saving polling resources. That is, when the target proximity is less than the set first proximity threshold, it is preliminarily judged that the distance between these wireless network elements is far and will not enter the wireless group fault for the time being. The target proximity can be monitored in the first polling cycle. If the target proximity is greater than or equal to the first proximity threshold, the change degree of the target proximity can be monitored based on the second polling cycle to enhance the frequency of monitoring the target proximity, that is, the system can monitor the proximity between the target wireless network elements according to the set second polling cycle. Based on the target proximity, the system will obtain a prediction result to determine whether a wireless group failure may occur. Through the above steps, the system can dynamically predict and monitor according to the proximity between the wireless network elements, timely discover potential group failure risks, and take corresponding measures to intervene and handle them. In the above process, the monitoring cycle can be dynamically adjusted, and different monitoring cycles can be dynamically set according to actual conditions, which can more accurately capture the occurrence trend of wireless group failures and improve the accuracy and timeliness of predictions. According to different proximity thresholds and monitoring cycles, the system can flexibly adjust the sensitivity and accuracy of the warning according to actual conditions, so as to better adapt to the wireless group failure prediction needs in different environments. The wireless group failure prediction system based on the target proximity can more effectively help operators and network maintenance personnel predict and handle potential wireless group failure problems by setting dynamic and flexible periodic polling, thereby improving the operation efficiency and stability of the network.

[0063] In the above embodiment of the present application, determining the prediction result is that a wireless group failure will occur in a preset area at a preset future time, including: obtaining target warning parameters of multiple target wireless network elements, wherein the target warning parameters are used to represent the number of users in the self-busy period of the preset area, the average resource utilization rate of the self-busy period, the user's feedback information on the communication quality, and the parameters determined by whether the current period belongs to the self-busy period; when the target warning parameter is greater than or equal to the preset warning parameter threshold, determining the prediction result is that a wireless group failure will occur in the preset area at a preset future time.

[0064] The above-mentioned number of users during busy hours may refer to the number of users in a specific time period in a preset area, and this time period may refer to a network busy period, such as a peak hour during the day or during a specific activity. By monitoring the change in the number of users during this time period, the load of the network can be determined.

[0065] The above-mentioned average resource utilization rate during busy hours may refer to the average value of network resource utilization rate in a specific time period within a preset area, including bandwidth utilization rate, channel utilization rate, etc. By monitoring these indicators, it is possible to understand whether network resources are sufficient to support current user needs and whether there is a resource shortage.

[0066] The above-mentioned user feedback information on communication quality may refer to information on communication quality fed back by users in various ways, including evaluations of call quality, data transmission speed, network coverage, etc. By analyzing user feedback information, it is possible to understand user satisfaction with network quality and discover potential problems in a timely manner.

[0067] Whether the current time period is a self-busy time period may refer to whether the current time period is in a busy period of the network, which may refer to a peak time period during the day or a specific activity period. By determining whether the current time period is a self-busy time period, the network load situation can be determined and measures can be taken in time to deal with possible problems.

[0068] In an optional embodiment, during the early warning process of wireless group failure, it is determined that the prediction result is that the preset area has a tendency to enter the wireless group failure. In order to accurately predict the occurrence of wireless group failure, the target early warning parameters of multiple target wireless network elements can also be monitored and analyzed so as to timely discover abnormal situations and make early warnings. Specifically, the target early warning parameters of multiple target wireless network elements can be obtained first, and these parameters can include the number of users in the self-busy period, the average resource utilization rate in the self-busy period, the user's feedback information on the communication quality, and whether the current period belongs to the self-busy period. These parameters reflect the load, resource utilization and user experience of the wireless network, and can be used as data basis for judging the trend of wireless group failure. Then, the target early warning parameter can be monitored to determine whether the target early warning parameter is greater than or equal to the preset early warning parameter threshold. When the target similarity is greater than or equal to the set threshold, it means that there is a certain degree of similarity between the multiple target wireless network elements, and they may be affected by some influence at the same time, resulting in the occurrence of wireless group failure. Further, when the target early warning parameter is greater than or equal to the preset early warning parameter threshold, it is determined that the prediction result is that the preset area will have a wireless group failure at a preset future time, that is, the system issues a wireless group failure early warning notification. In the above process, by monitoring the early warning parameters of multiple target wireless network elements, the operation status of the wireless network can be more comprehensively understood, and the risk of wireless group failure can be discovered in time. By setting the target proximity coefficient and target early warning parameters, false alarms can be avoided, and the accuracy and reliability of early warnings can be improved. It is convenient to issue early warning notifications of wireless group failures in time, and operation and maintenance personnel can respond quickly and take measures to reduce the impact of wireless network failures on users and improve user experience and network quality.

[0069] In the above embodiment of the present application, determining the target proximity of multiple target wireless network elements includes: obtaining an area type of a preset area, wherein the area type includes an indoor scene type and an outdoor scene type, the indoor scene type is used to represent an indoor scene without a backup battery to power the wireless network elements in the preset area, and the outdoor scene type is used to represent an outdoor scene with a backup battery to power the wireless network elements in the preset area; when the area type is an indoor scene type, performing station spacing normalization processing on the multiple target wireless network elements based on a preset polygonal box to obtain the target proximity degree; when the area type is an outdoor scene type, performing station spacing normalization processing on the multiple target wireless network elements based on the site hierarchy relationship between the multiple target wireless network elements to obtain the target proximity degree.

[0070] In an optional embodiment, the wireless group fault warning can timely discover and handle a large number of out-of-service faults in the wireless network to improve the stability and reliability of the network. Obtaining the target proximity of multiple target wireless network elements in a preset area can help operators more accurately determine the area where group faults may occur and take corresponding preventive measures. Specifically, for indoor scene type areas, multiple target wireless network elements can be normalized by using a preset polygonal box. This processing process can help determine the distance between target wireless network elements and calculate the proximity between them accordingly. In this way, it can be found that in the same indoor scene, wireless network elements with closer distances may have a higher possibility of mutual interference, which helps us to discover potential group fault hazards in advance. For outdoor scene type areas, the station spacing can be normalized based on the site hierarchical relationship between multiple target wireless network elements. In outdoor environments, the site hierarchical relationship of wireless network elements is usually affected by factors such as terrain and buildings, and the distance and mutual influence between different sites will also be different. By analyzing the site hierarchical relationship, the proximity between different sites can be more accurately evaluated, so as to carry out group fault warning in a targeted manner. In the above process, by evaluating the proximity between the target wireless network elements, the area where group failure may occur can be judged more accurately to avoid false alarms or missed alarms. For indoor scene type areas, the method of automatically processing the normalization of station spacing can reduce the cost of manual intervention and improve the efficiency and real-time nature of early warning. For outdoor scene type areas, targeted early warning strategies can be formulated according to the characteristics of different area types and site hierarchical relationships to improve the pertinence and effectiveness of early warning.

[0071] In the above-mentioned embodiment of the present application, wireless group fault warning information is generated based on network element attribute data, including: based on the network element attribute data, feature positioning of multiple target wireless network elements is performed to obtain a multi-level feature matrix, wherein the multi-level feature matrix is ​​used to represent a hierarchical attribute matrix of a multi-level structure to which the multiple target wireless network elements belong, and the multi-level structure includes at least a radio frequency unit, a baseband unit and a computer room; based on the multi-level feature matrix, fault analysis is performed on the multiple target wireless network elements to obtain a fault cause positioning result; based on the fault cause positioning result, wireless group fault warning information is generated.

[0072] In an optional embodiment, a warning can be given to a wireless group fault, and a fault analysis can be performed. When a large number of wireless network elements have out-of-service faults in the same geographical area, the cause of the fault can be quickly and accurately found and repaired to ensure the stability and reliability of the network. Specifically, first, feature positioning can be performed on multiple target wireless network elements. Feature positioning can refer to extracting feature information of each wireless network element by analyzing the performance parameters, alarm information, log records, etc. of the target wireless network element, so as to locate the cause of the subsequent fault. In the feature positioning process, the attributes of each level in the multi-level structure can be considered, including radio frequency units, baseband units, and machine rooms. Secondly, based on the multi-level feature matrix obtained by feature positioning, a fault analysis can be performed on multiple target wireless network elements. In this process, the common points and differences between different wireless network elements can be found by comparing their feature information. By analyzing these feature information, the possible location and range of the cause of the fault can be further determined. Finally, based on the results of the fault analysis of the multi-level feature matrix, the cause of the fault of multiple target wireless network elements can be located. Through this process, the root cause of the wireless group fault can be accurately found, and corresponding measures can be taken to repair and prevent it. At the same time, this multi-level feature matrix fault analysis method can also help network operation and maintenance personnel better understand the entire network structure and improve the efficiency of fault location and processing. Through the above implementation process, the fault analysis method based on the multi-level feature matrix also enables network operation and maintenance personnel to better understand and manage the network structure, timely discover potential fault hazards and repair them, thereby improving the stability and reliability of the network, and can improve the accuracy of fault location, speed up fault processing, and reduce the impact of faults on network operation, thereby improving the efficiency and reliability of the entire network operation.

[0073] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0074] Baseband processing unit / baseband unit (Building Baseband Unit, referred to as BBU), BBU is one of the core devices of the base station, responsible for baseband signal processing and base station control. BBU can be placed in the machine room or macro base station room, used to process core network and user signaling data.

[0075] The Remote Radio Unit (RRU) is responsible for radio frequency processing, converting intermediate frequency signals into radio frequency signals and transmitting them through antennas. By separating the RRU and BBU, signal attenuation is reduced and network efficiency is improved.

[0076] Active Antenna Unit (AAU) improves network performance by integrating RRU and antenna, achieves the purpose of multi-channel initialization, reduces signal attenuation and improves signal transmission efficiency.

[0077] Application Programming Interface (API) allows developers to define the communication methods between functional components through some predefined functions, thus completing a data interaction between users and developers.

[0078] Hypertext Transfer Protocol Request (HTTP for short) may refer to a request message from a client to a server. HTTP requests run at the application layer and include commonly used GET requests and POST requests.

[0079] The same-site relationship may refer to the 4G, 5G RRUs and cells under the same wireless base station, and the longitude and latitude coordinates of these RRUs and cells are the same.

[0080] A polygonal box refers to treating the wireless network coverage area as a polygonal area, and using latitude and longitude coordinates to mark the boundaries of this area on the map. It is often used to mark closed areas such as university campuses, residential areas, industrial parks, large supermarkets, scenic spots, etc.

[0081] A wireless group failure may refer to a failure in which a large number of macro base stations or indoor RRUs in a contiguous geographical area are decommissioned at the same time or successively in a short period of time, that is, a failure that makes it impossible to provide wireless services. Since wireless group failures involve a large number of wireless network elements, and these network elements are in adjacent contiguous geographical areas, when these network elements are decommissioned at the same time, users in the area cannot rely on wireless network elements in the current location or adjacent locations to provide wireless network services, which has a significant impact on the wireless communications of wireless users in this geographical area. In contiguous and adjacent geographical areas, whether macro base stations or indoor RRUs, when a BBU is decommissioned, the decommissioning of the BBU will cause the decommissioning of the RRUs and cells under it, and the decommissioning of the RRUs will cause the decommissioning of one or more logical cells configured by it. When the number reaches a certain specified threshold, the communication operators in each province and city will set it as needed and determine that a wireless group failure has occurred.

[0082] The average resource utilization rate during the busy period, that is, the average PRB utilization rate during the busy period. The physical resource block (PRB) can be the basic channel resource unit in the 4G and 5G networks, which is composed of continuous subcarriers in the frequency domain and continuous orthogonal frequency division multiplexing symbols in the time domain. PRB utilization refers to the overall utilization calculated in the time domain and frequency domain, and is usually referenced at the service access point between the media access control layer and the physical layer. The average PRB utilization rate during the busy period refers to the average PRB utilization rate of each 4G and 5G cell during its busiest period, which is closely related to the number of users and service throughput.

[0083] The technical solution proposed in the present application is described below in conjunction with an optional embodiment. The present application proposes a system and method for automatic early warning of wireless group failures and automatic analysis and location of problem links. The present application belongs to the field of wireless communications, and specifically relates to a system and method for early warning of wireless group failure risks, and can automatically analyze and locate problem links and propose solutions.

[0084] At present, the scale of 4G and 5G wireless networks continues to expand, and the number of users and types of services served continue to grow, which increases the complexity of operators' maintenance work and makes the maintenance work very arduous. Once a wireless group failure occurs in a certain geographical area, a large number of users and services in the area will find it difficult to use normally, requiring wireless network maintenance personnel to restore the network as soon as possible. At present, the network management platforms of various wireless equipment manufacturers only independently push alarms for each device. After other third-party platforms open the network management interface, they have the function of quasi-real-time statistics of the number of alarms in the entire network, but it is difficult to automatically determine whether a group failure has occurred. Multiple wireless failures that occur in different geographical areas in a similar period of time do not belong to wireless group failures. At present, it is necessary to rely on the manual experience of wireless network maintenance personnel and the geographical proximity of the retired wireless network elements to determine whether multiple wireless retired network elements cover a contiguous geographical area, and combine whether the total number of relevant retired network elements reaches the threshold, so as to determine whether it constitutes a wireless group failure. In addition, the network management platforms and various auxiliary platforms of various wireless equipment manufacturers, as well as wireless network maintenance personnel, find it difficult to timely discover the hidden dangers of wireless group failures before wireless group failures occur, and it is difficult to issue timely warnings. At the same time, the network management platforms and various auxiliary platforms of various wireless equipment manufacturers push alarm information to wireless network maintenance personnel, and only push a small amount of information data such as the number of alarms, the name of the alarm network element, and the type of alarm. It is difficult to help accurately locate which network element links of various network elements at different network levels are the main links that cause wireless group fault alarms. Moreover, once a wireless group fault occurs, various alarms such as main alarms, derivative alarms, and alarms outside the wireless group fault area are concentrated in the alarm system in a short period of time. The processing procedures of various alarms are different. The positioning operation of a large number of alarms is cumbersome, time-consuming and labor-intensive, which will affect the wireless network maintenance personnel's rapid and effective judgment of the main problems that cause wireless group fault alarms. Even many provincial and municipal wireless operators also run multiple sets of network management from different manufacturers at the same time. An administrator needs to switch back and forth between multiple sets of network management at the same time, which further increases the complexity of alarm handling and poses a great challenge to wireless network maintenance personnel in handling wireless group faults in a timely manner.

[0085] This application proposes a new system for early warning and problem location of wireless group failures. Based on this new system, a new method is used to determine whether adjacent physical areas are covered by judging the degree of geographical proximity of multiple wireless alarms related to the decommissioning of wireless network elements generated in a similar time period; then, when the degree of proximity is high, the new system automatically increases the monitoring frequency of wireless network elements in the relevant area, promptly obtains new alarms in the relevant area, and uses a new method to determine whether the wireless network elements corresponding to these alarms have a risk of wireless group failures, and promptly issues group failure risk warnings to wireless network maintenance personnel; then, the new method is used to locate the main problem links that lead to the risk of wireless group failures, and the results are promptly notified to the wireless network maintenance personnel to assist in quickly solving the problem. By timely warning and early warning of wireless group failures, as well as assisting in quickly locating the main problem links related to wireless group failure alarms, it is convenient for wireless network maintenance personnel to take intervention measures in advance to prevent the occurrence of wireless group failures or avoid further expansion of the impact range, thereby effectively reducing the impact of wireless group failures on users' wireless service perception.

[0086] The present application proposes a system and method for automatic early warning of wireless group failures and automatic analysis and location of problem links, which can automatically warn of wireless group failures and quickly analyze and locate the problem links of group failures that have occurred. In the operation and maintenance of wireless networks, the root causes of wireless group failures are mainly power supply problems, large-scale city power outages, abnormal power supply of the main power supply lines of wireless network elements with high concentration, or problems in the optical fiber transmission link, such as failure of the trunk optical cable of the macro station or indoor distribution station, failure of related optical cables in the bureau room where BBU is centrally deployed, and failure of the data transmission equipment connected to the BBU. The present application obtains the relevant alarm information of decommissioning in a timely manner for wireless indoor distribution system scenarios such as university campuses, residential areas, industrial parks, and large supermarkets with high density of wireless network elements, and judges the degree of proximity of wireless network elements, and timely issues early warning or alarm for wireless group failures. For wireless macro stations used for large-scale outdoor wireless coverage, the present application obtains the low voltage alarm information of the battery of the wireless macro station in a timely manner, and combines the topological relationship information of the adjacent positions of multiple alarm stations to predict the risk of wireless group failures in advance. At the same time, this application can automatically locate the alarm problem and recommend a solution in a timely manner based on the topological relationship of network elements at all levels of the wireless network, the alarm type, the alarm network element and the number of data information. This can timely remind the wireless network maintenance administrator to intervene in the risk of wireless group failures and avoid further expansion of the alarm scale.

[0087] The topological structure of the system of the present application can be as follows. According to the technical solution of the present application, four new modules are deployed, together with the wireless network management platform, hereinafter referred to as the "network management"; the basic data platform of the wireless network element equipment, hereinafter referred to as the "basic data platform"; the power and environmental data monitoring platform of the base station equipment, hereinafter referred to as the "dynamic environment platform", to form the system of the present technical solution. The four newly deployed modules are: system communication bus module, alarm data processing module, alarm data analysis module, and notification management module. The main functions of the system communication bus module are to obtain the alarm information of the wireless network element from the network management platform; to obtain the alarm information of the power equipment and line from the dynamic environment platform; to obtain the network element data at all levels related to various alarm information. The network element level includes RRU, BBU communication boards at all levels, BBU, RRU's uplink aggregation equipment, BBU rack, A equipment, that is, BBU's uplink wireless data transmission equipment; B equipment, that is, A equipment's uplink aggregation equipment. Obtain the data of the communication boards and BBUs at all levels of RRU and BBU from the wireless network management, and the data of the BBU rack, A equipment, and B equipment from the basic data platform, as well as the industrial parameter data of the wireless site. The acquired alarm data and relevant data of network elements at all levels are cached in the "data cache submodule" of the "alarm data processing module" to realize data interaction between the modules of this system.

[0088] The alarm data processing module includes: an alarm polling submodule, a data caching submodule, an alarm data parsing submodule, and an alarm data processing submodule. The main function is to obtain the main alarm information of the wireless network element through the polling mechanism, that is, the key alarm information related to the wireless group fault, and parse and process it. The main functions of each submodule are: the alarm polling submodule performs elastic periodic alarm polling on the wireless network element to determine whether the group fault warning threshold is reached. The data caching submodule temporarily caches the alarm data and the basic data of the relevant wireless network elements at all levels, so that the other submodules can quickly retrieve them for processing and analysis. The alarm data parsing submodule parses the format of the acquired alarm data to enhance the readability of the alarm data of the system of this application. The alarm data processing submodule converts the format of the parsed alarm data according to the system requirements of this application to facilitate subsequent data analysis. The alarm data analysis module includes a group fault warning analysis submodule, a transmission layer equipment fault analysis submodule, a BBU fault analysis submodule, and an RRU fault analysis submodule. Among them: the group fault warning analysis submodule analyzes the group fault risk of the received alarm data information; after judging that there is a group fault risk, the BBU fault analysis submodule, RRU fault analysis submodule and other submodules are started to analyze the alarm causes of the corresponding network layer network elements.

[0089] Notification management module, the notification module plays the role of external notification, which is used to send the group fault warning information or the analysis results of the group fault alarm to the wireless network maintenance administrator through group robots, text messages, and emails, and notify the relevant personnel to follow up. Alarm data interface principle, there are two main ways to obtain alarm data from the wireless network management platform. The first is to obtain it on demand from the northbound interface provided by the network management network element to the wireless communication operator, and the second is to access the network management system and export the alarm data from the network management alarm monitoring page. The second method process is more complicated and requires manual access to the network management through an account. Only designated IP addresses and trusted users have access rights, which is very unsuitable for the system described in this patent. In addition, the current industry advocates that the components should be decoupled to reduce the complexity of the software system implementation, while obtaining better security and platform maintainability in the later stage. The system of this application preferably adopts the first solution. The specific implementation details of the network management northbound interface will be embedded in the platform, and there will be no data exposure to the outside world, and users will not be able to access the data generated during the interaction between the system and the network management. Specifically, the network management will open an application interface for this system. The interaction between the network management and the system will be realized through this interface. The communication mode of this interface is bidirectional. Only the system can initiate the request first. The system first sends the authentication information to the interface. When the authentication is passed, the "bridge" of mutual trust access is built between the network management and the system. Then the system sends a request body with a specific structure to the interface. The request body contains what data the system wants to obtain from the network management. For alarm data, there are mainly "region", "time", "alarm code", "alarm type" and so on. After obtaining the request sent by the system, the network management interface will first parse whether the request is legal and whether the format is correct. When the request body meets the requirements, the network management will provide the corresponding data through the interface. If the requested data is illegal or the format is wrong, the network management will also give a corresponding error prompt. For security reasons, the function of the interface is single. The request and response between the network management and this system can only be realized through the interface of specific functions. There will not be an interface that realizes multiple functions. The principle of alarm data acquisition and processing is to periodically poll the network management and dynamic environment platform for out-of-service and power supply alarms through the "alarm polling submodule" in the "alarm data processing module".

[0090] The system adopts a flexible periodic polling mechanism, for example, once every 5 minutes by default, that is, based on the first polling cycle, it regularly requests relevant alarm data from the network management. When there is no relevant alarm data, the alarm data processing module, the alarm data analysis module and the notification management module remain silent and do not occupy additional system resources. When the alarm data is received and the alarm network element analyzed by the "group fault warning analysis" submodule of the "alarm data analysis module", that is, the "comprehensive proximity coefficient α" of type one or the alarm network element, that is, the "comprehensive proximity coefficient β" of type two, satisfies α≥α0 or β≥β0, the notification polling submodule modifies the polling cycle and modifies the polling interval cycle to 5 seconds, that is, based on the second polling cycle, it can be set as needed, and continuously sends alarm information polling requests to the network management according to the new frequency. The polling target is all wireless network elements within a radius of 3km of the above-mentioned alarm network element, which can be set as needed, and the duration is set to 30 minutes, which can be set as needed, or "α<α0", "β<β0".

[0091] Alarm data parsing principle: after obtaining alarm data through the polling mechanism, the alarm data formats of the network management platform and the dynamic environment platform are different, and are inconsistent with the format required for subsequent analysis of this system. Therefore, the acquired data is decoded in the "alarm data parsing" submodule, and the integrity is verified, and the data is reorganized according to the format required for subsequent analysis by the system. During the reorganization process, according to the physical structure of the wireless network element and the logical architecture of the wireless network, the alarms are divided into three categories: transmission equipment level alarms, BBU level alarms, and RRU level alarms. Alarm data processing principle: the "alarm data processing" submodule extracts important readable information from the parsed alarm data, including alarm information related to equipment decommissioning, cell unavailable alarms, battery low voltage alarms, AC power failure alarms, etc., administrative regions, and equipment involved in the alarm. Alarm data analysis principle: The "Alarm Data Analysis" module first statistically analyzes the number of major alarm codes (characterizing alarm types) related to the decommissioning of wireless devices to determine whether a wireless group failure has occurred and predict the risk of a wireless group failure. Once the conditions are met, the wireless network maintenance administrator is notified in a timely manner. Then, the "Alarm Data Analysis" module automatically analyzes and locates the main factors that cause wireless group failures or the main factors that cause wireless group failure warnings based on the topological relationship between the alarm code and the wireless network equipment, and notifies the wireless network maintenance administrator of the content of these factors in a timely manner.

[0092] The algorithm for judging the proximity of the distances between wireless sites. A wireless group fault refers to a serious fault that causes a large number of wireless network elements in a continuous geographical area to be decommissioned. Therefore, a reasonable and efficient algorithm should be used to quickly determine whether the home sites of multiple wireless RRUs related to the decommissioning alarm are in a continuous geographical area. This application uses a "proximity judgment algorithm" to achieve this purpose. At present, there are two main types of wireless group faults with relatively high probability of occurrence in the existing network and relatively large business impact range: the first type is a typical scenario in which the campuses of universities, industrial parks, large shopping malls and supermarkets, residential areas, etc., have a high density of wireless equipment deployment due to the construction of wireless indoor distribution systems, hereinafter referred to as "group fault type one". In this scenario, the wireless equipment RRU basically has no battery guarantee; the second type of scenario is that the macro stations in the area that are close to each other are decommissioned in a short period of time, hereinafter referred to as "group fault type two", and the AAU / RRU of the macro station basically has battery guarantee. All RRUs are classified according to the type of group fault to which they belong, either belonging to type one or type two. Different "proximity judgment algorithms" are used for two different types of group obstacles, where: for "group obstacle type 1", a "station spacing normalization algorithm based on polygonal frame" is used; for "group obstacle type 2", a "station spacing normalization algorithm based on site hierarchical relationship" is used. That is, when the area type is an indoor scene type, the station spacing of multiple target wireless network elements is normalized based on the preset polygonal frame to obtain the target proximity; when the area type is an outdoor scene type, the station spacing of multiple target wireless network elements is normalized based on the site hierarchical relationship between the multiple target wireless network elements to obtain the target proximity.

[0093] Based on the normalization algorithm of the similarity of polygon boxes, this algorithm is applicable to "group barrier type 1". The polygon box is a closed area marked with longitude and latitude, such as: university campus, residential area, industrial park, large supermarket, etc. Assume that the longitude and latitude coordinate set pol of a certain area polygon box is recorded as:

[0094] pol=((lon0,lat0),(lon1,lat1),…,(lon n ,lat n ));

[0095] lon1 and lat1 represent the longitude and latitude of the first coordinate point, lon2 and lat2 represent the longitude and latitude of the second coordinate point, n and lat nRespectively represent the longitude and latitude information of the nth coordinate point. In actual drawing, you only need to mark the vertices of the polygon area, and you can draw the polygonal box along the lines connecting the longitude and latitude coordinate points on the visual map. There are many ways to calculate the center point of the polygonal box. You can use the geographic space center point calculation or the shortest distance center point. The geographic center point is to convert the longitude and latitude space coordinate system into a plane coordinate system, and then convert the result into longitude and latitude coordinates. The center point is located in the center of the polygon; the shortest distance center point means that the sum of the distances from the center point to the surrounding multiple device coordinate points in the polygonal box is the shortest. The center point is not necessarily located in the center of the polygon. The calculated coordinates of the center point of the polygonal box are pol center It can be expressed as:

[0096] pol center =(lon center ,lat center );

[0097] After calculating the center point of the polygonal box, all device coordinates in the polygonal box are replaced with the coordinates of the center point. Each device in each polygonal box of the entire wireless network is processed according to the above method, and a coordinate information table of all RRUs in the entire wireless network can be obtained:

[0098] (RRU1,RRU2,…,RRU n );

[0099] The longitude and latitude of the first RRU are RRU1 = (lon1, lat1), the longitude and latitude of the second RRU are RRU2 = (lon2, lat2), and the longitude and latitude of the nth RRU are RRU n =(lon n ,lat n ).

[0100] Next, the shortest distance RRU is calculated based on the longitude and latitude coordinates. For all RRUs in all polygonal boxes, the search algorithm is used to match the distance between two RRU devices one by one. In order to avoid the same site and the same longitude and latitude devices from participating in the calculation, resulting in different sites and polygonal boxes being unable to form an association relationship, in the calculation process, not only the device with the shortest distance of 0 is saved, but also the first RRU with a distance greater than 0. For example: RRU1 and (RRU2,…,RRU n ) calculation, and the result is D(RRU1,RRU2),…,D(RRU1,RRU n ), D(·) represents the latitude and longitude distance function. Assume that RRU1 and RRU i If they are at the same site or in the same polygon, then D(RRU1,RRU i )=0,RRU iand RRU1 are the shortest distance relations; in addition, among all distance pairs greater than 0, D(RRU1,RRU j ) is the smallest, then RRU j It also has the shortest distance relationship with RRU1. After the first search is completed, (RRU1, RRU i ) and (RRU1,RRU j ) are added to the table. Here, we should pay attention to the position relationship. The first one is the original RRU and the second one is the target RRU, which means the target RRU is the one with the shortest distance to the original RRU. Continue the above search process: RRU2 and (RRU1, RRU3, …, RRU n )Compared with RRU n and (RRU1,…,RRU n-1 ) to find the RRUs whose shortest distances are equal to 0 (if any) and greater than 0 in turn.

[0101] After all RRUs are searched, a table of adjacent RRUs with the shortest distance is obtained: i ,RRU j ], represents the matrix RRU i and RRU j It is the adjacent RRU matrix. The distance between the corresponding RRUs is the shortest. They are a pair of adjacent RRUs. A pair of RRUs with the shortest distance equal to 0 belongs to adjacent RRUs. A pair of RRUs with the shortest distance greater than 0 also belongs to adjacent RRUs. i Non-zero shortest distance RRU j The distance between RRU is taken as the reference denominator. i The remaining RRUs in the adjacent RRU matrix are i The distances between them are normalized. For example: the distances between RRU1 and RRU2, RRU3, and RRU4 are 260 meters, 200 meters, and 300 meters, among which RRU3 is the shortest between RRU1 and other adjacent RRUs, and 200 meters is used as the normalized denominator. After normalization, the proximity between RRU1 and RRU2, RRU3, and RRU4 is 1.3, 1, and 1.5, respectively. Set the upper limit of the proximity as needed, for example, set it to 3, and then for each RRU in the polygonal box, establish an adjacent RRU subset with the adjacent RRU, and further establish proximity subsets N1 and N2. Among them: N1 is the set of each RRU in the polygonal box and the adjacent RRU subset with a proximity of 1, N1 = {n 11 ,n 21 ,…,n m1},n m1represents the adjacent RRU subsets whose similarity to RRUm is 1, such as {RRU1, RRU3} in the above example; N2 is the set of adjacent RRU subsets whose similarity to each RRU in the polygon is in the interval (1,3), N2 = {n 12 ,n 22 ,…,n m2},n m2 Indicates the adjacent RRU subset whose similarity to RRUm is in the interval (1,3], such as {RRU1, RRU2}, {RRU1, RRU4} in the above example. For all RRUs in all polygonal boxes, according to the above method, first establish adjacent RRU subsets, and then establish similarity subsets N1 and N2. Aggregate N1 and N2 of all polygonal boxes to form a "normalized adjacent RRU set" N, N = {N1, N2}. When two RRUs belonging to the "group fault type 1" scenario belong to a certain adjacent RRU subset of N1 or N2, it means that the two RRUs "are 1 in similarity" or "are in the interval (1,3]".

[0102] This algorithm is applicable to "group obstacle type 2" based on the normalization algorithm of the degree of proximity of the site hierarchy. Different from the relatively closed environment of group obstacle type 1, the macro station environment belonging to type 2 is relatively open. When the RRU of a base station in a certain place is decommissioned, the original coverage area of ​​the base station may still receive the wireless signals of the first-layer or second-layer stations adjacent to the periphery. For example: there are 3 macro stations within 500 meters around a macro station, and there are also three stations within 500 meters to 1000 meters. The 500-meter range is used as a first-layer station, and the macro stations within 500 meters to 1000 meters are used as second-layer stations; when the macro station equipment is decommissioned, the wireless users in the original coverage area of ​​the macro station are likely to receive the wireless signals of the surrounding first-layer stations. Even if the first-layer stations are also decommissioned, users may receive the wireless signals of the second-layer stations, and wireless communication can still be barely maintained. The algorithm here is used to determine the similarity of RRUs of multiple macro sites that have been retired or are at risk of retirement. The specific algorithm is as follows: Each macro site in the entire network is uniquely numbered, and the set of all macro sites is represented as: (Base1, Base2, …, Base n ), where the longitude and latitude of the macro station numbered n is Base n =(lon n ,lat n ). Use the search algorithm to match the longitude and latitude distances between two macro stations within a specified range (for example, all macro stations within a radius of 1500 meters from macro station Base1), and get the distances from Base1 to all surrounding base stations within a radius of 1500 meters: D(Base1,Base2),...,D(Base1,Base m ), D(·) represents the latitude and longitude distance function. In the same way, we can get Base2, Base3, ..., Basen The distance to all surrounding macro stations within a radius of 1500 meters. In order to distinguish whether a macro station is a first-layer station or a second-layer station and the other macro stations within a radius of 1500 meters, it is necessary to normalize the distance between the first-layer and second-layer stations. That is, the empirical average distance between the first-layer and second-layer stations is used as the reference denominator, and the actual distance between the two macro stations is used as the reference numerator, and the distance between the two macro stations is normalized. The distance normalization function of the first-layer station is:

[0103]

[0104] The above formula represents the normalized function of the one-layer station spacing between macro stations i and j, and d1 is the one-layer station distance.

[0105] The normalized function of the second-layer station is expressed as:

[0106]

[0107] The above formula represents the normalized function of the second-layer station spacing between macro stations i and j, and d2 is the second-layer station distance.

[0108] For Macro Base i and Base j :if and are all in the interval (0,1], then Base j Base i The first floor station; if and If it is in the interval (0,1], then Base j Base i For example, if the macro base station within the range of 500 meters is a first-layer base station, and the base station within the range of 500 meters to 1000 meters is a second-layer base station. The distance from Base1 to Base2 is 400 meters, and the distance from Base1 to Base3 is 800 meters. The normalized function of the first-layer base station spacing can be calculated: The normalized function of the second-layer station spacing is: Therefore, Base2 is the first-layer station of Base1, and Base3 is the second-layer station of Base1. The above algorithm is used for each macro station and the surrounding macro stations within a radius of 1500 meters (set as needed) to obtain the first-layer station subset and the second-layer station subset of each macro station. All the first-layer station subsets are summarized to form the first-layer station set S1, S1 = {s 11 ,s 21 ,…,s n1},s n1 Refers to the first-layer station subset of the macro station numbered n. Summarize all second-layer station subsets to form the second-layer station set S2, S2 = {s12 ,s 22 ,…,s n2},s n2 Refers to the second-layer station subset of the macro station numbered n. S1 and S2 are aggregated to form the site hierarchy set S of the macro station, S = {S1, S2}. When the two macro stations that have decommissioning-related alarms belong to subset S1 or S2, it means that the hierarchical relationship between the two macro stations is a first-layer station or a second-layer station, respectively. By introducing the "normalized adjacent RRU set" N and the "site hierarchy set of macro stations" S, the system avoids the need to perform calculations every time when judging the similarity of the wireless sites involved in the decommissioning-related alarms, thereby speeding up the judgment. After the "normalized adjacent RRU set" N and the "site hierarchy set of macro stations" S are established for the first time, as the wireless equipment at the new site is gradually connected to the network, or as the wireless equipment at the existing site is gradually dismantled, N and S need to be updated in a timely manner.

[0109] Based on the wireless group fault warning algorithm of "comprehensive proximity coefficient" and "warning coefficient", that is, the target proximity and target warning coefficient, once a group fault occurs, the wireless communication of a large number of users will be significantly affected, and a large number of wireless network quality complaints will be generated at that time or afterwards. This application uses the scale and proximity-related factors of the network elements involved in the wireless group fault, and the severity-related factors that cause wireless user complaints as references to set the threshold of the wireless group fault warning. The system automatically determines whether the real-time values ​​of the "comprehensive proximity coefficient" and "warning coefficient" exceed the warning trigger threshold. For type 1 wireless group faults, the potential severity of wireless complaints is related to the number of decommissioned RRUs or wireless logical cells, hereinafter referred to as the number of decommissioned network elements; the proximity factor between the RRU with decommissioning alarm and the nearest adjacent RRU with alarm, hereinafter referred to as the proximity factor; the number of service self-busy-hour users under the decommissioned RRU or wireless logical cell, hereinafter referred to as the self-busy-hour user number factor; whether it is in the service self-busy-hour time period, hereinafter referred to as the time period factor; the average resource utilization rate of the wireless logical cell during the self-busy-hour period, hereinafter referred to as the load factor; the complaint tendency of the wireless site scenario, hereinafter referred to as the scenario factor, and other parameters. For type 2 wireless group faults, the potential severity of wireless complaints is related to the number of decommissioned macro sites, hereinafter referred to as the number of decommissioned macro sites; the hierarchical relationship between the inter-station distances of the decommissioned macro sites, hereinafter referred to as the hierarchical factor; the number of service self-busy-hour users of the decommissioned macro sites, hereinafter referred to as the self-busy-hour user number factor; whether it is in the service self-busy-hour time period, the average resource utilization rate of the wireless logical cell during the self-busy-hour period, and the complaint tendency of the main scenarios covered by the wireless site, hereinafter referred to as the scenario factor, and other parameters.

[0110] The description and value of the above factors are as follows: Number of retired network elements: n net ; Similarity factor: ne rru={1,0.7,0.5}, similarity = 1, value 1; similarity is between (1,1.5], value 0.7; similarity > 1.5, value 0.5, can be adjusted as needed; self-busy user number factor: n user ={1,0.5,0.4}, that is, the number of users during the busy period. If the number of users during the busy period is ≥100, the value is 1; if the number of users during the busy period is 50≤the number of users during the busy period is <100, the value is 0.8; if the number of users during the busy period is <50, the value is 0.4. It can be adjusted as needed. Time period factor: t busy ={1,0.5}, that is, whether the current time period belongs to the self-busy time period. When the time is self-busy, the value is 1; when it is not self-busy, the value is 0.5, which can be adjusted as needed; Load factor: p load ={1,0.6,0.3}, that is, the average resource utilization rate during the busy period, the average PRB during the busy period ≥ 70%, the value is 1; 30% ≤ the average PRB during the busy period < 70%, the value is 0.6; the average PRB during the busy period < 30%, the value is 0.3, which can be adjusted as needed; scenario factor, td scene ={1,0.7,0.4}, which is the user feedback information on communication quality. According to the annual complaint data, the top three scenarios with the largest number of wireless complaints take a value of 1; the scenarios with the number of wireless complaints between the fourth and sixth take a value of 0.7; the rest take a value of 0.4, which can be adjusted as needed; Number of decommissioned macro sites: n base ; Hierarchy factor: r gap ={1,0.6}, two macro stations are in a one-layer station relationship, the value is 1; two macro stations are in a two-layer station relationship, the value is 0.6. Using the above factors, this application proposes two different types of wireless group failure warning coefficient algorithms. When the warning coefficient exceeds the set threshold, the system issues a wireless group failure warning notification.

[0111] In this application, based on the different area types of the preset areas, the target proximity degree can be expressed as α or β, the target warning parameter can be expressed as A or B, the first proximity threshold (α1 or β1), the second proximity threshold (α0 or β0) and the preset warning parameter threshold (A1 or B1).

[0112] The early warning coefficient algorithm of type 1 wireless group obstacle corresponds to type 1 wireless group obstacle, that is, the indoor scene type. The early warning coefficient A can be expressed as:

[0113]

[0114] For type 1 wireless group fault, the “comprehensive proximity coefficient” α, which comprehensively represents the number and proximity of the out-of-service alarm RRUs, can be expressed as:

[0115]

[0116] The "comprehensive proximity coefficient" α determines whether to increase the polling frequency of the alarm polling submodule and whether to issue a wireless group fault warning based on the number of out-of-service alarms and the proximity of the sites involved. The "warning coefficient" A, based on the number of out-of-service alarms and the proximity of the sites involved, additionally considers other weight factors related to the severity of wireless user complaints, namely the number of users during busy hours, time period factors, load factors, and scenario factors, to determine whether to issue a wireless group fault warning from another dimension. When multiple RRUs of type 1 are out of service, the "group fault warning analysis" submodule calculates the "comprehensive proximity coefficient" α and the "warning coefficient" A, where: when "α<α0", the polling cycle of the "alarm polling" submodule remains unchanged, and at the same time, the fault analysis submodules of network elements at all levels, such as the "BBU fault analysis" submodule and the "RRU fault analysis" submodule, do not need to be started for fault analysis; when "α≥α0", the "group fault warning analysis" submodule feeds back information to the "alarm polling" submodule to notify it to modify the polling cycle and increase the polling frequency. At the same time, the fault analysis submodule starts to perform fault analysis; when "α≥α1", note that α1>α0, or the warning coefficient A satisfies "A≥A1", the "group fault warning analysis" submodule feeds back information to the "notification management" module, which sends the wireless group fault warning information to the wireless network maintenance administrator. The warning information contains key information such as the site name, RRU name, upstream BBU name, and the main possible reasons for the generation of the wireless group fault warning.

[0117] The early warning coefficient algorithm for type 2 wireless group failure corresponds to type 2 wireless group failure, that is, the outdoor scene type. The early warning coefficient B is:

[0118]

[0119] For type 2 wireless group fault, the “comprehensive proximity coefficient” β, which comprehensively represents the number of macro base station low voltage alarms and the degree of proximity of the macro base stations involved, is:

[0120]

[0121] The "comprehensive proximity coefficient" β determines whether to increase the polling frequency of the alarm polling submodule and whether to issue a wireless group fault warning based on the number of macro base station low voltage alarms and the level of inter-station spacing involved. The "warning coefficient" B, based on the number of macro base station low voltage alarms and the level of inter-station spacing involved, additionally considers other weight factors related to the severity of wireless user complaints, namely the number of users during busy hours, time period, load, and scenario factors, to determine whether to issue a wireless group fault warning from another dimension. When "β<β0", the polling cycle of the "alarm polling" submodule remains unchanged. At the same time, the fault analysis submodules of network elements at all levels, such as the "BBU fault analysis" submodule and the "RRU fault analysis" submodule, do not need to be started for fault analysis; when "β≥β0", the "group fault warning analysis" submodule feeds back information to the "alarm polling" submodule, notifying it to modify the polling cycle and increase the polling frequency. At the same time, the fault analysis submodule starts to perform fault analysis; when "β≥β1", note that β1>β0, or the warning coefficient B satisfies "B≥B1", the "group fault warning analysis" submodule feeds back information to the "notification management" module, and the "notification management" module sends the wireless group fault warning information to the wireless network maintenance administrator. The warning information contains key information such as the macro station name, the upstream BBU name, and the main possible reasons for the wireless group fault warning.

[0122] The introduction of multi-level warning thresholds, the first proximity threshold (α1 and β1), the second proximity threshold (α0 and β0) and the preset warning parameter threshold (A1 and B1), plays a hierarchical response role, helps reduce misjudgment, helps reduce the operating load of the fault analysis submodules of the "BBU fault analysis" submodule, the "RRU fault analysis" submodule and other network elements at all levels, and helps reduce the operating load of the "alarm polling" submodule. With the increase in the number of low voltage alarms of retired RRUs or macro stations, and at the same time, with the difference in the weights of various factors of each wireless network element, the "comprehensive proximity coefficient" or "warning coefficient" increases at different rates. When one of the two reaches the preset value, the system immediately issues a wireless group fault warning notification.

[0123] Problem location analysis algorithm for alarms related to wireless network element decommissioning. Among all levels of wireless network elements, RRU is the network element device closest to wireless users. This application analyzes and locates the relevant links that affect the wireless service capabilities of RRU. The fault links that will cause RRU decommissioning are extended from the RRU network element to the upper network element links, mainly including: RRU, BBU, and transmission layer equipment. Among them: RRU links include optical path failures, power supply failures, etc.; BBU links include communication board failures, optical path failures, power supply failures, etc.; transmission layer equipment includes equipment port failures, optical path failures, power supply failures, etc. The degree of influence on the occurrence of wireless group failures, from high to low: transmission layer equipment → BBU → RRU. Assuming that RRU fails, only RRU alarms will be triggered, but BBU alarms will not be triggered. In addition to wireless group failures caused by power outages and trunk optical cable interruptions, the possibility of a large number of RRU devices having failures at the same time is very low. At this time, it is difficult to find the real cause of the failure if only the RRU at this level is analyzed, and it is also necessary to combine the upper-level equipment, that is, BBU and transmission layer equipment for analysis. However, when the upper-level equipment of the RRU fails, a large number of RRUs will fail at the same time. Therefore, after determining the type of group fault, the cause of the group fault is located in combination with the characteristics of the group fault.

[0124] Here, the RRU feature location method is introduced. Features refer to the RRU hierarchical attributes. For example, by specifying the machine room, rack, BBU and board, the target RRU can be found. When the machine room, rack, BBU or board fails, the RRU will generate an alarm. A one-dimensional feature matrix is ​​established for each RRU: i = [board number, BBU number, rack number, room number], then all RRUs are: RRU = [RRU1, RRU2, ..., RRU N ] T Find the maximum number of identical elements in each column of the RRU involved in the wireless group fault warning, and you can get the result: RRU num =[Num 板卡 , Num BBU , Num 机架 , Num 机房 ], then find the RRU num The category corresponding to the most elements in RRU, that is, cat = argmax (RRU num ), this category is the cause of the RRU group fault. For example, the wireless group fault warning involves multiple target RRU alarms, RRU1 = [1,0,1,1], RRU2 = [1,0,1,1], RRU3 = [1,0,2,1], RRU = [RRU1, RRU2, RRU3] T , RRU num =[2,1,2,3], argmax(RRU num) The result is the category of "machine room", so these RRU alarms have a characteristic that the upstream machine rooms all belong to the same machine room. A failure in a certain link in the machine room leads to multiple target RRU alarms, and then further on-site obstacle analysis and positioning of the machine room is carried out. Similarly, a BBU feature matrix can also be established: BBU i = [chain link number, B device number, A device number, bureau room number, frame number]. By analyzing these attributes, the link where the problem occurs can be quickly located. After completing the data analysis and integration, the data analysis module packages the analysis results, including: analysis files, statistical files and alarm notifications. The analysis and statistical files contain alarm network element location information, cause analysis and operation guides. The alarm notification contains information such as the alarm occurrence time, alarm type, alarm quantity, assessment threshold, and repair time.

[0125] The notification principle of early warning information, the alarm data analysis module will make corresponding integrated statistics and analysis based on the alarm information given by the network management, and the obtained result information data will reach the notification management module through the communication bus. The data analysis result data includes: analysis file, statistical file and alarm notification. The analysis file and statistical file contain the alarm network element location information, cause analysis and operation guide, and the alarm notification contains information such as the alarm occurrence time, alarm type, alarm quantity, assessment threshold, etc. The alarm notification will notify the designated wireless network maintenance personnel to track and handle through the group robot interface and SMS interface, so as to timely grasp the scale of the fault and the progress of the handling. In addition, the analysis report and statistical report will be sent to the designated mailbox via email. After receiving the alarm notification, the wireless network maintenance personnel will log in to the mailbox in time to view the detailed alarm information, analysis report and fault handling suggestions.

[0126] This application proposes a triple safety and reliability guarantee mechanism. The system has several major modules: network management platform, dynamic environment monitoring platform, alarm data processing module, alarm data analysis module, and notification management module. The alarm data stream is transmitted along the data bus, and the functions of each module are clear and do not cross each other. Guarantee mechanism one: The system adopts an interface development method. This application applies the low coupling concept in current software development to the development of wireless alarm monitoring. Through the interface method, the functional components are decoupled, the software reuse is improved, and the problems of unclear functions and redundant and chaotic codes in traditional development are avoided. At the same time, the difficulty of later code maintenance and version iteration is significantly reduced. Some local networks run multiple sets of network management systems from different manufacturers at the same time. An administrator needs to manage multiple sets of network management systems, which is difficult. This application also reduces the difficulty of handling alarms from multiple sets of network management systems through a new model of interface development. Guarantee mechanism 2: independent authentication mechanism, using interface development, can only send specific information to specific modules; each module has an independent authentication mechanism, and authentication needs to be verified before sending a request, and all links and requests are encrypted twice; the system runs in the cloud, and the operation and maintenance personnel can only see the pushed alarm related information, but cannot access the core data information, and are not clear about the relationship between the modules. Ensure that key information is not leaked. Guarantee mechanism 3: simple configuration and easy to use. The system described in the application only needs to configure a small amount of input data such as cities, mailboxes, and group robot IDs before use. After starting the software, the operation and maintenance personnel do not need to be on duty. The system has a fault-tolerant error correction mechanism, which automatically checks the results of each operation. After a module has an abnormal operation, the error correction mechanism will be automatically started, and the data flow bus will send a request to the module again. When the module still cannot give a correct response after a period of time, the system will intervene directly, stop running, and inform the system operation and maintenance personnel. The results of each operation are saved in the cloud for later tracing.

[0127] The present application proposes a normalized calculation method for the degree of proximity based on a polygonal frame. For each wireless network element in the polygonal frame, the straight-line distance between each network element and the nearest adjacent network element is used as the normalized denominator, and the straight-line distance between the network element and all other network elements in the polygonal frame is used as the numerator. The resulting value is used to measure the degree of proximity between the network element and all other network elements in the frame. This technical point expresses the subjective judgment ability of "wireless network elements in a certain area have successively generated decommissioning alarms" in a digital and quantitative manner. The use of this technology can assist in quickly and automatically determining that wireless network elements in a certain polygonal frame area have successively generated decommissioning-related alarms in a short period of time and there is a risk of wireless group failure. When this technical point is used to judge the degree of proximity of wireless station sites involved in decommissioning-related alarms in a polygonal frame, it avoids the need to perform calculations each time, thereby speeding up the judgment speed.

[0128] The present application proposes a normalized calculation method for the proximity based on the site hierarchy relationship. This technology uses the empirical data of the station spacing between the first-layer and second-layer stations between macro stations as the denominator, and the actual straight-line distance between macro stations as the numerator. The obtained value is used to measure the proximity and site hierarchy relationship between macro stations involved in the low-voltage battery alarm. This technology expresses the subjective judgment ability of "the site hierarchy topological relationship between macro stations corresponding to several low-voltage battery alarms" in a digital and quantitative way, and can quickly and automatically judge the approximate distance and site hierarchy relationship between macro stations corresponding to multiple randomly generated low-voltage battery alarms. The use of this technology can assist in the rapid and automatic judgment of the risk of wireless group failure in the area where the macro station corresponding to the low-voltage battery alarm is located. At the same time, when this technical point is used to judge the proximity of wireless macro stations involved in the low-voltage battery alarm of the macro station, it avoids the need to perform calculations every time, thereby speeding up the judgment speed.

[0129] The present application proposes an automatic judgment method for sending a wireless group fault warning notification. This technology uses the number of wireless network elements involved in the decommissioning related alarms, the degree of proximity, the scale of users affected, the tendency of user complaints, the wireless network load and other factors that affect the ability to provide basic wireless services in the target area and the severity of wireless complaints that may occur once a wireless group fault occurs as the judgment basis for automatically issuing wireless group fault warning information. As the number of decommissioned RRU or macro station low voltage alarms increases, and at the same time, with the difference in the weights of various factors of each wireless network element, the increasing speed of the "comprehensive proximity coefficient" or "warning coefficient" is different. When one of the two reaches the preset value, the system immediately issues a wireless group fault warning notification, which avoids both false warnings and untimely warnings.

[0130] The flexible periodic polling mechanism proposed in this application. Obtain the alarm information related to the decommissioning from the wireless network management and macro station power monitoring platform, and use the number and similarity of the wireless station sites involved in the relevant alarms obtained as the basis for automatic decision-making on the flexible adjustment of the polling cycle from large to small, and set the scope of the polling object network element in a targeted manner. Improve the timeliness of alarm information acquisition, and help to reasonably allocate system computing resources to avoid the rapid increase and waste of computing resource demand.

[0131] This application proposes an automatic analysis and positioning method for the problem link of the decommissioning-related alarm. Starting from the dimensions of RRU, BBU, transport layer equipment, etc., according to the decommissioning-related alarm list obtained during the polling cycle, combined with the alarm code information, according to the network topology hierarchy relationship, the characteristic matrix of the wireless network element is used for automatic analysis and positioning. According to the network topology hierarchy relationship, the wireless network element characteristic matrix is ​​used for automatic analysis, with clear logic, accurate positioning of the alarm problem link, and fast analysis speed, which helps wireless network maintenance personnel to handle problems efficiently and accurately.

[0132] Figure 3 is a schematic diagram of an optional early warning system for wireless group failure according to an embodiment of the present invention, such as Figure 3 As shown in the figure, the figure includes an interactive network management platform, a basic data platform, a dynamic environment platform, a system communication bus module, an alarm data processing module, an alarm analysis module and a notification management module, wherein the network management platform includes a northbound interface, the basic data platform includes an open interface, the dynamic environment platform includes an open interface, the alarm data processing module includes a data cache submodule, an alarm data parsing submodule, an alarm data processing submodule and an alarm polling submodule, the alarm analysis module includes a group fault warning analysis submodule, an RRU fault analysis submodule, a BBU fault analysis submodule and a transport layer equipment fault analysis submodule, and the notification management module includes a group robot interface, a SMS interface and an email interface.

[0133] Figure 4 is a schematic diagram of an optional elastic periodic polling process according to an embodiment of the present invention, such as Figure 4 As shown, alarm polling is performed according to the preset polling cycle to determine whether there is an alarm. If there is no alarm, the alarm data processing module, the alarm data analysis module and the notification management module remain silent, and the execution is jumped to perform alarm polling according to the preset polling cycle; if there is an alarm, the "group fault warning analysis submodule" analyzes the alarm data, and the analysis obtains: comprehensive similarity coefficient α or β, and determines whether it belongs to situation 1: α≥α0 or β≥β0, or situation 2: α<α0 or β<β0. When it belongs to situation 1, the polling cycle is set to 5 seconds, the polling cycle is changed, and the execution is jumped to perform alarm polling according to the preset polling cycle; when it belongs to situation 2, the polling cycle is set to 5 minutes, the polling cycle is changed, and the execution is jumped to perform alarm polling according to the preset polling cycle.

[0134] Figure 5 FIG. 1 is a schematic diagram of a process for determining the degree of proximity of targets in an optional indoor scene type according to an embodiment of the present invention. Figure 5As shown, a longitude and latitude coordinate set pol of all RRUs in a polygonal frame is established, and the longitude and latitude of the center point of the RRU in the polygonal frame are calculated. All RRU coordinates in the polygonal frame are replaced by the longitude and latitude of the center point. By using the above steps, a coordinate information table of all RRUs in all polygonal frames of the entire network is obtained, and the shortest distance between any two RRUs in the RRU coordinate information table is calculated to obtain a "shortest distance similar RRU table", which is normalized with the shortest non-zero distance between adjacent RRUs as the denominator, and an upper limit value of the degree of proximity is set to establish an "adjacent RRU subset" between adjacent RRUs, and further establish "similarity subsets" N1 and N2. According to the above steps, "similarity subsets" N1 and N2 of all polygonal frames are established, and N1 and N2 of all polygonal frames are aggregated to form a "normalized adjacent RRU set", N, N = {N1, N2}. When the two RRUs with faults belong to subsets N1 or N2, different degrees of proximity are represented.

[0135] Figure 6 FIG. 1 is a schematic diagram of a process for determining target proximity in an optional outdoor scene type according to an embodiment of the present invention. Figure 6 As shown, each macro station in the entire network is uniquely numbered, and the straight-line distance between each macro station and all other macro stations within the specified distance range is calculated to obtain the distance function. According to the preset average station spacing values ​​of the first and second layers of stations, the distance functions are normalized respectively. The first-layer station subset and the second-layer station subset of each macro station are obtained, and all the first-layer station subsets are aggregated to form the first-layer station set S1; similarly, the second-layer station set S2 is formed. S1 and S2 are aggregated to form the site level set S of the macro station. When the two macro stations that have failed belong to the subset S1 or S2, different degrees of proximity are represented.

[0136] Figure 7 is a schematic diagram of a target warning coefficient determination process in an optional indoor scene type according to an embodiment of the present invention, such as Figure 7 As shown, the warning coefficient A of the wireless group fault of type one and the "comprehensive proximity system" α are calculated. When α≥α0 is established, the polling cycle is modified and the polling frequency is increased. The "alarm analysis module" performs fault analysis, and when α≥α1 or A≥A1 is established, the "group fault warning analysis submodule" feeds back the analysis results to the "notification management" module, and the "notification management" module sends the wireless group fault warning information to the wireless network maintenance administrator.

[0137] Figure 8 FIG. 1 is a schematic diagram of a target warning coefficient determination process in an optional outdoor scene type according to an embodiment of the present invention. Figure 8As shown, the warning coefficient β of the wireless group fault of type 2 and the "comprehensive proximity system" β are calculated. When β≥β0 is established, the polling cycle is modified and the polling frequency is increased. The "alarm analysis module" performs fault analysis, and when β≥β1 or B≥B1 is established, the "group fault warning analysis submodule" feeds back the analysis results to the "notification management" module, and the "notification management" module sends the wireless group fault warning information to the wireless network maintenance administrator.

[0138] Fig. 9 is a schematic diagram of an optional fault location analysis according to an embodiment of the present invention, such as Fig. 9 As shown, a one-dimensional feature table is established for each RRU according to the board number, room number, rack number, and BBU number; the key information table of all RRUs is obtained by merging; the faulty RRUs are associated, and the number of each feature in the key information table of the faulty RRU is counted to obtain a feature quantity table; the feature corresponding to the element with the largest value in the feature quantity table is inversely calculated; the cause of the fault is located in the feature.

[0139] According to another aspect of the embodiment of the present application, a wireless group failure warning device is also provided, which can execute the wireless group failure warning method of the above embodiment. The specific implementation method and preferred application scenario are the same as the above embodiment and will not be repeated here.

[0140] Fig.10 is a schematic diagram of a wireless group fault warning device according to an embodiment of the present application, such as Fig.10 As shown, the device includes the following: an acquisition module 1002 , a determination module 1004 , a prediction module 1006 and a generation module 1008 .

[0141] Among them, the acquisition module is used to acquire the network element attribute data of multiple target wireless network elements in the preset area when a communication failure is detected in the preset area, wherein the multiple target wireless network elements are used to represent the wireless network elements in the preset area that have failed between the preset historical moment and the current moment; the determination module is used to determine the target proximity of the multiple target wireless network elements, wherein the target proximity is used to characterize the spatial aggregation degree between the multiple target wireless network elements; the prediction module is used to predict the wireless group failure of the preset area based on the target proximity to obtain a prediction result, wherein the prediction result is used to indicate whether the preset area will have a wireless group failure at a preset future moment, and the preset future moment is after the current moment; the generation module is used to generate wireless group failure warning information based on the network element attribute data when the prediction result is that the preset area will have a wireless group failure at a preset future moment, wherein the wireless group failure warning information includes: the fault cause location result of the fault of the multiple target wireless network elements.

[0142] Among them, the prediction module is also used to determine that the prediction result is that no wireless group failure will occur in the preset area at a preset future time when the target proximity is less than the first proximity threshold, and repeatedly execute the step of determining the target proximity of multiple target wireless network elements; when the target proximity is greater than or equal to the first proximity threshold, determine the prediction result is that a wireless group failure will occur in the preset area at a preset future time.

[0143] Among them, the prediction module is also used to repeatedly execute the step of determining the target proximity of multiple target wireless network elements based on the first polling cycle when the target proximity is less than the second proximity threshold, wherein the second proximity threshold is less than the first proximity threshold; and to repeatedly execute the step of determining the target proximity of multiple target wireless network elements based on the second polling cycle when the target proximity is greater than or equal to the second proximity threshold, wherein the second polling cycle is less than the first polling cycle.

[0144] Among them, the prediction module is also used to obtain target warning parameters of multiple target wireless network elements, wherein the target warning parameters are used to represent the number of users in the self-busy period based on the preset area, the average resource utilization rate of the self-busy period, the user's feedback information on the communication quality and the parameters determined whether the current period belongs to the self-busy period; when the target warning parameter is greater than or equal to the preset warning parameter threshold, it is determined that the prediction result is that a wireless group failure will occur in the preset area at a preset future time.

[0145] Among them, the determination module is also used to obtain the area type of a preset area, wherein the area type includes an indoor scene type and an outdoor scene type, the indoor scene type is used to represent an indoor scene without a backup battery to power the wireless network elements in the preset area, and the outdoor scene type is used to represent an outdoor scene with a backup battery to power the wireless network elements in the preset area; when the area type is an indoor scene type, the station spacing of multiple target wireless network elements is normalized based on a preset polygonal box to obtain a target proximity; when the area type is an outdoor scene type, the station spacing of multiple target wireless network elements is normalized based on the site hierarchy relationship between the multiple target wireless network elements to obtain a target proximity.

[0146] Among them, the generation module is also used to perform feature positioning of multiple target wireless network elements based on network element attribute data to obtain a multi-level feature matrix, wherein the multi-level feature matrix is ​​used to represent the hierarchical attribute matrix of the multi-level structure to which the multiple target wireless network elements belong, and the multi-level structure includes at least a radio frequency unit, a baseband unit and a computer room; based on the multi-level feature matrix, fault analysis is performed on the multiple target wireless network elements to obtain fault cause positioning results; based on the fault cause positioning results, wireless group fault warning information is generated.

[0147] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0148] The above-mentioned memory may refer to a device inside a computer for storing data and programs, and may include memory, hard disk, etc., wherein the memory may be used to temporarily store running programs and data, the hard disk may be used to store programs and data for a long time, and the memory may be used to enable the computer to read and write data, and execute programs; the above-mentioned processor may be responsible for executing instructions in computer programs and performing data processing, and may be responsible for controlling and executing various operations, including arithmetic operations, logical operations, data transmission, etc.

[0149] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0150] The above-mentioned computer storage medium may refer to a medium in a computer memory used to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser disks, etc.; the stored program included in the computer-readable storage medium may be a set of instructions that can be recognized and executed by a computer, running on an electronic computer, and is an information tool that meets certain needs of people.

[0151] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0152] The above-mentioned computer program product may refer to a software program that has been written, tested and released and can be run on a computer or other device. The computer program product may include an application, an operating system, tool software, etc., which is used to implement specific functions or solve specific problems.

[0153] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0154] The above-mentioned non-volatile computer-readable storage medium may refer to a medium for storing data. The non-volatile computer-readable storage medium can keep the data from being lost when the power is off, and can be used to store long-term data, such as operating systems, applications, and user files. The non-volatile storage medium may include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0155] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0156] The above-mentioned computer program may refer to a collection of instructions used to tell a computer to perform a specific task or operation. A computer program may be written by a programmer using a specific programming language and may include algorithms, data structures, logic, and control flows. A computer program may be used for a variety of purposes, including application software, operating systems, and the like.

[0157] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0159] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0160] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0162] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for early warning of wireless group failure, characterized in that: include: In the case where a communication failure is detected in a preset area, acquiring network element attribute data of a plurality of target wireless network elements in the preset area, wherein the plurality of target wireless network elements are used to represent wireless network elements in the preset area that have failed between a preset historical moment and a current moment; Determining a target proximity degree of the multiple target wireless network elements, wherein the target proximity degree is used to characterize a spatial aggregation degree between the multiple target wireless network elements; Based on the target proximity, a wireless group failure prediction is performed on the preset area to obtain a prediction result, wherein the prediction result is used to indicate whether a wireless group failure will occur in the preset area at a preset future time, and the preset future time is after the current time; When the prediction result is that a wireless group failure will occur in the preset area at the preset future time, wireless group failure warning information is generated based on the network element attribute data, wherein the wireless group failure warning information includes: fault cause locating results of the faults occurring in the multiple target wireless network elements.

2. The method for early warning of wireless group failure according to claim 1, characterized in that: Based on the target proximity, a wireless group obstacle prediction is performed on the preset area to obtain a prediction result, including: When the target proximity is less than the first proximity threshold, determining the prediction result as no wireless group failure will occur in the preset area at the preset future time, and repeating the step of determining the target proximity of the multiple target wireless network elements; When the target proximity degree is greater than or equal to the first proximity degree threshold, it is determined that the prediction result is that a wireless group failure will occur in the preset area at the preset future time.

3. The method for early warning of wireless group failure according to claim 2, characterized in that: Repeating the step of determining target proximity of the multiple target wireless network elements includes: In a case where the target proximity is less than a second proximity threshold, based on the first polling cycle, repeatedly performing the step of determining the target proximity of the multiple target wireless network elements, wherein the second proximity threshold is less than the first proximity threshold; When the target proximity is greater than or equal to the second proximity threshold, the step of determining the target proximity of the multiple target wireless network elements is repeated based on a second polling cycle, wherein the second polling cycle is smaller than the first polling cycle.

4. The method for early warning of wireless group failure according to claim 2, characterized in that: Determining the prediction result that a wireless group failure will occur in the preset area at the preset future time includes: Acquire target warning parameters of the multiple target wireless network elements, wherein the target warning parameters are used to represent parameters determined based on the number of users in the self-busy period of the preset area, the average resource utilization rate of the self-busy period, user feedback information on communication quality, and whether the current period belongs to the self-busy period; When the target warning parameter is greater than or equal to a preset warning parameter threshold, it is determined that the prediction result is that a wireless group failure will occur in the preset area at the preset future time.

5. The method for early warning of wireless group failure according to claim 1, characterized in that: Determining the target proximity of the multiple target wireless network elements includes: Obtaining an area type of the preset area, wherein the area type includes an indoor scene type and an outdoor scene type, the indoor scene type is used to represent an indoor scene without a backup battery to power the wireless network elements in the preset area, and the outdoor scene type is used to represent an outdoor scene with a backup battery to power the wireless network elements in the preset area; When the area type is the indoor scene type, performing station spacing normalization processing on the multiple target wireless network elements based on a preset polygonal frame to obtain the target proximity degree; In the case where the area type is the outdoor scene type, based on the site hierarchical relationship between the multiple target wireless network elements, the multiple target wireless network elements are normalized for each other to obtain the target proximity degree.

6. The method for early warning of wireless group failure according to claim 1, characterized in that: Based on the network element attribute data, wireless group fault warning information is generated, including: Based on the network element attribute data, feature positioning is performed on the multiple target wireless network elements to obtain a multi-level feature matrix, wherein the multi-level feature matrix is ​​used to represent a hierarchical attribute matrix of a multi-level structure to which the multiple target wireless network elements belong, and the multi-level structure includes at least a radio frequency unit, a baseband unit, and an equipment room; Performing fault analysis on the multiple target wireless network elements based on the multi-level feature matrix to obtain the fault cause location result; The wireless group fault warning information is generated based on the fault cause positioning result.

7. A wireless group fault warning device, characterized in that: include: An acquisition module, configured to acquire network element attribute data of a plurality of target wireless network elements in a preset area when a communication failure is detected in the preset area, wherein the plurality of target wireless network elements are used to represent wireless network elements in the preset area that have failed between a preset historical moment and a current moment; A determination module, configured to determine a target proximity degree of the multiple target wireless network elements, wherein the target proximity degree is used to characterize a spatial aggregation degree between the multiple target wireless network elements; A prediction module, configured to perform a wireless group failure prediction on the preset area based on the target proximity degree, and obtain a prediction result, wherein the prediction result is used to indicate whether a wireless group failure will occur in the preset area at a preset future time, and the preset future time is after the current time; A generation module is used to generate wireless group failure warning information based on the network element attribute data when the prediction result is that a wireless group failure will occur in the preset area at the preset future time, wherein the wireless group failure warning information includes: the fault cause location result of the faults of the multiple target wireless network elements.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program executes the early warning method for wireless group failure described in any one of claims 1 to 6 when running.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the wireless group failure early warning method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the early warning method for wireless group failure according to any one of claims 1 to 6.

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