Method for measuring wireless indicator, and storage medium and electronic apparatus

Abstract

Provided in the embodiments of the present disclosure are a method for measuring a wireless indicator, and a storage medium and an electronic apparatus. The method comprises: on the basis of a measurement request, selecting an existing measurement model, which has the highest model measurement precision priority, as a first measurement model to perform measurement; and on the basis of communication data of a wireless indicator to be measured, training a measurement model, which has a model measurement precision priority higher than that of the selected first measurement model, so as to measure a subsequent measurement request of the same type.

Description

Wireless indicator detection method, storage medium and electronic device

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on Chinese patent application CN202311284210.3, filed on September 28, 2023, entitled “Wireless indicator detection method, storage medium and electronic device”, and claims the priority of the patent application, and all the disclosed contents thereof are incorporated into this application by reference. Technical Field

[0003] The embodiments of the present disclosure relate to the field of communications, and in particular, to a wireless indicator detection method, a storage medium, and an electronic device. Background Art

[0004] In recent years, as operations and maintenance systems have continued to evolve towards intelligentization, artificial intelligence (AI) algorithms have increasingly been embedded in operators' production systems. Wireless operations and maintenance scenarios include indicator prediction, anomaly warnings, root cause analysis, and anomaly detection. The current common approach for wireless operations and maintenance detection algorithms is to first train a relevant wireless operations and maintenance model, predict key performance indicators (KPIs) for the next N days, then use supervised or unsupervised methods to test the predicted values. Based on the test results, indicator predictions or anomaly warnings are performed. Root cause analysis or anomaly location can also be performed based on the relevant wireless operations and maintenance model combined with actual data.

[0005] The bottleneck of these wireless O&M methods is the need to pre-train models to ensure timely response during inference and meet the real-time requirements of production systems. However, both pre-training a general model and training individual models for each metric or object present challenges: the number of metrics in the wireless O&M field can reach hundreds, and the number of objects under different dimensional combinations increases exponentially. Pre-training and maintaining a model for each metric or object is inherently labor-intensive. Furthermore, new objects in the system require pre-training models before use, resulting in poor scalability. While training a single general model can address the need for training a large number of models, its accuracy decreases significantly. The greater the number of metrics and objects, the worse the model performance. Using large models with numerous parameters to improve accuracy increases the complexity of model design and computing resource consumption, while also increasing the difficulty of inference and making it difficult for the system to return timely inference results.

[0006] In summary, the detection model of wireless operation and maintenance in the related art has the problems of large training volume and low accuracy.

[0007] Summary of the Invention

[0008] According to an embodiment of the present disclosure, a wireless indicator detection method is provided, including: selecting an existing detection model with the highest model detection accuracy priority as a first detection model for detection based on a detection request; and training a detection model with a model detection accuracy priority higher than that of the first detection model based on communication data of the wireless indicator to be detected, so as to detect the next detection request of the same type.

[0009] According to another embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0010] According to another embodiment of the present disclosure, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG1 is a hardware structure block diagram of a computer terminal of a wireless indicator detection method according to an embodiment of the present disclosure;

[0012] FIG2 is a schematic diagram of a wireless indicator detection operation network architecture according to an embodiment of the present disclosure;

[0013] FIG3 is a flow chart of a wireless indicator detection method according to an embodiment of the present disclosure;

[0014] FIG4 is a structural block diagram of a wireless indicator detection device according to an embodiment of the present disclosure;

[0015] FIG5 is a schematic diagram of a model reasoning process according to an embodiment of the present disclosure;

[0016] FIG6 is a schematic diagram of a detection algorithm reasoning process according to an embodiment of the present disclosure;

[0017] FIG7 is a schematic diagram of a model training process according to an embodiment of the present disclosure;

[0018] FIG8 is a schematic diagram of a detection algorithm training process according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0020] It should be noted that the terms "first", "second", etc. in the description and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0021] Related art wireless operations and maintenance often struggle to balance computational resource conservation, accuracy, and timely system response when processing detection models. This is due to model characteristics: the better a model fits a particular type of data, the less general it tends to be. Academic research typically aims to improve model accuracy while maintaining general applicability. However, such models are often complex and resource-intensive, making them unsuitable for industrial production. Therefore, related art either pursues accuracy by heavily subdividing the model, which consumes resources and compromises scalability to new data types. Alternatively, they pursue general applicability by using complex models, which increases inference resource consumption and prevents timely system response. Both approaches suffer from usability drawbacks due to large training volumes and low accuracy. To address this issue, embodiments of the present application divide model detection into three tiers based on accuracy: indicator model, dimensional model, and object model. The model detection accuracy of the indicator model, dimensional model, and object model is prioritized from low to high. In this embodiment, a higher priority for model detection accuracy indicates more accurate detection results, but lower scalability to new data types. In the actual implementation process, indicators refer to key data used to measure network quality and business busyness during wireless communication, such as connection rate, call drop rate, number of users, etc. Dimensions are used to limit the statistical scope of indicators. Common ones include provinces, cities, communities, etc., and can also be customized user groups. There can be multiple dimensions. An object is an instance pointed to by the true value after the combination of dimensions and indicators. For example, if the dimensions are province plus city, and the indicator is connection rate, then the connection rate of Nanjing, Jiangsu Province is an actual object. It should be noted that the embodiment of the present application only divides the detection model into three levels by example. In the actual implementation process, the number of levels of model division can be determined according to actual conditions, and no specific restrictions are made here.

[0022] The embodiments of the present application are aimed at scenarios such as indicator prediction, anomaly warning, root cause analysis (such as fault classification, fault scenario analysis, etc.), and anomaly detection (such as anomaly location, locating the specific root cause of the anomaly through root cause analysis after the anomaly is discovered) in the field of wireless operation and maintenance. A wireless indicator detection method is proposed. The operating conditions of the wireless communication system and whether there are abnormal status indicators can be predicted in advance through indicator prediction and anomaly warning, and root cause analysis and anomaly detection can be performed in combination with actual data.

[0023] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a computer terminal as an example, FIG1 is a hardware structure block diagram of a computer terminal of a wireless indicator detection method according to an embodiment of the present disclosure. As shown in FIG1 , the computer terminal may include one or more (only one is shown in FIG1 ) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that the structure shown in FIG1 is only for illustration and does not limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than those shown in FIG1 , or have a configuration different from that shown in FIG1 .

[0024] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the wireless indicator detection method in the embodiment of the present disclosure. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. 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 located relative to the processor 102, and these remote memories may be connected to the mobile terminal 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.

[0025] The transmission device 106 is used to receive or transmit data via a network. Examples of such networks may include wireless networks provided by a computer terminal's communications provider. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0026] Figure 2 is a schematic diagram of the network architecture for wireless indicator detection operation according to an embodiment of the present disclosure. The embodiment of the present application can run on the network architecture shown in Figure 2. As shown in Figure 2, the network architecture includes: a data management module, a data source, a model management module, a task management module, and a front-end management page. The data management module is used to request data from the database for page display and push to the model module for early warning model training and reasoning. The model management module is used to manage the three-layer model and perform training and reasoning tasks. The task management module is used to receive front-end requests, query data from the data module and push it to the model module for processing, as well as manage scheduled tasks. The front-end management page is used to interact with users. Users select the dimensions and indicators that need to be inferred through the front-end page. In addition, users can also set scheduled tasks to continuously update the early warning model.

[0027] In this embodiment, a wireless indicator detection method running on the above-mentioned computer terminal or network architecture is provided. FIG3 is a flow chart of the wireless indicator detection method according to an embodiment of the present disclosure. As shown in FIG3 , the flow chart includes the following steps:

[0028] Step S302: selecting the detection model with the highest detection accuracy priority among the existing models according to the detection request as the first detection model for detection;

[0029] In an exemplary embodiment, the hierarchical type of the detection model includes at least one of the following: an indicator model, a dimensional model, and an object model; wherein the model detection accuracy priorities of the indicator model, the dimensional model, and the object model are sorted from low to high.

[0030] As mentioned above, the embodiment of the present application is divided into three levels according to the model detection accuracy, namely the indicator model, the dimension model, and the object model. The model detection accuracy priorities of the indicator model, the dimension model, and the object model are sorted from low to high. In the embodiment of the present application, the higher the priority of the model detection accuracy, the more accurate the detection result, but the lower the scalability to new types of data. In the actual implementation process, the number of model layers can be increased or decreased, and is not limited to the three layers of indicators, dimensions, and objects. There can be only indicator and dimension layers or indicator and object layers.

[0031] In an exemplary embodiment, the detection model with the highest priority in model detection accuracy among the existing detection models is selected as the first detection model for detection based on the detection request, including: based on the area information and indicator information contained in the detection request, selecting the detection model with the highest priority in model detection accuracy among the existing detection models as the first detection model for detection.

[0032] In actual implementation, as described above, the first detection model is one of all existing detection models; the term "first" is used for distinction only. In actual implementation, prior to testing, several existing detection models, namely, existing detection models, are already present. Based on the user's input detection request, the detection model with the highest detection accuracy priority that matches the detection request is selected for testing.

[0033] In an exemplary embodiment, a detection model with the highest priority in model detection accuracy is selected as the first detection model, including: selecting an object model based on indicator information and region information, and if the object model is successfully selected, using the object model as the first detection model for detection; if the object model fails to be selected, selecting a dimension model based on region information, and if the dimension model is successfully selected, using the dimension model as the first detection model for detection; if the dimension model fails to be selected, selecting an indicator model based on indicator information, and using the indicator model as the first detection model for detection.

[0034] In actual implementation, when selecting a detection model based on the region and indicator information included in the detection request, the object model is selected first according to the model detection accuracy priority order. The object model is selected based on the region and indicator information, and the corresponding object model needs to be selected based on the region and indicator information. If object model selection fails, a matching dimensional model is selected based on the priority order. The dimensional model is trained based on the region information and needs to be selected based on the region information. If dimensional model selection fails, the indicator model is selected based on the priority order. The indicator model is trained based on the indicator information and needs to be selected based on the indicator information.

[0035] In actual implementation, indicators refer to key data used to measure network quality and service load during wireless communications, such as connection rate, call drop rate, and number of users. Dimensions, on the other hand, limit the statistical scope of indicators. Common examples include province, city, and community, but they can also be customized user groups. Multiple dimensions are possible. An object is an instance to which the actual value of the dimension and indicator combination refers. For example, if the dimensions are province and city, and the indicator is connection rate, then the connection rate of Nanjing, Jiangsu Province, is an actual object.

[0036] In an exemplary embodiment, the detection model with the highest priority in existing model detection accuracy is selected as the first detection model for detection, including: using the first detection model to perform indicator prediction on the wireless indicator corresponding to the detection request to obtain indicator prediction data; using the first detection model to perform early warning detection on the indicator prediction data to obtain early warning detection results.

[0037] In the embodiment of the present disclosure, after the model detection accuracy in the above steps is obtained and the warning detection result is obtained, an abnormal warning can be performed. In fact, it can also be considered that the warning detection of the indicator prediction data is the abnormal warning of the indicator prediction data.

[0038] In actual implementation, the detection model can be divided into a prediction model and an early warning model. The prediction model is used to predict wireless indicators, while the early warning model is used to detect early warnings based on multiple predictions and issue anomaly warnings. In actual implementation, the detection model can also have both prediction and early warning functions, that is, it is not divided into two models. This is not specifically limited here. In actual implementation, the detection model provided by the embodiments of the present disclosure can also perform root cause analysis or anomaly detection by testing actual data. This will not be discussed in detail here.

[0039] Step S304 : Based on the communication data of the wireless indicator to be detected, a detection model having a higher detection accuracy priority than the first detection model is trained to detect the next detection request of the same type.

[0040] In an exemplary embodiment, a detection model having a higher model detection accuracy priority than the first detection model is trained based on the communication data of the wireless indicator to be detected, including: when the first detection model is an indicator model, the dimensional model and the object model are trained based on the communication data of the wireless indicator to be detected; when the first detection model is a dimensional model, the object model is trained based on the communication data of the wireless indicator to be detected.

[0041] In the actual implementation process, when the first detection model is the indicator model, it means that when selecting from the existing detection models according to the order of model detection accuracy priority, the indicator model was selected, which means that the dimensional model and the object model with a higher model detection accuracy priority than the indicator model are not already available and do not exist. Therefore, it is necessary to train the dimensional model and the object model based on the communication data of the wireless indicator to be detected, in preparation for the next detection request to be tested. When the first detection model is the dimensional model, it means that when selecting from the existing detection models according to the order of model detection accuracy priority, the dimensional model was selected, which means that the object model with a higher model detection accuracy priority than the dimensional model does not exist. It is necessary to train the object model based on the communication data of the wireless indicator to be detected, in preparation for the next detection request to be tested.

[0042] In an exemplary embodiment, the wireless indicator detection method further includes: training and updating the first detection model according to the communication data of the wireless indicator to be detected.

[0043] During the actual implementation process, the existing model is updated and trained to ensure the accuracy of subsequent detection.

[0044] In an exemplary embodiment, before selecting the existing detection model with the highest detection accuracy priority as the first detection model for detection, the method further includes: acquiring communication data of the wireless indicator to be detected according to the detection request.

[0045] Through the above steps, a wireless indicator detection method is provided, which selects the detection model with the highest priority of model detection accuracy as the first detection model for detection according to the detection request; and trains the detection model with a higher priority of model detection accuracy than the first detection model selected according to the communication data of the wireless indicator to be detected, so as to detect the next detection request of the same type. This can solve the problem of large training volume and low accuracy of the detection model in the wireless indicator detection in the related art, thereby achieving the effect of reducing the training volume of the detection model and improving the accuracy of wireless indicator detection.

[0046] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the embodiment of the present disclosure is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD-ROM), including a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the method described in the embodiment of the present disclosure.

[0047] In this embodiment, a wireless indicator detection device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments. The details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0048] FIG4 is a block diagram of a wireless indicator detection device according to an embodiment of the present disclosure. As shown in FIG4 , the detection device 40 includes: a detection selection module 410 configured to select, based on a detection request, an existing detection model with the highest detection accuracy priority as a first detection type for detection. A detection training module 420 configured to train, based on communication data of the wireless indicator to be detected, a detection model with a higher detection accuracy priority than the first detection model, so as to detect the next detection request of the same type.

[0049] In actual implementation, the various modules in the detection device 40 can also be divided into different functional units to implement the various steps in the wireless indicator detection method in the above embodiment. The specific division and naming of the modules in the detection device 40 are not specifically limited, as long as the various steps in the wireless indicator detection method can be implemented. The specific division of the modules in the detection device 40 is the same as the overall function of the modules in the wireless indicator detection operation network architecture in the above embodiment, but the functional division and naming of each individual module can be different, which is not specifically limited here.

[0050] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0051] An embodiment of the present disclosure further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0052] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0053] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0054] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0055] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0056] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present disclosure can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented using program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Thus, the embodiments of the present disclosure are not limited to any specific combination of hardware and software.

[0057] In order to enable those skilled in the art to better understand the technical solutions of the embodiments of the present disclosure, they are described below in conjunction with different embodiments.

[0058] Example 1

[0059] In the embodiments of the present disclosure, the technical solutions provided by the embodiments of the present disclosure are illustrated with examples for indicator prediction and abnormality warning in wireless operation and maintenance detection.

[0060] The core function of the wireless indicator detection device provided by the embodiment of the present disclosure is to divide the detection model into three-layer models: indicators, dimensions, and objects. The device may initially only provide a model for the indicator layer. When using the device, the user selects indicators under different dimensional combinations for detection. The first appearing dimensional combination will use the indicator model for inference to give a result, ensuring that the operation and maintenance device responds in a timely manner. While inferring, the data under the dimensional combination will be used to train the corresponding dimensional model and object model. When the indicators under the same dimensional combination are used for reasoning again, the object model that can be matched will be used for reasoning first. For data that does not have a corresponding object model, the dimensional model is used for reasoning, and the data is used to train a new object model. Through the above scheme, the investment in computing resources can be reduced while basically maintaining the accuracy of detection, thereby improving the user experience.

[0061] In the embodiments of the present disclosure, the detection model mentioned in the above embodiments includes a prediction model and an early warning model, wherein the prediction model is used to predict wireless indicators, and the early warning model is used to warn of abnormalities in wireless indicators.

[0062] The process steps of the embodiment of the present disclosure are divided into two parts: abnormal warning model reasoning and abnormal warning model training. Both can be triggered by the user and performed simultaneously.

[0063] FIG5 is a schematic diagram of a model reasoning process according to an embodiment of the present disclosure. As shown in FIG5 , the model reasoning business process includes:

[0064] 1. First, the user selects the dimension and indicator combination for inference on the front-end page.

[0065] 2. The front end sends an inference request to the task management module.

[0066] 3. The task management module requests query data from the data management module and passes it to the model management module.

[0067] 4. After receiving the data, the model management module detects whether the object model exists. If so, it uses the object model for reasoning.

[0068] 5. If the object model does not exist, continue to check whether the dimensional model exists.

[0069] 6. If a dimensional model exists, use the dimensional model for reasoning; if not, use the indicator model for reasoning.

[0070] 7. The inference results are returned to the front-end for display.

[0071] Figure 6 is a schematic diagram of the detection algorithm reasoning process according to an embodiment of the present disclosure. As shown in Figure 6, the prediction model is first used to predict data for the next n days. The early warning model is then used to detect whether there are any anomalies in the predicted data. If there are anomalies, an early warning result is output. The prediction model and early warning model used in the process can be replaced with various prediction algorithms and early warning algorithms.

[0072] FIG7 is a schematic diagram of a model training process according to an embodiment of the present disclosure, as shown in FIG7 , including:

[0073] 1. First, the user selects the dimension and indicator combination for inference on the front-end page.

[0074] 2. The front end sends an inference request to the task management module.

[0075] 3. The task management module requests query data from the data management module and passes it to the model management module.

[0076] 4. After receiving the data, the model management module detects whether the corresponding models of each layer exist.

[0077] 5. If the model exists, update the old model.

[0078] 6. If the model does not exist, train a new model.

[0079] Figure 8 is a schematic diagram of the detection algorithm training process according to an embodiment of the present disclosure. As shown in Figure 8, in this embodiment of the present disclosure, the prediction model can be trained first, and then the early warning model can be trained. The prediction model and early warning model used in the process can be replaced by various prediction algorithms and early warning algorithms.

[0080] Example 2

[0081] The process for issuing an abnormal warning for the number of VoLTE users in a newly deployed system is as follows:

[0082] 1. Users select dimensions and indicators on the front-end page to query data.

[0083] 2. Select the abnormal warning function for reasoning.

[0084] At this point, the system is in a brand new state, with only the indicator model for the number of VoLTE users. According to the process of the embodiment of the present disclosure, the indicator model will be selected for inference at this point.

[0085] 3. Backstage synchronous training model.

[0086] When using the indicator model of the number of VoLTE users for reasoning, the background will simultaneously train new dimensional models and object models, and update the existing indicator models. The dimensional model at this time is a city dimension model, that is, all VoLTE user number indicators with the city dimension can use this model for reasoning. The object model generated by training is in the city dimension, which can be 7 different models from Xuzhou to Zhenjiang (for example only, without any specific limitation on the solution of the embodiment of the present disclosure). To use this model, the corresponding object model can be used only when the indicator is the number of VoLTE users, the dimension is the city, and the dimension value is Zhenjiang (one of the 7 cities).

[0087] 4. The inference results are returned to the front-end for display.

[0088] Example 3

[0089] Based on the training results of Example 2, in Example 3, a new object is selected for abnormal warning.

[0090] 1. Select dimensions and indicators to query data.

[0091] In the embodiment of the present disclosure, the selected dimension and indicator is the number of VoLTE users in Nanjing.

[0092] 2. Select the abnormal warning function for reasoning.

[0093] At this time, since the city-dimensional VoLTE user number model has been trained in Example 2, but there is no object-dimensional model for Nanjing, the former dimension model is preferentially selected for inference.

[0094] 3. Backstage synchronous training model.

[0095] When using the VoLTE user number indicator model for inference, the background will simultaneously train a new object model and update the existing indicator model and dimension model.

[0096] 4. The inference results are returned to the front-end for display.

[0097] In actual implementation, when the dimensions are fixed or not relevant, such as in large-scale screen maintenance scenarios, the dimensional model can usually be deleted because no new dimensions will appear. Alternatively, when focusing on overall metrics rather than specific objects, the object model can be deleted, retaining only the metrics and dimensions layers.

[0098] It should be noted that in the embodiments of the present disclosure (including scenario embodiments one, two, and three), only the steps of indicator prediction and abnormal warning are described. In the actual implementation process, the wireless indicator detection device provided by the embodiment of the present disclosure is used to execute the wireless indicator detection method in the above embodiment. In addition to being applicable to indicator prediction and abnormal warning in the field of wireless operation and maintenance, it can also be applicable to scenarios of root cause analysis and abnormality detection.

[0099] The embodiments of the present disclosure relate to a method for detecting wireless indicators based on a hierarchical model, and in particular to network operation and maintenance in the field of communications. Network operation and maintenance usually involves monitoring and analyzing various quantitative indicators generated during the communication process to evaluate network conditions and locate network problems. Abnormal warning uses algorithms to identify in advance the possibility of abnormalities occurring in a unit in the operation and maintenance system to avoid problems. Current network operation and maintenance systems are developing in the direction of intelligent operation and maintenance. The embodiments of the present disclosure aim to provide a wireless indicator detection device based on model layering to improve the intelligence and availability of the operation and maintenance system. The embodiments of the present disclosure propose a wireless indicator detection technology based on model layering, first providing a batch of models with strong versatility and poor accuracy to meet the timeliness of system response, and then training new models with poor versatility but high accuracy during use, and only training the models that need to be used to avoid waste of resources.

[0100] The technical solution of the embodiment of the present disclosure can be applied to the operation and maintenance system of wireless indicators under 4 / 5G communication networks, and is oriented to the indicator prediction, anomaly warning, root cause analysis, and anomaly detection scenarios of wireless indicators. Communication operators all have their own big data operation and maintenance systems, and they also have the need to develop in the direction of intelligent operation and maintenance. The embodiment of the present disclosure only needs simple adaptation to be embedded in various operation and maintenance systems, and can also be redeveloped using the solution of the embodiment of the present disclosure. The detection algorithm in the embodiment of the present disclosure can be replaced with various prediction and analysis algorithms, etc. The indicator detection method of the embodiment of the present disclosure that first predicts the indicator and then warns of anomalies, the prediction and detection steps can also be replaced with various prediction and detection algorithms.

[0101] The above description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will appreciate that various modifications and variations of the present disclosure are possible. Any modifications, equivalent substitutions, or improvements made within the principles of the present disclosure should be included within the scope of protection of the present disclosure.

Claims

1. A wireless indicator detection method, comprising: According to the detection request, a detection model with the highest detection accuracy priority among the existing models is selected as the first detection model for detection; According to the communication data of the wireless indicator to be detected, a detection model with a higher model detection accuracy priority than the first detection model is trained to detect the next detection request of the same type.

2. The method according to claim 1, wherein: The hierarchical type of the detection model includes at least one of the following: Indicator model, dimensional model, object model; wherein the model detection accuracy priorities of the indicator model, the dimensional model, and the object model are arranged in descending order.

3. The method according to claim 2, wherein: The step of selecting, according to the detection request, a detection model with the highest detection accuracy priority of an existing model as a first detection model for detection includes: According to the area information and indicator information included in the detection request, the detection model with the highest model detection accuracy priority is selected from the existing detection models as the first detection model for detection.

4. The method according to claim 3, wherein: The step of selecting the detection model with the highest model detection accuracy priority as the first detection model includes: selecting the object model according to the indicator information and the area information, and if the object model is successfully selected, using the object model as the first detection model for detection; In the case where the object model fails to be selected, the dimensional model is selected according to the region information, and in the case where the dimensional model is successfully selected, the dimensional model is used as the first detection model for detection; In the case where the dimensional model fails to be selected, the indicator model is selected according to the indicator information, and the indicator model is used as the first detection model for detection.

5. The method according to claim 1, wherein: The step of selecting the detection model with the highest detection accuracy priority of the existing models as the first detection model for detection includes: Using the first detection model to perform indicator prediction on the wireless indicator corresponding to the detection request to obtain indicator prediction data; The first detection model is used to perform early warning detection on the indicator prediction data to obtain an early warning detection result.

6. The method according to claim 2, wherein: The step of training, according to the communication data of the wireless indicator to be detected, a detection model having a higher priority in model detection accuracy than the first detection model, comprises: In the case where the first detection model is the indicator model, training the dimensional model and the object model according to the communication data of the wireless indicator to be detected; In the case where the first detection model is the dimensional model, the object model is trained according to the communication data of the wireless indicator to be detected.

7. The method according to claim 1, wherein: Also includes: The first detection model is trained and updated according to the communication data of the wireless indicator to be detected.

8. The method according to claim 1, wherein: Before selecting the detection model with the highest detection accuracy priority of the existing models as the first detection model for detection, the method further includes: The communication data of the wireless indicator to be detected is obtained according to the detection request.

9. A computer-readable storage medium having a computer program stored therein, wherein: When the computer program is executed by a processor, the method described in any one of claims 1 to 8 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

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