A communication quality monitoring and early warning method and system based on big data

Abstract

This invention discloses a communication quality monitoring and early warning method and system based on big data, comprising: acquiring historical signal data and preprocessing the acquired historical signal data; acquiring the communication cycle time nodes of the historical signal data, and dividing the preprocessed historical signal data into multiple periodic signal datasets based on the communication cycle time nodes of the network data; calculating each preset index data in each periodic signal dataset, and determining the communication quality value of each periodic signal dataset based on each preset index data; inputting the communication quality values ​​of each periodic signal dataset into a neural network model for training and optimization to obtain a communication quality value prediction model; predicting the latest signal data using the communication quality value prediction model to obtain a predicted communication quality value; comparing the predicted communication quality value with the preset pre-communication quality value to determine whether to issue an early warning. This invention can improve the accuracy and efficiency of communication quality monitoring.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a communication quality monitoring and early warning method and system based on big data. Background Art

[0002] At present, with the rapid development of network technology, network communication has gradually become an indispensable part of people's lives, and more and more people are accustomed to using the network for communication. In the process of communication, the quality of communication determines the timeliness and effectiveness of people's communication. Through comprehensive monitoring of communication quality, it is possible to timely discover faults in the communication process, timely warn of communication faults, and take timely disposal measures to solve communication faults, so that the communication system can operate benignly.

[0003] However, the amount of communication data generated by the communication network is huge and complex, and it is difficult for existing technologies to efficiently process and analyze it, which will lead to inefficient data processing and make it impossible to timely discover and solve faults that occur during the communication process. In addition, in some existing technologies, only a single communication data parameter is used to study and monitor the communication quality, which lacks a comprehensive understanding of communication quality issues and makes it impossible to make effective decisions and subsequent optimization strategies. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a communication quality monitoring and early warning method based on big data, comprising:

[0005] Acquiring historical signal data, and preprocessing the acquired historical signal data;

[0006] Acquire the communication cycle time nodes of the historical signal data, and divide the pre-processed historical signal data into a plurality of periodic signal data sets based on the communication cycle time nodes of the network data;

[0007] Calculating each preset indicator data in each of the periodic signal data sets, and determining the communication quality value of each of the periodic signal data sets according to each preset indicator data;

[0008] Inputting the communication quality values ​​of each of the periodic signal data sets into a neural network model for training and optimization to obtain a communication quality value prediction model;

[0009] Predicting the latest signal data using the communication quality value prediction model to obtain a predicted communication quality value;

[0010] The predicted communication quality value is compared with a preset communication quality value to determine whether to issue an early warning prompt.

[0011] Furthermore, the preprocessing method for the acquired historical signal data includes at least one of data cleaning, correlation analysis, and normalization processing.

[0012] Furthermore, each preset indicator data includes signal strength, signal-to-noise ratio, bit error rate, packet loss rate and delay, but is not limited to the above preset indicator data.

[0013] Further, the calculating of each preset indicator data in each of the periodic signal data sets, and determining the communication quality value of each of the periodic signal data sets according to each preset indicator data, includes:

[0014] The communication quality value of each periodic signal data set is determined according to each preset indicator data and the weight corresponding to each preset indicator data.

[0015] Furthermore, the calculation formula for the communication quality value of each periodic signal data set is as follows:

[0016]

[0017] Among them, A is the communication quality value of each periodic signal data set, a is the weight corresponding to a preset indicator data of each periodic signal data set, S is a preset indicator data of each periodic signal data set, and n is the number of preset indicator data in each periodic signal data set.

[0018] Furthermore, the communication quality values ​​of each of the periodic signal data sets are input into a neural network model for training and optimization to obtain a communication quality value prediction model, including:

[0019] Dividing the communication quality value data of each of the periodic signal data sets into a training set, a validation set and a test set;

[0020] The initial prediction model is trained by inputting the training set into the neural network model, and the neural network model is optimized by hyperparameter tuning;

[0021] The optimized initial prediction model is verified and tested by inputting the verification set and the test set into the neural network model until the neural network model meets the preset convergence conditions;

[0022] The verified and tested neural network model is deployed to obtain a communication quality value prediction model.

[0023] Further, comparing the predicted communication quality value with a preset communication quality value to determine whether to issue an early warning prompt includes:

[0024] Calculating an average value of the communication quality values ​​of each periodic signal data set, and determining the average value as a preset pre-communication quality value;

[0025] comparing the predicted communication quality value with a preset pre-communication quality value;

[0026] If the predicted communication quality value exceeds the preset communication quality value, it is determined that the current network communication quality is good and no early warning prompt is required;

[0027] If the predicted communication quality value does not exceed the preset communication quality value, it is determined that the current network communication quality is poor and an early warning prompt is required.

[0028] The present invention provides a communication quality monitoring and early warning device based on big data, comprising:

[0029] An acquisition module, used for acquiring historical signal data and preprocessing the acquired historical signal data;

[0030] A division module, used for obtaining the communication cycle time nodes of the historical signal data, and dividing the pre-processed historical signal data into a plurality of periodic signal data sets based on the communication cycle time nodes of the network data;

[0031] A calculation module, used to calculate each preset indicator data in each of the periodic signal data sets, and determine the communication quality value of each of the periodic signal data sets according to each preset indicator data;

[0032] A prediction module, used for inputting the communication quality value of each periodic signal data set into a neural network model for training and optimization to obtain a communication quality value prediction model;

[0033] Predicting the latest signal data using the communication quality value prediction model to obtain a predicted communication quality value;

[0034] The early warning module is used to compare the predicted communication quality value with the preset communication quality value to determine whether to issue an early warning prompt.

[0035] Compared with the prior art, the communication quality monitoring and early warning method and system based on big data in the embodiment of the present invention has the following beneficial effects:

[0036] By adopting big data analysis technology, the present invention can efficiently process and analyze the huge and complex communication data generated by the communication network, so that faults occurring in the communication process can be discovered in time, early warning prompts can be given in time, and the efficiency of communication quality monitoring can be improved. Moreover, by comprehensively monitoring the indicators in the communication data, the problems of communication quality can be fully understood, so as to make effective decisions and optimize strategies in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1It is a flow chart of a communication quality monitoring and early warning method based on big data in an embodiment of the present invention;

[0038] Figure 2 It is a schematic diagram of the composition of a communication quality monitoring and early warning device based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0040] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0041] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0042] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0043] like Figure 1As shown, in an embodiment of the present application, a communication quality monitoring and early warning method based on big data is provided, including: step S100: obtaining historical signal data, and preprocessing the acquired historical signal data; step S200: obtaining the communication cycle time node of the historical signal data, and dividing the preprocessed historical signal data into multiple periodic signal data sets based on the communication cycle time node of the network data; step S300: calculating each preset indicator data in each periodic signal data set, and determining the communication quality value of each periodic signal data set according to each preset indicator data; step S400: inputting the communication quality value of each periodic signal data set into a neural network model for training and optimization to obtain a communication quality value prediction model; step S500: predicting the latest signal data through the communication quality value prediction model to obtain a predicted communication quality value; step S600: comparing the predicted communication quality value with the preset pre-communication quality value to determine whether to issue an early warning prompt.

[0044] Furthermore, the present invention, by adopting big data analysis technology, can efficiently process and analyze the huge and complex communication data generated by the communication network, so that faults occurring in the communication process can be discovered in time, early warning prompts can be issued in time, and the efficiency of communication quality monitoring can be improved. Moreover, by comprehensively monitoring the indicators in the communication data, the problems with the communication quality can be fully understood, so as to make effective decisions and optimize the strategies in the future.

[0045] In an embodiment of the present application, a communication quality monitoring and early warning method based on big data is provided, and the preprocessing method of the acquired historical signal data includes at least one of data cleaning, correlation analysis, and normalization processing.

[0046] In an embodiment of the present application, a communication quality monitoring and early warning method based on big data is provided, and each preset indicator data includes signal strength, signal-to-noise ratio, bit error rate, packet loss rate, and delay, but is not limited to the above preset indicator data.

[0047] In an embodiment of the present application, a communication quality monitoring and early warning method based on big data is provided, wherein each preset indicator data in each periodic signal data set is calculated, and the communication quality value of each periodic signal data set is determined according to each preset indicator data, including: determining the communication quality value of each periodic signal data set according to each preset indicator data and the weight corresponding to each preset indicator data.

[0048] In an embodiment of the present application, a communication quality monitoring and early warning method based on big data is provided, and the calculation formula of the communication quality value of each periodic signal data set is as follows:

[0049]

[0050] Among them, A is the communication quality value of each periodic signal data set, a is the weight corresponding to a preset indicator data of each periodic signal data set, S is a preset indicator data of each periodic signal data set, and n is the number of preset indicator data in each periodic signal data set.

[0051] In an embodiment of the present application, a communication quality monitoring and early warning method based on big data is provided, wherein the communication quality values ​​of each of the periodic signal data sets are input into a neural network model for training and optimization to obtain a communication quality value prediction model, including: dividing the communication quality value data of each of the periodic signal data sets into a training set, a validation set, and a test set; training the initial prediction model by inputting the training set into the neural network model, and further optimizing the neural network model by hyperparameter tuning; verifying and testing the optimized initial prediction model by inputting the validation set and the test set into the neural network model until the neural network model meets the preset convergence conditions; and deploying the verified and tested neural network model to obtain a communication quality value prediction model.

[0052] Specifically, the communication quality value prediction model is a neural network model constructed according to different machine learning algorithms. The prediction accuracy can be improved by screening data through different machine learning algorithms. Specifically, the machine learning algorithms corresponding to the communication quality value prediction model include: support vector machine (SVM algorithm), K nearest neighbor algorithm (K-Nearest Neighbor, KNN algorithm), K mean algorithm (K-Means algorithm), random forest algorithm, etc.

[0053] In an embodiment of the present application, a communication quality monitoring and early warning method based on big data is provided, wherein the predicted communication quality value is compared with a preset pre-communication quality value to determine whether to issue an early warning prompt, including: calculating the average value of the communication quality values ​​of each periodic signal data set, and determining the average value as the preset pre-communication quality value; comparing the predicted communication quality value with the preset pre-communication quality value; if the predicted communication quality value exceeds the preset pre-communication quality value, it is determined that the current network communication quality is good and no early warning prompt is required; if the predicted communication quality value does not exceed the preset pre-communication quality value, it is determined that the current network communication quality is poor and an early warning prompt is required.

[0054] like Figure 2As shown, in an embodiment of the present application, a communication quality monitoring and early warning device based on big data is provided, including: an acquisition module, used to acquire historical signal data and preprocess the acquired historical signal data; a division module, used to acquire the communication cycle time node of the historical signal data, and divide the preprocessed historical signal data into multiple periodic signal data sets based on the communication cycle time node of the network data; a calculation module, used to calculate each preset indicator data in each periodic signal data set, and determine the communication quality value of each periodic signal data set according to each preset indicator data; a prediction module, used to input the communication quality value of each periodic signal data set into a neural network model for training and optimization, and obtain a communication quality value prediction model; predict the latest signal data through the communication quality value prediction model to obtain a predicted communication quality value; an early warning module, used to compare the predicted communication quality value with the preset predicted communication quality value, and determine whether to issue an early warning prompt.

[0055] Preferably, each module in the above-mentioned communication quality monitoring and early warning device based on big data can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. Among them, the hardware can be through a computer device, which can be a terminal. The computer device communicates with an external terminal in a wired or wireless manner, and the wireless method can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies.

[0056] In summary, the embodiment of the present invention provides a communication quality monitoring and early warning method based on big data, which includes: obtaining historical signal data and preprocessing the obtained historical signal data; obtaining the communication cycle time node of the historical signal data, and dividing the preprocessed historical signal data into multiple periodic signal data sets based on the communication cycle time node of the network data; calculating each preset indicator data in each periodic signal data set, and determining the communication quality value of each periodic signal data set according to each preset indicator data; inputting the communication quality value of each periodic signal data set into the neural network model for training and optimization, and obtaining a communication quality value prediction model; predicting the latest signal data through the communication quality value prediction model to obtain a predicted communication quality value; comparing the predicted communication quality value with the preset pre-communication quality value to determine whether to issue an early warning prompt. The present invention can efficiently process and analyze the huge and complex communication data generated by the communication network by adopting big data analysis technology, so that it can timely discover the faults occurring in the communication process, and timely issue early warning prompts, thereby improving the efficiency of communication quality monitoring, and by comprehensively monitoring the indicators in the communication data, it can fully understand the problem of communication quality, so as to make effective decisions and optimize strategies in the future.

[0057] Finally, it should be noted that it is obvious that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0058] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0059] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0060] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0061] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A communication quality monitoring and early warning method based on big data, characterized in that: include: Acquiring historical signal data, and preprocessing the acquired historical signal data; Acquire the communication cycle time nodes of the historical signal data, and divide the pre-processed historical signal data into a plurality of periodic signal data sets based on the communication cycle time nodes of the network data; Calculating each preset indicator data in each of the periodic signal data sets, and determining the communication quality value of each of the periodic signal data sets according to each preset indicator data; Inputting the communication quality values ​​of each of the periodic signal data sets into a neural network model for training and optimization to obtain a communication quality value prediction model; Predicting the latest signal data using the communication quality value prediction model to obtain a predicted communication quality value; Compare the predicted communication quality value with the preset pre-communication quality value to determine whether to issue an early warning prompt; The step of calculating each preset indicator data in each periodic signal data set, and determining the communication quality value of each periodic signal data set according to each preset indicator data, includes: Determine the communication quality value of each periodic signal data set according to each preset indicator data and the weight corresponding to each preset indicator data; The calculation formula of the communication quality value of each periodic signal data set is as follows: Wherein, A is the communication quality value of each of the periodic signal data sets, ai is the weight corresponding to the i-th preset indicator data in each of the periodic signal data sets, Si is the i-th preset indicator data in each of the periodic signal data sets, and n is the number of n preset indicator data in each of the periodic signal data sets; The communication quality values ​​of each periodic signal data set are input into a neural network model for training and optimization to obtain a communication quality value prediction model, including: Dividing the communication quality value data of each of the periodic signal data sets into a training set, a validation set and a test set; The initial prediction model is trained by inputting the training set into the neural network model, and the neural network model is optimized by hyperparameter tuning; The optimized initial prediction model is verified and tested by inputting the verification set and the test set into the neural network model until the neural network model meets the preset convergence conditions; Deploy the verified and tested neural network model to obtain a communication quality value prediction model; The comparing the predicted communication quality value with the preset communication quality value to determine whether to issue an early warning prompt includes: Calculating an average value of the communication quality values ​​of each periodic signal data set, and determining the average value as a preset pre-communication quality value; comparing the predicted communication quality value with a preset pre-communication quality value; If the predicted communication quality value exceeds the preset communication quality value, it is determined that the current network communication quality is good and no early warning prompt is required; If the predicted communication quality value does not exceed the preset communication quality value, it is determined that the current network communication quality is poor and an early warning prompt is required.

2. According to the big data-based communication quality monitoring and early warning method of claim 1, it is characterized in that: The preprocessing method for the acquired historical signal data includes at least one of data cleaning, correlation analysis, and normalization processing.

3. The communication quality monitoring and early warning method based on big data according to claim 1 is characterized in that: The preset indicator data include signal strength, signal-to-noise ratio, bit error rate, packet loss rate and delay, but are not limited to the above preset indicator data.

4. A communication quality monitoring and early warning device based on big data, characterized in that: include: An acquisition module, used for acquiring historical signal data and preprocessing the acquired historical signal data; A division module, used for obtaining the communication cycle time nodes of the historical signal data, and dividing the pre-processed historical signal data into a plurality of periodic signal data sets based on the communication cycle time nodes of the network data; A calculation module, used to calculate each preset indicator data in each of the periodic signal data sets, and determine the communication quality value of each of the periodic signal data sets according to each preset indicator data; A prediction module, used for inputting the communication quality value of each periodic signal data set into a neural network model for training and optimization to obtain a communication quality value prediction model; Predicting the latest signal data using the communication quality value prediction model to obtain a predicted communication quality value; An early warning module, used to compare the predicted communication quality value with a preset pre-communication quality value to determine whether to issue an early warning prompt; The step of calculating each preset indicator data in each periodic signal data set, and determining the communication quality value of each periodic signal data set according to each preset indicator data, includes: Determine the communication quality value of each periodic signal data set according to each preset indicator data and the weight corresponding to each preset indicator data; The calculation formula of the communication quality value of each periodic signal data set is as follows: Wherein, A is the communication quality value of each of the periodic signal data sets, ai is the weight corresponding to the i-th preset indicator data in each of the periodic signal data sets, Si is the i-th preset indicator data in each of the periodic signal data sets, and n is the number of n preset indicator data in each of the periodic signal data sets; The communication quality values ​​of each periodic signal data set are input into a neural network model for training and optimization to obtain a communication quality value prediction model, including: Dividing the communication quality value data of each of the periodic signal data sets into a training set, a validation set and a test set; The initial prediction model is trained by inputting the training set into the neural network model, and the neural network model is optimized by hyperparameter tuning; The optimized initial prediction model is verified and tested by inputting the verification set and the test set into the neural network model until the neural network model meets the preset convergence conditions; Deploy the verified and tested neural network model to obtain a communication quality value prediction model; The comparing the predicted communication quality value with the preset communication quality value to determine whether to issue an early warning prompt includes: Calculating an average value of the communication quality values ​​of each periodic signal data set, and determining the average value as a preset pre-communication quality value; comparing the predicted communication quality value with a preset pre-communication quality value; If the predicted communication quality value exceeds the preset communication quality value, it is determined that the current network communication quality is good and no early warning prompt is required; If the predicted communication quality value does not exceed the preset communication quality value, it is determined that the current network communication quality is poor and an early warning prompt is required.

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