WLAN (Wireless Local Area Network) test evaluation method and device, equipment and storage medium
By building and using target prediction models to evaluate WLAN to be tested, the problem of difficulty in comprehensively evaluating WLAN card performance in the prior art is solved, and efficient and accurate WLAN test evaluation is achieved.
Patent Information
- Application Number
- CN202411698638.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-04-15
AI Technical Summary
It is difficult for the prior art to comprehensively evaluate the key performance indicators of electronic device WLAN cards, resulting in the impact of user experience and network performance.
By obtaining multiple sample data to form a sample data set, multiple initial models for predicting WLAN performance are constructed, the target prediction model is obtained based on the sample data set and the initial model, and finally the WLAN to be tested is evaluated based on the target prediction model.
It realizes automated testing of WLAN, improves testing efficiency and accuracy, reduces interference from human factors, and ensures consistency of testing standards.
Smart Images
Figure CN119835191A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of wireless local area networks, and in particular to a WLAN test and evaluation method, apparatus, device, and storage medium. Background Art
[0002] With the rapid development of science and technology, wireless local area network (WLAN) technology has become an indispensable part of modern life, especially in the field of mobile office and entertainment, where wireless network performance directly affects the user experience and efficiency. Therefore, comprehensive evaluation of the WLAN performance of electronic devices is of great significance to ensure user experience and network performance.
[0003] Currently, there are many brands of WLAN cards on the market with different standards and power consumption, which brings challenges to testing and evaluation. Therefore, a testing and evaluation method is needed that can comprehensively evaluate the key performance indicators of WLAN cards in electronic devices to ensure user experience and network performance. Summary of the invention
[0004] The present disclosure provides a WLAN test evaluation method, device, equipment and storage medium to at least solve the above technical problems existing in the prior art.
[0005] According to a first aspect of the present disclosure, a WLAN test evaluation method is provided, the method comprising:
[0006] Acquire multiple sample data to form a sample data set, where the sample data includes attribute information and performance parameters of the WLAN, where the performance parameters are used to characterize the configuration and performance quality of the WLAN;
[0007] Building multiple initial models for predicting WLAN performance;
[0008] Based on the sample data set and the multiple initial models, a target prediction model is obtained;
[0009] The WLAN to be tested is tested and evaluated based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested.
[0010] In one possible implementation manner, after acquiring the plurality of sample data, the method further comprises: preprocessing the plurality of sample data; the preprocessing comprises:
[0011] Data enhancement is performed on the performance parameters in the plurality of sample data.
[0012] In one embodiment, the method further comprises:
[0013] A label for each of the sample data is determined, where the label is used to indicate whether the sample data meets a preset condition.
[0014] In one possible implementation, obtaining a target prediction model based on the sample data set and the plurality of initial models includes:
[0015] Dividing the sample data set into a training set and a test set;
[0016] Based on the training set, the multiple initial models are trained respectively to obtain multiple prediction models corresponding to each initial model;
[0017] Based on the test set, each prediction model is verified to obtain a target prediction model.
[0018] In one possible implementation, the verifying each prediction model based on the test set to obtain a target prediction model includes:
[0019] Perform prediction on each prediction model according to the test set to obtain a prediction result corresponding to each prediction model;
[0020] A target prediction model is determined based on the prediction results.
[0021] In one possible implementation, determining a target prediction model based on the prediction result includes:
[0022] The prediction model corresponding to the prediction result that meets the target conditions is determined as the target prediction model.
[0023] In one possible implementation manner, testing and evaluating the WLAN to be tested based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested includes:
[0024] Performing performance testing on the WLAN to be tested to obtain attribute information and performance parameters of the WLAN to be tested;
[0025] The attribute information and performance parameters of the WLAN to be tested are input into the target prediction model to output a test evaluation result.
[0026] According to a second aspect of the present disclosure, a WLAN testing device is provided, the device comprising:
[0027] An acquisition module, used for acquiring a plurality of sample data to form a sample data set, wherein the sample data includes attribute information and performance parameters of the WLAN, and the performance parameters are used for characterizing the configuration and performance quality of the WLAN;
[0028] A building module, for building a plurality of initial models for predicting WLAN performance;
[0029] A determination module, used to obtain a target prediction model based on the sample data set and the multiple initial models;
[0030] The execution module is used to test and evaluate the WLAN to be tested based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested.
[0031] According to a third aspect of the present disclosure, there is provided an electronic device, including:
[0032] at least one processor; and
[0033] a memory communicatively connected to the at least one processor; wherein,
[0034] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.
[0035] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the present disclosure.
[0036] The present disclosure provides a WLAN testing method, device, equipment and storage medium, which obtains multiple sample data to form a sample data set, wherein the sample data includes attribute information and performance parameters of the WLAN, and the performance parameters are used to characterize the configuration and performance quality of the WLAN; then constructs multiple initial models for predicting WLAN performance; then obtains a target prediction model based on the sample data set and the multiple initial models; finally, the WLAN to be tested is tested and evaluated based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested. Compared with the prior art, this solution realizes the automated testing of WLAN and improves the testing efficiency; in addition, due to the use of standardized algorithms and the consistency of the testing standards, the testing accuracy is improved and the interference of human factors is reduced.
[0037] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:
[0039] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.
[0040] Figure 1 A schematic diagram of a WLAN testing method according to an embodiment of the present disclosure is shown;
[0041] Figure 2 A schematic diagram of the structure of a WLAN testing device according to an embodiment of the present disclosure is shown;
[0042] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0043] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.
[0044] Mobile phones, tablets, laptops, cameras, smart TVs, smart home appliances, sweeping robots and other electronic terminal devices all need to connect to the WLAN network. WLAN performance is crucial to user experience and network performance, so the WLAN card of electronic devices needs to be thoroughly tested.
[0045] Currently, WLAN testing requires manual operation, such as manually adjusting the AP channel and manually using the software provided by the manufacturer to adjust the signal attenuator to achieve attenuator control; and the evaluation is not standardized, because different WLAN manufacturers use different attenuation strengths when using attenuators, different AP channels, inconsistent throughput, different signal strengths, and different wireless router frequency bands, which requires testers to make manual judgments, resulting in low test efficiency. Based on this, the present disclosure provides a WLAN testing method.
[0046] like Figure 1 A schematic diagram of a WLAN testing method provided by an embodiment of the present disclosure is shown, and the method includes:
[0047] S1. Acquire multiple sample data to form a sample data set, where the sample data includes attribute information and performance parameters of the WLAN, and the performance parameters are used to characterize the configuration and performance quality of the WLAN.
[0048] Collect sample data containing WLAN attribute information and performance parameters for subsequent model building and training, where WLAN attribute information includes but is not limited to WLAN manufacturer, etc. Performance parameters are used to characterize the configuration and performance quality of WLAN, such as channel, frequency band, bandwidth, attenuation value, signal strength, throughput, delay, packet loss rate, etc. Through actual testing or simulated test environments, record signal strength and throughput data under different configuration conditions such as channel, frequency band, bandwidth, and attenuation value.
[0049] In one example, the method further includes:
[0050] A label for each of the sample data is determined, where the label is used to indicate whether the sample data meets a preset condition.
[0051] By labeling each sample data, the label is used to characterize whether the sample data meets the test requirements, so each piece of data can be labeled with a PASS or FAIL label. By labeling each sample data, supervision information can be provided for model training to ensure that the model can learn the correct classification rules. Table 1 is an example of sample data.
[0052] Table 1
[0053]
[0054] S2. Construct multiple initial models for predicting WLAN performance.
[0055] Based on algorithms such as machine learning or deep learning, build multiple initial models for predicting WLAN performance. For example, select appropriate algorithms such as logistic regression, decision tree, support vector machine, neural network, etc.
[0056] S3. Obtain a target prediction model based on the sample data set and the multiple initial models.
[0057] According to the attribute information and performance parameters of the sample data, the initial model is trained, the initial model is preliminarily evaluated, and the model with better performance is selected as the candidate model.
[0058] Further evaluation of candidate models can be performed using methods such as cross-validation. Based on the evaluation results, the model with the highest prediction accuracy and the strongest generalization ability is selected as the target prediction model. The target prediction model is tuned to improve its prediction performance.
[0059] S4. Testing and evaluating the WLAN to be tested based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested.
[0060] The determined target prediction model is used to test and evaluate the WLAN to be tested. Specifically, the information is input into the target prediction model to obtain a prediction result. Based on the prediction result, the performance of the WLAN to be tested is evaluated, and a corresponding test report or suggestion can also be given.
[0061] In the above scheme, a sample data set is formed by obtaining multiple sample data, wherein the sample data includes the attribute information and performance parameters of the WLAN, and the performance parameters are used to characterize the configuration and performance quality of the WLAN; then multiple initial models for predicting the performance of the WLAN are constructed; then based on the sample data set and the multiple initial models, a target prediction model is obtained; finally, the WLAN to be tested is tested and evaluated based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested. Compared with the prior art, this scheme realizes automated testing and improves testing efficiency; in addition, due to the use of standardized algorithms and the consistency of test standards, the test accuracy is improved and the interference of human factors is reduced.
[0062] In one example, after acquiring the plurality of sample data, the method further includes: preprocessing the plurality of sample data; the preprocessing includes:
[0063] Data enhancement is performed on the performance parameters in the plurality of sample data.
[0064] By adjusting the attenuation value, changing the signal strength, etc., more diverse sample data can be generated, thereby improving the diversity of the data set and the robustness of the model. Through data enhancement, the model can also be prevented from overfitting and the model's generalization ability on unseen data can be improved.
[0065] In one example, obtaining a target prediction model based on the sample data set and the multiple initial models includes:
[0066] Dividing the sample data set into a training set and a test set;
[0067] Based on the training set, the multiple initial models are trained respectively to obtain multiple prediction models corresponding to each initial model;
[0068] Based on the test set, each prediction model is verified to obtain a target prediction model.
[0069] The entire sample data set is divided into two subsets, namely the training set and the test set. Cross-validation can also be performed, that is, the sample data set is divided into k subsets (called "folds"), and then the training and validation process is repeated k times. In each iteration, a subset is selected as the validation set and the remaining subsets are used as the training set. This method can make more comprehensive use of the data set and reduce the bias and variance of the model evaluation.
[0070] Use the training set to train multiple initial models, where the "label" column is encoded as binary (PASS=1, FAIL=0). The initial model can be a model based on different algorithms or assumptions, such as linear regression, logistic regression model, decision tree, random forest, support vector machine, neural network, etc. Through training, each initial model will learn the data rules and patterns in the training set and generate a corresponding prediction model.
[0071] In one example, verifying each of the prediction models based on the test set to obtain a target prediction model includes:
[0072] Perform prediction on each prediction model according to the test set to obtain a prediction result corresponding to each prediction model;
[0073] A target prediction model is determined based on the prediction results.
[0074] Wherein, determining a target prediction model based on the prediction result includes:
[0075] The prediction model corresponding to the prediction result that meets the target conditions is determined as the target prediction model.
[0076] Each prediction model is verified using the test set. The purpose of verification is to evaluate the performance of the prediction model on unseen data. For example, this can be accomplished by calculating evaluation indicators such as prediction accuracy, recall, F1 score, mean square error (MSE), etc. By comparing the performance of different prediction models on the test set, the prediction model with the best performance can be selected as the target prediction model.
[0077] In one example, by drawing a learning curve, one can visually observe the performance of the model under different training set sizes, thereby determining whether the model is overfitting or underfitting.
[0078] You can also reserve some sample data as a validation set, where the validation set is a data set independent of the training set and the test set, and is used for parameter adjustment and selection during the model training process. By comparing the performance of the model on the validation set under different parameter configurations, the optimal model parameters can be selected. The existence of the validation set can avoid overfitting of the model on the training set and ensure that the model has good generalization ability on unseen data.
[0079] After model selection, this scheme determines that the optimal target prediction model is the logistic regression model (LogisticRegression).
[0080] In order to further improve the prediction ability of the logistic regression model, the sample data set obtained in step S1 is divided into a training set and a test set, for example, 70% of the data is used as a training set and 30% of the data is used as a test set. The training set is used to train the logistic regression model and estimate the regression coefficient (β).
[0081] Make predictions on the test set, compare the predicted probability with the threshold (such as 0.5), convert the result into a classification (PASS or FAIL), and then evaluate the model performance, such as calculating accuracy, precision, recall and other indicators until the accuracy meets the preset conditions, and then obtain the final target prediction model.
[0082] In one example, testing and evaluating the WLAN to be tested based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested includes:
[0083] Performing performance testing on the WLAN to be tested to obtain attribute information and performance parameters of the WLAN to be tested;
[0084] The attribute information and performance parameters of the WLAN to be tested are input into the target prediction model to output a test evaluation result.
[0085] First, connect the electronic device terminal (assuming it is a computer) installed with the WLAN to be tested to the local area network where the attenuator and the test machine are located; the computer to be tested executes the software for switching the router channel, and after the switching is completed; the test machine executes the task of adjusting the attenuator; then use the Chariot Endpoint client to perform a throughput test and record the current signal strength; the computer to be tested inputs the current computer's WLAN card manufacturer, signal strength, current routing channel, current router frequency band, throughput data and other data into the target prediction model, and the model performs intelligent evaluation and judges PASS or Fail based on the evaluation results.
[0086] According to the embodiments of the present disclosure, Figure 2 A schematic diagram of a WLAN testing device provided by the present disclosure is shown, the device comprising:
[0087] An acquisition module 10 is used to acquire a plurality of sample data to form a sample data set, wherein the sample data includes attribute information and performance parameters of the WLAN, and the performance parameters are used to characterize the configuration and performance quality of the WLAN;
[0088] A construction module 20 is used to construct a plurality of initial models for predicting WLAN performance;
[0089] A determination module 30, configured to obtain a target prediction model based on the sample data set and the plurality of initial models;
[0090] The execution module 40 is configured to test and evaluate the WLAN to be tested based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested.
[0091] In the above scheme, the acquisition module forms a sample data set by acquiring multiple sample data, wherein the sample data includes the attribute information and performance parameters of the WLAN, and the performance parameters are used to characterize the configuration and performance quality of the WLAN; then the construction module constructs multiple initial models for predicting the performance of the WLAN; the determination module obtains the target prediction model based on the sample data set and the multiple initial models; finally, the execution module tests and evaluates the WLAN to be tested based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested. Compared with the prior art, this scheme realizes automated testing and improves testing efficiency; in addition, due to the use of standardized algorithms and the consistency of test standards, the test accuracy is improved and the interference of human factors is reduced.
[0092] In one example, the acquisition module 10 is further used for:
[0093] Preprocessing the plurality of sample data; the preprocessing comprises:
[0094] Data enhancement is performed on the performance parameters in the plurality of sample data.
[0095] In one example, the acquisition module 10 is further used for:
[0096] A label for each of the sample data is determined, where the label is used to indicate whether the sample data meets a preset condition.
[0097] In one example, the determination module 30 is used to:
[0098] Dividing the sample data set into a training set and a test set;
[0099] Based on the training set, the multiple initial models are trained respectively to obtain multiple prediction models corresponding to each initial model;
[0100] Based on the test set, each prediction model is verified to obtain a target prediction model.
[0101] In one example, the determination module 30 is further configured to:
[0102] Perform prediction on each prediction model according to the test set to obtain a prediction result corresponding to each prediction model;
[0103] A target prediction model is determined based on the prediction results.
[0104] In one example, the determination module 30 is further configured to:
[0105] The prediction model corresponding to the prediction result that meets the target conditions is determined as the target prediction model.
[0106] In one example, the execution module 40 is used to:
[0107] Performing performance testing on the WLAN to be tested to obtain attribute information and performance parameters of the WLAN to be tested;
[0108] The attribute information and performance parameters of the WLAN to be tested are input into the target prediction model to output a test evaluation result.
[0109] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device, including:
[0110] at least one processor; and
[0111] a memory communicatively connected to the at least one processor; wherein,
[0112] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.
[0113] According to an embodiment of the present disclosure, the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the method described in the present disclosure.
[0114] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.
[0115] Figure 3 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.
[0116] like Figure 3As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.
[0117] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0118] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the WLAN test method. For example, in some embodiments, the WLAN test method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the WLAN test method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the WLAN test method in any other appropriate manner (e.g., by means of firmware).
[0119] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0120] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0121] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0123] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.
[0124] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.
[0125] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0126] In addition, 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 technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.
[0127] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. A WLAN test and evaluation method, characterized in that: The method comprises: Acquire multiple sample data to form a sample data set, where the sample data includes attribute information and performance parameters of the WLAN, where the performance parameters are used to characterize the configuration and performance quality of the WLAN; Building multiple initial models for predicting WLAN performance; Based on the sample data set and the multiple initial models, a target prediction model is obtained; The WLAN to be tested is tested and evaluated based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested.
2. The method according to claim 1, characterized in that After obtaining the plurality of sample data, the method further comprises: preprocessing the plurality of sample data; the preprocessing comprises: Data enhancement is performed on the performance parameters in the plurality of sample data.
3. The method according to claim 1, characterized in that The method further includes: A label for each of the sample data is determined, where the label is used to indicate whether the sample data meets a preset condition.
4. The method according to claim 1, characterized in that: The step of obtaining a target prediction model based on the sample data set and the plurality of initial models comprises: Dividing the sample data set into a training set and a test set; Based on the training set, the multiple initial models are trained respectively to obtain multiple prediction models corresponding to each initial model; Based on the test set, each prediction model is verified to obtain a target prediction model.
5. The method according to claim 4, characterized in that The step of verifying each prediction model based on the test set to obtain a target prediction model includes: Perform prediction on each prediction model according to the test set to obtain a prediction result corresponding to each prediction model; A target prediction model is determined based on the prediction results.
6. The method according to claim 5, characterized in that The determining of the target prediction model based on the prediction result comprises: The prediction model corresponding to the prediction result that meets the target conditions is determined as the target prediction model.
7. The method according to claim 1, characterized in that The WLAN to be tested is tested and evaluated based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested, including: Performing performance testing on the WLAN to be tested to obtain attribute information and performance parameters of the WLAN to be tested; The attribute information and performance parameters of the WLAN to be tested are input into the target prediction model to output a test evaluation result.
8. A WLAN testing device, characterized in that: The device comprises: An acquisition module, used for acquiring a plurality of sample data to form a sample data set, wherein the sample data includes attribute information and performance parameters of the WLAN, and the performance parameters are used for characterizing the configuration and performance quality of the WLAN; A building module, for building a plurality of initial models for predicting WLAN performance; A determination module, used to obtain a target prediction model based on the sample data set and the multiple initial models; The execution module is used to test and evaluate the WLAN to be tested based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested.
9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute: Acquire multiple sample data to form a sample data set, where the sample data includes attribute information and performance parameters of the WLAN, where the performance parameters are used to characterize the configuration and performance quality of the WLAN; Building multiple initial models for predicting WLAN performance; Based on the sample data set and the multiple initial models, a target prediction model is obtained; The WLAN to be tested is tested and evaluated based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested.
10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute: Acquire multiple sample data to form a sample data set, where the sample data includes attribute information and performance parameters of the WLAN, where the performance parameters are used to characterize the configuration and performance quality of the WLAN; Building multiple initial models for predicting WLAN performance; Based on the sample data set and the multiple initial models, a target prediction model is obtained; The WLAN to be tested is tested and evaluated based on the target prediction model and the attribute information and performance parameters of the WLAN to be tested.