Quality evaluation method and device of wireless network and computer equipment

Abstract

The invention discloses a quality evaluation method and device of a wireless network and computer equipment. The method comprises the following steps: acquiring wireless network sensing data of a target base station, the sensing data comprising at least one of a wireless network load condition, a wireless network lagging condition, a wireless network time delay condition and a wireless network downloading rate condition; performing cloud agglomeration processing on the sensing data to obtain a cloud agglomeration result, and generating multiple types of wireless network sensing data information streams based on the cloud agglomeration result; performing cloud separation on each type of wireless network sensing data information flow to obtain sensing data after cloud separation; and evaluating the quality of the wireless network of the to-be-evaluated area based on the sensing data after cloud separation. According to the method and the device, the technical problem of relatively low accuracy of wireless network quality evaluation in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of wireless network technology, and in particular to a method, device and computer equipment for evaluating the quality of a wireless network. Background Art

[0002] In recent years, with the rapid development of IoT services, wireless networks, as their core carrier platform, are facing unprecedented challenges. The widespread application of IoT services requires wireless networks to provide stable and efficient services. However, due to the significant differences between IoT services and traditional services in data transmission, traffic patterns, and user behavior, the optimization and management of wireless networks have become extremely complex. Especially in terms of user perception, due to the large-scale deployment of IoT devices and the high-frequency transmission of data, traditional network monitoring and optimization methods are difficult to accurately and real-time evaluate the impact of network quality on user perception.

[0003] Although the network perception evaluation method provided in the related art can quantitatively describe customer perception, its weighting coefficient depends on manual setting, lacks the ability to adapt to dynamic changes in the network, and may significantly affect the accuracy of the evaluation when it is improperly set, resulting in poor optimization measures. Or it overly relies on thresholds set by business experience, lacks the ability to deeply analyze and intelligently process multi-dimensional perception data, resulting in low accuracy of perception evaluation in complex network environments, making it difficult to fully capture various factors that affect user perception. Summary of the invention

[0004] The embodiments of the present application provide a wireless network quality assessment method, apparatus, and computer device to at least solve the technical problem of low accuracy of wireless network quality assessment in the related art.

[0005] According to one aspect of an embodiment of the present application, a method for assessing the quality of a wireless network is provided, comprising: acquiring wireless network perception data of a target base station, the perception data comprising at least one of: wireless network load conditions, wireless network freeze conditions, wireless network delay conditions, and wireless network download rate conditions; performing cloud aggregation processing on the perception data to obtain cloud aggregation results, and generating multiple types of wireless network perception data information flows based on the cloud aggregation results; performing cloud separation on each type of the wireless network perception data information flow to obtain perception data after cloud separation; and assessing the quality of the wireless network in an area to be assessed based on the perception data after cloud separation.

[0006] Optionally, cloud clustering is performed on the perception data to obtain a cloud clustering result, including: normalizing the perception data to obtain normalized perception data; performing feature extraction on the normalized perception data to obtain perception data containing multi-dimensional features, wherein the multi-dimensional features include at least one of the following: a load corresponding to the perception data, a collection period corresponding to the perception data, and a numerical feature of the perception data, wherein the numerical feature of the perception data includes the maximum value, minimum value, mean value and standard deviation of different types of perception data; cloud clustering is performed on the perception data containing multi-dimensional features to obtain the cloud clustering result.

[0007] Optionally, cloud clustering is performed on the perception data containing multi-dimensional features respectively to obtain the cloud clustering result, including: obtaining an objective function for constraining the cloud clustering result, the objective function is determined at least based on a standard vector distance and a vector supplement coefficient, the standard vector distance is used to represent the standard vector distance between the perception data and the cluster center of the cloud clustering result, and the vector supplement coefficient is determined based on the ratio of the perception data that must be included in each cluster; using the objective function to perform cloud clustering on the perception data containing multi-dimensional features to obtain the cloud clustering result.

[0008] Optionally, the vector supplement coefficient is determined by the following method, including:

[0009]

[0010] In the formula, DH h Represents the data subset with relevant relationship in each type of perception data, Card(Z i ) represents the number of nodes in the i-th cluster that are related to DH h The number of data in the data with constraint relationship, Card(DH h ) indicates DH h The number of data in Z i Indicates the number of DH in the i-th cluster h The data in the card (Z i ).

[0011] Optionally, multiple types of wireless network perception data information flows are generated based on the cloud clustering results, including: obtaining the center points of multiple clusters in the cloud clustering results; generating multiple wireless network perception data information flows according to preset rules based on the center points of each cluster, wherein each wireless network perception data information flow contains a corresponding target feature, and the feature value of each target feature is determined according to the value of the feature contained in the center point of each cluster, and the preset rules represent the range of types to which the target features contained in the wireless network perception data information flow belong, and the wireless network perception data information flow corresponding to each cluster is a type of wireless network perception data information flow.

[0012] Optionally, the method also includes: reconstructing the perception data corresponding to the multiple wireless network perception data information flows according to the multiple wireless network perception data information flows to obtain reconstructed data; comparing the reconstructed data with the wireless network perception data, and determining evaluation results of the multiple wireless network perception data information flows based on the comparison results, wherein the evaluation results are used to represent the expression capabilities of the multiple wireless network perception data information flows.

[0013] Optionally, performing cloud separation on each type of the wireless network perception data information flow to obtain the perception data after cloud separation includes: obtaining a plurality of target features contained in each type of the wireless network perception data information flow;

[0014] Optionally, cloud separation is performed on each type of the wireless network perception data information flow to obtain perception data after cloud separation, including: obtaining multiple information flow strength sequences in each type of the wireless network perception data information flow, the information flow strength sequence is used to characterize the indicators corresponding to the wireless network perception data information flow in each type of the wireless network perception data information flow, and the indicators are used to evaluate the quality of the wireless network perception data information flow; selecting an information flow strength sequence in a target area from the multiple information flow strength sequences to determine it as the information flow strength sequence to be separated; determining the category of the information flow strength sequence to be separated based on the distance between the information flow strength sequence to be separated and each type of the wireless network perception data information flow, to obtain the perception data after cloud separation.

[0015] According to another aspect of an embodiment of the present application, a wireless network quality assessment device is also provided, including: an acquisition module, used to acquire wireless network perception data of a target base station, the perception data including at least one of: wireless network load conditions, wireless network freeze conditions, wireless network delay conditions, and wireless network download rate conditions; a clustering module, used to perform cloud clustering processing on the perception data to obtain cloud clustering results, and generate multiple types of wireless network perception data information flows based on the cloud clustering results; a separation module, used to perform cloud separation on each type of the wireless network perception data information flow, respectively, to obtain perception data after cloud separation; an assessment module, used to assess the quality of the wireless network in the assessment area based on the perception data after cloud separation.

[0016] According to another aspect of the embodiment of the present application, a computer device is provided, including: a memory and a processor, wherein the memory is used to store program instructions; and the processor is connected to the memory and is used to execute the above-mentioned wireless network quality assessment method.

[0017] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned wireless network quality assessment method by running the computer program.

[0018] According to yet another aspect of the embodiments of the present application, a computer program product is provided, including computer instructions, which implement the above-mentioned wireless network quality assessment method when executed by a processor.

[0019] In an embodiment of the present application, wireless network perception data of a target base station is obtained, and the perception data includes at least one of: wireless network load conditions, wireless network freeze conditions, wireless network delay conditions, and wireless network download rate conditions; the perception data is cloud clustered to obtain cloud clustering results, and multiple types of wireless network perception data information flows are generated based on the cloud clustering results; each type of wireless network perception data information flow is cloud separated to obtain cloud-separated perception data; based on the cloud-separated perception data, the quality of the wireless network in the evaluation area is evaluated, thereby achieving the purpose of secondary segmentation of the clustered data and evaluating the wireless network based on the secondary segmented data, thereby achieving the technical effect of improving the accuracy of wireless network quality evaluation, and thus solving the technical problem of low accuracy of wireless network quality evaluation in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a quality assessment method for a wireless network according to an embodiment of the present application;

[0022] Figure 2 is a flow chart of a quality assessment method for a wireless network according to an embodiment of the present application;

[0023] Figure 3 It is a structural diagram of a quality assessment device for a wireless network according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] The information collected in the embodiments of the present application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or reject automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0027] In the related art, the weighting coefficients of various perception indicators are mainly set manually, which is too simple and has poor interpretability. The weighting coefficients are not set accurately, which may have a great impact on the user's network perception evaluation, or the basic thresholds of indicators are set based on business experience for evaluation, resulting in low accuracy of wireless network quality evaluation. In order to solve the problems existing in the related art, the embodiment of the present application provides a wireless network quality evaluation method, which can be run on Figure 1 In the computer terminal shown, the computer terminal is explained below.

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

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

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

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

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

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

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

[0035] Figure 2 is a flow chart of a quality assessment method for a wireless network according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0036] Step S202, obtaining wireless network perception data of the target base station, wherein the perception data includes at least one of: wireless network load, wireless network freeze, wireless network delay, and wireless network download rate;

[0037] In step S202, the wireless network sensing data includes multiple data streams, and the target area H covered by the target base station is H={h 1 ,h 2 ,...,h n}, where h n represents the nth region, and the data flow set corresponding to each region is O={o 1 ,o 2 ,...,o n}, where o n Indicates the data stream contained in the nth region, each data stream contains multiple types of perception data, for example: h i ∈(Class 1 ,Class 2 ,...Class E ), indicating that the categories of perception data contained in the i-th region include: Class 1 ,Class 2 ,...Class E E.g. Class 1 Indicates network lag, Class 2 Network delay, Class E represents the network download rate, and E represents the number of categories of perception data. Each data stream contains multiple perception data, and each perception data contains features of multiple dimensions.

[0038] Step S204, performing cloud aggregation processing on the sensing data to obtain a cloud aggregation result, and generating multiple types of wireless network sensing data information flows based on the cloud aggregation result;

[0039] Step S206, performing cloud separation on each type of wireless network perception data information flow to obtain cloud-separated perception data;

[0040] Step S208: evaluating the quality of the wireless network in the area to be evaluated based on the post-cloud separation perception data.

[0041] Through the above steps S202 to S208, in the embodiment of the present application, by acquiring the wireless network perception data of the target base station, the perception data includes at least one of: wireless network load, wireless network freeze, wireless network delay, and wireless network download rate; the perception data is subjected to cloud clustering to obtain cloud clustering results, and multiple types of wireless network perception data information flows are generated based on the cloud clustering results; each type of wireless network perception data information flow is subjected to cloud separation to obtain cloud-separated perception data; based on the cloud-separated perception data, the quality of the wireless network in the area to be evaluated is evaluated, thereby achieving the purpose of secondary segmentation of the clustered data and evaluating the wireless network based on the secondary segmented data, thereby achieving the technical effect of improving the accuracy of wireless network quality evaluation, and thus solving the technical problem of low accuracy of wireless network quality evaluation in related technologies. The following is a detailed description.

[0042] In some embodiments of the present application, the specific steps of performing cloud clustering processing on the perception data to obtain a cloud clustering result include: normalizing the perception data to obtain normalized perception data; performing feature extraction on the normalized perception data to obtain perception data containing multi-dimensional features, wherein the multi-dimensional features include at least one of the following: the load corresponding to the perception data, the collection period corresponding to the perception data, and the numerical features of the perception data, and the numerical features of the perception data include the maximum value, minimum value, mean and standard deviation of different types of perception data; performing cloud clustering processing on the perception data containing multi-dimensional features to obtain the cloud clustering result.

[0043] Wherein, cloud clustering is performed on the perception data containing multi-dimensional features respectively to obtain the cloud clustering result, including: obtaining an objective function for constraining the cloud clustering result, the objective function is determined at least according to a standard vector distance and a vector supplement coefficient, the standard vector distance is used to represent the standard vector distance between the perception data and the cluster center of the cloud clustering result, and the vector supplement coefficient is determined according to the ratio of the perception data that must be included in each cluster; and cloud clustering is performed on the perception data containing multi-dimensional features using the objective function to obtain the cloud clustering result.

[0044] It should be noted that the objective function is as follows:

[0045]

[0046] In the formula, c represents the number of nodes (clusters), N represents the number of data flows, and b ie Represents data flow Y e For the cloud aggregation center (cluster center) A i The membership degree of ie≤1 and A i represents the i-th cloud cluster center, r represents the fuzzy coefficient (constant), χ ie represents the vector supplement coefficient. ||·|| represents the standard vector distance.

[0047] The vector supplement coefficient is determined by the following methods, including:

[0048]

[0049] In the formula, DH h Represents the data subset with relevant relationship in each type of perception data, Card(Z i ) represents the number of nodes in the i-th cluster that are related to DH h The number of data in the data with constraint relationship, Card(DH h ) indicates DH h The number of data in Z i Indicates the number of DH in the i-th cluster h The data in the card (Z i ).

[0050] It should be noted that the data subset with correlation in each type of perception data represents data related to each type of perception data, and the constraint relationship represents the constraint relationship of each cluster, for example: time, that is, data at the same time.

[0051] In some embodiments of the present application, multiple types of wireless network perception data information flows are generated based on the cloud clustering results, including: obtaining the center points of multiple clusters in the cloud clustering results (cloud clustering center points); generating multiple wireless network perception data information flows according to preset rules based on the center points of each cluster, wherein each wireless network perception data information flow contains a corresponding target feature, and the feature value of each target feature is determined according to the value of the feature contained in the center point of each cluster, and the preset rules represent the range of types to which the target features contained in the wireless network perception data information flow belong, and the wireless network perception data information flow corresponding to each cluster is a type of wireless network perception data information flow.

[0052] For example: around the cloud gathering center A i , for example: A 1 ,A 2 ,...,A c Construct multiple wireless network sensing data information flows NTG = {P 1 ,P 2 ,...,P c}, where P i = {P i1 ,P i2 ,...,Pid}, i = 1, 2, ..., c, d represents the feature dimension of the perception data, P i represents the i-th wireless network sensing data information flow, P i = {P i1 ,P i2 ,...,P id} indicates that the i-th wireless network sensing data information flow contains from 1 to d target features.

[0053] It is understandable that the wireless network sensing data information flow is similar to the information tree structure. In actual application scenarios, each target feature (each dimension target feature)

[0054] P ij =[A ij -δ / 2*range j ,A ij +δ / 2*range j ]

[0055] Among them, A ij Represents the cloud gathering center A i The value of the j-th branch (j-th dimension feature), i = 1, 2, ..., c, j = 1, 2, ..., q, q, c are all positive integers, δ represents G i The value of G i Represents perception data, range j Represents the numerical change value of the j-th dimension feature of the original data, where the original data is the perception data before feature extraction.

[0056] Understandably, A i Indicates the center of the value.

[0057] In an optional manner, the perception data corresponding to the multiple wireless network perception data information flows are reconstructed according to the multiple wireless network perception data information flows, and the perception data corresponding to the multiple wireless network perception data information flows are determined as reconstructed data; the reconstructed data is compared with the wireless network perception data, and evaluation results of the multiple wireless network perception data information flows are determined based on the comparison results, and the evaluation results are used to represent the expression capabilities of the multiple wireless network perception data information flows.

[0058] The specific reconstruction process is as follows: From multiple wireless network sensing data information flows NTG = {P 1 ,P 2 ,...,P c}Reconstruct the data flow Y e , and obtain the reconstructed data If Y e and If the similarity is greater than a preset similarity threshold, the evaluation result is determined to be a successful reconstruction, and the expression capability of the wireless network perception data information flow is determined to be verified.

[0059] In wireless network, data information flow P is sensed i If the expressive ability is verified,

[0060] Data Flow Y e P i The membership degree is The membership degree of other wireless network sensing data information flows is g=1,2,...,c and g≠i,

[0061] In wireless network, data information flow P is sensed i When the expressive ability of is verified, the membership aggregation and the constructed wireless network perception data information flow are defuzzified to calculate

[0062]

[0063] In the formula, and YesP i The lower and upper bounds of The calculation formula is:

[0064]

[0065] In the formula, δ j Represents the value of the j-th dimension feature of the perception data.

[0066] In some embodiments of the present application, cloud separation is performed on each type of the wireless network perception data information flow to obtain the perception data after cloud separation, including: obtaining multiple information flow strength sequences in each type of the wireless network perception data information flow, the information flow strength sequence is used to characterize the indicators corresponding to the wireless network perception data information flow in each type of the wireless network perception data information flow, and the indicators are used to evaluate the quality of the wireless network perception data information flow; selecting the information flow strength sequence in the target area from the multiple information flow strength sequences, and determining it as the information flow strength sequence to be separated; determining the category of the information flow strength sequence to be separated according to the distance between the information flow strength sequence to be separated and each type of the wireless network perception data information flow, to obtain the perception data after cloud separation.

[0067] Specifically, if a wireless network perception data information flow does not contain any information flow strength sequence within the target area, it is regarded as an unknown category and is not used in the cloud separation stage; for a wireless network perception data information flow that contains at least one information flow strength sequence within the target area, the category of its wireless network perception data information flow is assigned according to the number of information flow strength sequences of different categories.

[0068] The more uneven the data value distribution in the information flow, the greater the information flow intensity. For example, if the number of users under all base stations is evenly distributed from 10 to 100, the information is evenly distributed and the information flow intensity is small; if the traffic range under all base stations is 10M-1000M, 60% of the users are above 900M, and 30% of the users are below 100M, the distribution is uneven and the information flow intensity is large.

[0069] In an optional manner, a rule base of the target area level and the information flow intensity level can be set to extract cloud separation rules of information flow intensity from each wireless network perception data information flow. For information flow intensity cloud separation, the distance between the information flow intensity and each wireless network perception data information flow is calculated.

[0070] The level represents the value of traffic / number of users, and the rule base is used to define different areas, such as schools and shopping malls.

[0071] The specific cloud separation process is as follows:

[0072] For the information flow intensity sequence F to be separated, cloud separation is performed according to the following steps:

[0073] If the characteristics of the information flow strength sequence to be separated belong to the characteristics of multiple target characteristics contained in the wireless network perception data information flow, it is determined that the information flow strength sequence belongs to the category of the wireless network perception data information flow. In the actual application scenario, if the characteristics of the information flow strength sequence to be separated do not belong to the characteristics of multiple target characteristics contained in the wireless network perception data information flow, the information flow strength sequence F to be separated and the wireless network perception data information flow P are calculated. i The distance between them is divided into P containing the closest information flow intensity sequence F. i in the category.

[0074] The perception data after cloud separation is used to train a preset model, such as an Xgboost model, to determine the degree of influence of each type of data on wireless network perception in different scenarios from the perception data after cloud separation, and determine the weight of each type of data according to the degree of influence, receive the characteristics of the area to be evaluated, use the trained model to analyze the characteristics of the area to be evaluated, determine the weight of each type of cloud separation data in the area to be evaluated, and then evaluate the wireless network quality of the area to be evaluated.

[0075] Figure 3 is a structural diagram of a quality assessment device for a wireless network according to an embodiment of the present application, the device comprising:

[0076] An acquisition module 30 is used to acquire wireless network perception data of a target base station, wherein the perception data includes at least one of: wireless network load, wireless network freeze, wireless network delay, and wireless network download rate;

[0077] A clustering module 32 is used to perform cloud clustering processing on the sensing data to obtain a cloud clustering result, and generate multiple types of wireless network sensing data information flows based on the cloud clustering result;

[0078] A separation module 34, configured to perform cloud separation on each type of wireless network perception data information flow to obtain cloud-separated perception data;

[0079] The evaluation module 36 is used to evaluate the quality of the wireless network in the evaluation area based on the post-cloud separation perception data.

[0080] Through the above-mentioned wireless network quality assessment device, in an embodiment of the present application, by acquiring the wireless network perception data of the target base station, the perception data includes at least one of: wireless network load status, wireless network freeze status, wireless network delay status, and wireless network download rate status; cloud clustering is performed on the perception data to obtain cloud clustering results, and multiple types of wireless network perception data information flows are generated based on the cloud clustering results; cloud separation is performed on each type of the wireless network perception data information flow to obtain cloud-separated perception data; based on the cloud-separated perception data, the quality of the wireless network in the area to be assessed is assessed, thereby achieving the purpose of secondary segmentation of the clustered data and assessing the wireless network based on the secondary segmented data, thereby achieving the technical effect of improving the accuracy of wireless network quality assessment, and then solving the technical problem of low accuracy of wireless network quality assessment in related technologies.

[0081] The clustering module 32 includes: a clustering submodule, which is used to perform cloud clustering processing on the perception data to obtain a cloud clustering result, including: normalizing the perception data to obtain normalized perception data; performing feature extraction on the normalized perception data to obtain perception data containing multi-dimensional features, wherein the multi-dimensional features include at least one of the following: the load corresponding to the perception data, the collection period corresponding to the perception data, and the numerical features of the perception data, and the numerical features of the perception data include the maximum value, minimum value, mean and standard deviation of different types of perception data; performing cloud clustering processing on the perception data containing multi-dimensional features to obtain the cloud clustering result.

[0082] The clustering submodule includes: a processing unit, which is used to perform cloud clustering processing on the perception data containing multi-dimensional features respectively to obtain the cloud clustering result, including: obtaining an objective function for constraining the cloud clustering result, the objective function is determined at least according to a standard vector distance and a vector supplement coefficient, the standard vector distance is used to represent the standard vector distance between the perception data and the cluster center of the cloud clustering result, and the vector supplement coefficient is determined according to the ratio of the perception data that must be included in each cluster; using the objective function to perform cloud clustering processing on the perception data containing multi-dimensional features to obtain the cloud clustering result.

[0083] The processing unit includes: a determination subunit, which is used to determine the vector supplement coefficient in the following manner, including:

[0084]

[0085] In the formula, DH h Represents the data subset with relevant relationship in each type of perception data, Card(Z i ) represents the number of nodes in the i-th cluster that are related to DH h The number of data in the data with constraint relationship, Card(DH h ) indicates DH h The number of data in Z i Indicates the number of DH in the i-th cluster h The data in the card (Z i ).

[0086] The clustering module 32 includes: a generation submodule, which is used to generate multiple types of wireless network perception data information flows based on the cloud clustering results, including: obtaining the center points of multiple clusters in the cloud clustering results; generating multiple wireless network perception data information flows according to the center points of each cluster according to preset rules, wherein each wireless network perception data information flow contains a corresponding target feature, and the feature value of each target feature is determined according to the value of the feature contained in the center point of each cluster, and the preset rule represents the range of types to which the target features contained in the wireless network perception data information flow belong, and the wireless network perception data information flow corresponding to each cluster is a type of wireless network perception data information flow.

[0087] The generation submodule includes: an evaluation unit, which is used to reconstruct the perception data corresponding to the multiple wireless network perception data information flows according to the multiple wireless network perception data information flows to obtain reconstructed data; compare the reconstructed data with the wireless network perception data, and determine the evaluation results of the multiple wireless network perception data information flows based on the comparison results, and the evaluation results are used to represent the expression capabilities of the multiple wireless network perception data information flows.

[0088] The separation module 34 includes: a separation submodule, which is used to perform cloud separation on each type of the wireless network perception data information flow to obtain the perception data after cloud separation, including: obtaining multiple information flow strength sequences in each type of the wireless network perception data information flow, the information flow strength sequence is used to characterize the indicators corresponding to the wireless network perception data information flow in each type of the wireless network perception data information flow, and the indicators are used to evaluate the quality of the wireless network perception data information flow; selecting the information flow strength sequence in the target area from the multiple information flow strength sequences, and determining it as the information flow strength sequence to be separated; determining the category of the information flow strength sequence to be separated according to the distance between the information flow strength sequence to be separated and each type of the wireless network perception data information flow, and obtaining the perception data after cloud separation.

[0089] It should be noted that Figure 3 The wireless network quality assessment device is used to perform Figure 2 The quality assessment method of the wireless network shown in the figure, therefore the relevant explanations in the above-mentioned quality assessment method of the wireless network are also applicable to the quality assessment device of the wireless network, and will not be repeated here.

[0090] The embodiment of the present application further provides a computer device, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned wireless network quality assessment method.

[0091] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned wireless network quality assessment method by running the computer program.

[0092] An embodiment of the present application also provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the wireless network quality assessment method in the present application are implemented.

[0093] The embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the wireless network quality assessment method in the present application.

[0094] The embodiment of the present application also provides a computer program, which, when executed by a processor, implements the steps of the wireless network quality assessment method in the present application.

[0095] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0096] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0099] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0100] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0101] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for evaluating the quality of a wireless network, characterized in that: include: Acquire wireless network perception data of the target base station, wherein the perception data includes at least one of: wireless network load, wireless network freeze, wireless network delay, and wireless network download rate; Performing cloud aggregation processing on the sensing data to obtain a cloud aggregation result, and generating multiple types of wireless network sensing data information flows based on the cloud aggregation result; Performing cloud separation on each type of wireless network perception data information flow to obtain cloud-separated perception data; The quality of the wireless network in the area to be evaluated is evaluated based on the post-cloud separation perception data.

2. The method according to claim 1, characterized in that Performing cloud aggregation processing on the perception data to obtain a cloud aggregation result includes: Normalizing the sensed data to obtain normalized sensed data; Performing feature extraction on the normalized perception data to obtain perception data containing multi-dimensional features, wherein the multi-dimensional features include at least one of the following: a load corresponding to the perception data, a collection period corresponding to the perception data, and a numerical feature of the perception data, wherein the numerical feature of the perception data includes a maximum value, a minimum value, a mean value, and a standard deviation of different types of perception data; Perform cloud clustering processing on the perception data containing multi-dimensional features to obtain the cloud clustering result.

3. The method according to claim 2, characterized in that Performing cloud clustering processing on the perception data containing multi-dimensional features respectively to obtain the cloud clustering result includes: Acquire an objective function for constraining a cloud clustering result, wherein the objective function is determined at least according to a standard vector distance and a vector supplement coefficient, wherein the standard vector distance is used to represent a standard vector distance between the sensory data and a cluster center of the cloud clustering result, and the vector supplement coefficient is determined according to a ratio of the sensory data that must be included in each cluster; The objective function is used to perform cloud clustering processing on the perception data containing multi-dimensional features to obtain the cloud clustering result.

4. The method according to claim 3, characterized in that The vector supplement coefficient is determined by the following method, including: In the formula, DH h Represents the data subset with relevant relationship in each type of perception data, Card(Z i ) represents the number of nodes in the i-th cluster that are related to DH h The number of data in the data with constraint relationship, Card(DH h ) indicates DH h The number of data in Z i Indicates the number of DH in the i-th cluster h The data in the card (Z i ).

5. The method according to claim 1, characterized in that Generating multiple types of wireless network perception data information flows based on the cloud aggregation results, including: Obtaining the center points of multiple clusters in the cloud clustering result; Multiple wireless network perception data information flows are generated according to preset rules based on the center point of each cluster, wherein each wireless network perception data information flow contains corresponding target features, and the feature value of each target feature is determined according to the value of the feature contained in the center point of each cluster. The preset rules represent the range of types to which the target features contained in the wireless network perception data information flow belong, and the wireless network perception data information flow corresponding to each cluster is a type of wireless network perception data information flow.

6. The method according to claim 5, characterized in that The method further comprises: Reconstruct the perception data corresponding to the multiple wireless network perception data information flows according to the multiple wireless network perception data information flows to obtain reconstructed data; The reconstructed data is compared with the wireless network perception data, and evaluation results of the multiple wireless network perception data information flows are determined based on the comparison result, wherein the evaluation results are used to represent the expression capabilities of the multiple wireless network perception data information flows.

7. The method according to claim 1, characterized in that Performing cloud separation on each type of wireless network perception data information flow to obtain cloud-separated perception data includes: Acquire multiple information flow strength sequences in each type of the wireless network perception data information flow, the information flow strength sequences are used to characterize indicators corresponding to the wireless network perception data information flows in each type of the wireless network perception data information flow, and the indicators are used to evaluate the quality of the wireless network perception data information flow; Selecting an information flow intensity sequence in a target area from the plurality of information flow intensity sequences and determining it as the information flow intensity sequence to be separated; According to the information flow strength sequence to be separated and the information flow distance of each type of the wireless network perception data, the category of the information flow strength sequence to be separated is determined, and the perception data after cloud separation is obtained.

8. A quality assessment device for a wireless network, characterized in that: include: An acquisition module is used to acquire wireless network perception data of a target base station, wherein the perception data includes at least one of: wireless network load, wireless network freeze, wireless network delay, and wireless network download rate; A clustering module, configured to perform cloud clustering processing on the sensing data to obtain a cloud clustering result, and generate multiple types of wireless network sensing data information flows based on the cloud clustering result; A separation module, used to perform cloud separation on each type of wireless network perception data information flow to obtain cloud-separated perception data; An evaluation module is used to evaluate the quality of the wireless network in the evaluation area based on the post-cloud separation perception data.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; The processor is connected to the memory, and is used to execute the quality assessment method of a wireless network as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the quality assessment method for a wireless network as claimed in any one of claims 1 to 7 is implemented.

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