Network evaluation method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to a network evaluation method and device, electronic equipment and storage medium, and belongs to the field of communication technology. The method comprises: obtaining network evaluation indexes of a target network at M sampling time points; storing the M network evaluation indexes of the target network in multiple candidate queues respectively, the multiple network evaluation indexes in the candidate queues being sorted in order of sampling time from early to late, and the set lengths of at least two candidate queues being different; and if the number of network evaluation indexes in a target queue reaches the set length of the target queue, evaluating the target network based on the multiple network evaluation indexes in the target queue. Thus, the set lengths of at least two candidate queues are different, network evaluation can be triggered multiple times at intervals of a certain time length, the updating frequency of network evaluation results can be reduced, the stability of network evaluation is improved, and the method is suitable for application scenarios of throughput level evaluation of cellular networks.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a network evaluation method, apparatus, electronic device, and storage medium. Background Technology

[0002] Currently, to ensure that electronic devices can access the internet normally, most systems require network evaluation. However, most related technologies rely on manual network evaluation, which consumes a lot of manpower and resources. Moreover, network evaluation results often change frequently between multiple results, meaning that the network evaluation results are updated too often, resulting in poor stability and affecting user experience. Summary of the Invention

[0003] This disclosure provides a network evaluation method, apparatus, electronic device, computer-readable storage medium, and computer program product to at least solve the problems of network evaluation methods in related technologies, such as high consumption of human and material resources, excessively frequent updates of network evaluation results, and poor stability of network evaluation. The technical solution of this disclosure is as follows:

[0004] According to a first aspect of the present disclosure, a network evaluation method is provided, comprising: obtaining network evaluation indicators of a target network at M sampling times, wherein M is a positive integer; storing the M network evaluation indicators of the target network into multiple candidate queues, wherein the multiple network evaluation indicators in the candidate queues are sorted from earliest to latest according to the sampling time, at least two of the candidate queues have different set lengths, and the set length of the candidate queues is the maximum number of indicators allowed to be stored in the candidate queues; if the number of network evaluation indicators in the target queue reaches the set length of the target queue, evaluating the target network based on the multiple network evaluation indicators in the target queue, wherein the multiple candidate queues include the target queue.

[0005] In one embodiment of this disclosure, evaluating the target network based on multiple network evaluation metrics in the target queue includes: obtaining predefined conditions for the target evaluation result; identifying whether the predefined conditions for the target evaluation result are met based on the multiple network evaluation metrics in the target queue; and updating the evaluation result of the target network to the target evaluation result if the predefined conditions for the target evaluation result are met.

[0006] In one embodiment of this disclosure, the plurality of candidate queues include a first queue and a second queue, wherein the set length of the first queue is less than the set length of the second queue;

[0007] The step of evaluating the target network based on multiple network evaluation metrics in the target queue includes: if the target queue is the first queue, identifying the conditions that satisfy the first evaluation result based on the multiple network evaluation metrics in the first queue, and updating the evaluation result of the target network to the first evaluation result; wherein, the first evaluation result includes the target network's throughput being greater than or equal to a first set threshold, and / or the target network's throughput level being higher than or equal to a set level.

[0008] In one embodiment of this disclosure, evaluating the target network based on multiple network evaluation metrics in the target queue includes: if the target queue is the second queue, identifying conditions that satisfy a second evaluation result based on the multiple network evaluation metrics in the second queue, and updating the evaluation result of the target network to the second evaluation result; identifying conditions that satisfy a third evaluation result based on the multiple network evaluation metrics in the second queue, and updating the evaluation result of the target network to the third evaluation result; wherein the second evaluation result and the third evaluation result include the target network's throughput being less than the first preset threshold, and / or the target network's throughput level being lower than the preset level.

[0009] In one embodiment of this disclosure, the network evaluation metric includes at least one transmission rate selected from uplink rate or downlink rate. The method further includes: setting the average value of multiple transmission rates in the first queue as a condition for setting the first evaluation result if the average value of multiple transmission rates in the first queue is greater than a second preset threshold; setting the uplink rate in the second queue as greater than a third preset threshold and the downlink rate in the second queue as less than or equal to the third preset threshold as a condition for setting the second evaluation result, wherein the third preset threshold is less than or equal to the second preset threshold; setting the average value of multiple transmission rates in the second queue as less than or equal to the second preset threshold, and / or setting at least K transmission rates in the second queue as less than or equal to the second preset threshold as a condition for setting the third evaluation result, wherein K is a positive integer.

[0010] In one embodiment of this disclosure, the setting conditions for obtaining the target evaluation result include: obtaining the mapping relationship between the candidate queue and the setting conditions of the candidate evaluation result; and based on the mapping relationship, selecting the setting conditions of the candidate evaluation result that have a mapping relationship with the target queue from the condition library, and using them as the setting conditions of the target evaluation result.

[0011] In one embodiment of this disclosure, the method further includes: if the set conditions for each of the target evaluation results are not met, keeping the evaluation result of the target network unchanged.

[0012] In one embodiment of this disclosure, after evaluating the target network, the method further includes: retrieving the top N network evaluation metrics from the target queue and returning to execute the steps of obtaining the network evaluation metrics of the target network at M sampling times and subsequent steps, where M is a positive integer and N≥M.

[0013] In one embodiment of this disclosure, after evaluating the target network, the method further includes: if the evaluation result of the target network is a set evaluation result, clearing the network evaluation indicators in the remaining candidate queues other than the target queue.

[0014] In one embodiment of this disclosure, the set evaluation result includes the target network's throughput being greater than or equal to a first set threshold, and / or the target network's throughput level being higher than or equal to a set level.

[0015] In one embodiment of this disclosure, evaluating the target network includes determining the throughput and / or throughput level of the target network.

[0016] In one embodiment of this disclosure, the target network includes a cellular network.

[0017] According to a second aspect of the present disclosure, a network evaluation apparatus is provided, comprising: an acquisition module configured to acquire network evaluation indicators of a target network at M sampling times, wherein M is a positive integer; a storage module configured to store the M network evaluation indicators of the target network into multiple candidate queues, wherein the multiple network evaluation indicators in the candidate queues are sorted from earliest to latest according to the sampling time, at least two of the candidate queues have different set lengths, and the set length of the candidate queues is the maximum number of indicators allowed to be stored in the candidate queues; and an evaluation module configured to evaluate the target network based on the multiple network evaluation indicators in the target queue if the number of network evaluation indicators in the target queue reaches the set length of the target queue, wherein the multiple candidate queues include the target queue.

[0018] In one embodiment of this disclosure, the evaluation module is further configured to perform: obtaining the set conditions for the target evaluation result; identifying whether the set conditions for the target evaluation result are met based on multiple network evaluation indicators in the target queue; and updating the evaluation result of the target network to the target evaluation result if the set conditions for the target evaluation result are met.

[0019] In one embodiment of this disclosure, the plurality of candidate queues include a first queue and a second queue, wherein the set length of the first queue is less than the set length of the second queue;

[0020] The evaluation module is further configured to perform the following: if the target queue is the first queue, based on multiple network evaluation indicators in the first queue, identify the set conditions that satisfy the first evaluation result, and update the evaluation result of the target network to the first evaluation result; wherein, the first evaluation result includes the throughput of the target network being greater than or equal to a first set threshold, and / or the throughput level of the target network being higher than or equal to a set level.

[0021] In one embodiment of this disclosure, the evaluation module is further configured to perform: if the target queue is the second queue, based on multiple network evaluation indicators in the second queue, identify the conditions for satisfying the second evaluation result, and update the evaluation result of the target network to the second evaluation result; based on multiple network evaluation indicators in the second queue, identify the conditions for satisfying the third evaluation result, and update the evaluation result of the target network to the third evaluation result; wherein the second evaluation result and the third evaluation result include the target network's throughput being less than the first set threshold, and / or the target network's throughput level being lower than the set level.

[0022] In one embodiment of this disclosure, the network evaluation metric includes at least one transmission rate selected from uplink rate or downlink rate. The evaluation module is further configured to perform the following: setting the average value of multiple transmission rates in the first queue as greater than a second preset threshold as a setting condition for the first evaluation result; setting each uplink rate in the second queue as greater than a third preset threshold, and each downlink rate in the second queue as less than or equal to the third preset threshold as a setting condition for the second evaluation result, wherein the third preset threshold is less than or equal to the second preset threshold; setting the average value of multiple transmission rates in the second queue as less than or equal to the second preset threshold, and / or setting at least K transmission rates in the second queue as less than or equal to the second preset threshold as a setting condition for the third evaluation result, wherein K is a positive integer.

[0023] In one embodiment of this disclosure, the evaluation module is further configured to perform: obtaining the mapping relationship between the candidate queue and the set conditions of the candidate evaluation results; and based on the mapping relationship, selecting the set conditions of the candidate evaluation results that have a mapping relationship with the target queue from the condition library, and using them as the set conditions of the target evaluation results.

[0024] In one embodiment of this disclosure, the evaluation module is further configured to perform the following: if the set conditions for each of the target evaluation results are not met, keep the evaluation result of the target network unchanged.

[0025] In one embodiment of this disclosure, after evaluating the target network, the evaluation module is further configured to perform: extracting the top N network evaluation metrics from the target queue and returning to perform the steps of obtaining the network evaluation metrics of the target network at M sampling times and subsequent steps, where M is a positive integer and N≥M.

[0026] In one embodiment of this disclosure, after evaluating the target network, the evaluation module is further configured to: if the evaluation result of the target network is a set evaluation result, clear the network evaluation indicators in the remaining candidate queues other than the target queue.

[0027] In one embodiment of this disclosure, the set evaluation result includes the target network's throughput being greater than or equal to a first set threshold, and / or the target network's throughput level being higher than or equal to a set level.

[0028] In one embodiment of this disclosure, the evaluation module is further configured to perform: determining the throughput and / or throughput level of the target network.

[0029] In one embodiment of this disclosure, the target network includes a cellular network.

[0030] According to a third aspect of the present disclosure, an electronic device is provided, including a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the method described in the first aspect of the present disclosure.

[0031] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the method described in the first aspect of the present disclosure.

[0032] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, characterized in that, when executed by a processor of an electronic device, the computer program implements the steps of the method as described in the first aspect of the present disclosure.

[0033] The technical solution provided by the embodiments of this disclosure brings at least the following beneficial effects: the number of network evaluation indicators in the target queue can reach the set length of the target queue, that is, when the target queue is full, network evaluation is automatically triggered, and the set lengths of at least two candidate queues are different, so network evaluation can be triggered multiple times at certain intervals. Compared with the problem of network evaluation results being updated too frequently due to multiple simultaneous triggering of network evaluation, this solution can reduce the update frequency of network evaluation results, improve the stability of network evaluation, improve user experience, and is suitable for application scenarios of throughput level evaluation of cellular networks.

[0034] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0036] Figure 1 This is a flowchart illustrating a network evaluation method according to an exemplary embodiment.

[0037] Figure 2 This is a flowchart illustrating a network evaluation method according to another exemplary embodiment.

[0038] Figure 3 This is a flowchart illustrating a network evaluation method according to another exemplary embodiment.

[0039] Figure 4 This is a flowchart illustrating a network evaluation method according to another exemplary embodiment.

[0040] Figure 5 This is a flowchart illustrating a network evaluation method according to another exemplary embodiment.

[0041] Figure 6 This is a block diagram illustrating a network evaluation apparatus according to an exemplary embodiment.

[0042] Figure 7 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0044] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0045] The acquisition, storage, use, and processing of data in this disclosed technical solution all comply with the relevant laws and regulations.

[0046] Figure 1 This is a flowchart illustrating a network evaluation method according to an exemplary embodiment, such as... Figure 1 As shown, the network evaluation method of this disclosure includes the following steps.

[0047] S101, obtain the network evaluation index of the target network at M sampling times, where M is a positive integer.

[0048] It should be noted that the network evaluation method of this disclosure is executed by an electronic device, such as a mobile phone, laptop, desktop computer, in-vehicle terminal, smart home appliance, wearable device, etc. Wearable devices may include wrist-worn devices (such as smartwatches and smart bracelets), head-worn devices, foot-worn devices, etc. The network evaluation method of this disclosure can be executed by the network evaluation device of this disclosure, which can be configured in any electronic device to execute the network evaluation method of this disclosure.

[0049] It should be noted that the target network may include any network in the relevant technologies, without further restrictions here.

[0050] For example, the target network includes cellular networks, also known as mobile networks, which are a type of mobile communication hardware architecture. These can include GSM (Global System for Mobile Communications), CDMA (Code Division Multiple Access) mobile communication systems, 4G (the 4th Generation) mobile communication systems, and so on.

[0051] For example, the target network includes WLAN (Wireless Local Area Networks).

[0052] It should be noted that there are no strict restrictions on the sampling time. For example, the time interval between two adjacent sampling times is 2 seconds. The network evaluation metrics may differ at different sampling times.

[0053] It should be noted that network evaluation metrics can include any metric used for network evaluation in the relevant technologies, without much limitation here. For example, network evaluation metrics can include transmission rate (such as uplink rate, downlink rate), bandwidth, latency, RRT (Round-Trip Time), throughput, latency-bandwidth product, utilization, packet loss rate, retransmission rate, etc.

[0054] In one implementation, obtaining network evaluation metrics of the target network at M sampling times includes obtaining the uplink rate and downlink rate of the target network at the i-th sampling time, and taking the maximum value of the uplink rate and downlink rate at the i-th sampling time as the transmission rate of the target network at the i-th sampling time. Therefore, the maximum value of the uplink rate and downlink rate at the same sampling time can be used as the transmission rate of the target network at that sampling time.

[0055] In one implementation, before obtaining the network evaluation index of the target network at M sampling times, the method further includes identifying the evaluation conditions that satisfy the target network. Therefore, the network evaluation index of the target network at M sampling times can be obtained only when the evaluation conditions of the target network are met.

[0056] It should be noted that the evaluation criteria for the target network are not overly restrictive. For example, the ability of an electronic device to access the target network, and / or the ability of an electronic device to access the target network, can be used as evaluation criteria for the target network.

[0057] S102, store the M network evaluation indicators of the target network into multiple candidate queues respectively. The multiple network evaluation indicators in the candidate queues are sorted from early to late according to the sampling time. At least two candidate queues have different set lengths. The set length of the candidate queue is the maximum number of indicators that the candidate queue is allowed to store.

[0058] It should be noted that there must be at least two candidate queues, but there are no strict restrictions on the number of candidate queues.

[0059] For example, there are two candidate queues, including a first queue and a second queue, where the set length of the first queue is less than the set length of the second queue. For example, the set length of the first queue is 5, and the set length of the second queue is 10.

[0060] For example, there are four candidate queues, including a first queue, a second queue, a third queue, and a fourth queue, with the set lengths of the first to fourth queues increasing progressively. For example, the set lengths of the first to fourth queues are 5, 10, 15, and 20 respectively.

[0061] It should be noted that the multiple network evaluation indicators in the candidate queue are sorted from earliest to latest according to the sampling time. Therefore, the later the sampling time of the network evaluation indicator, the later the network evaluation indicator will be in the candidate queue.

[0062] In one implementation, M network evaluation metrics of the target network are stored in a candidate queue, including storing one network evaluation metric of the target network at the tail of the candidate queue if M is 1.

[0063] In one implementation, storing M network evaluation metrics of the target network into a candidate queue includes, if M is a positive integer greater than 1, storing the M network evaluation metrics of the target network sequentially into the tail of the candidate queue according to the sampling time from earliest to latest.

[0064] In one implementation, the candidate queue includes a FIFO (First Input First Output) queue.

[0065] For example, taking a candidate queue of 2 as an example, the candidate queues include a first queue and a second queue, where the first queue has a set length of 5 and the second queue has a set length of 10.

[0066] If both the first and second queues include network evaluation indicators 1 to 4, where the sampling time t1 of network evaluation indicator 1 is earlier than the sampling time t2 of network evaluation indicator 2, the sampling time t2 of network evaluation indicator 2 is earlier than the sampling time t3 of network evaluation indicator 3, and the sampling time t3 of network evaluation indicator 3 is earlier than the sampling time t4 of network evaluation indicator 4, then the order of network evaluation indicators 1 to 4 in the first and second queues is as follows: network evaluation indicator 1, network evaluation indicator 2, network evaluation indicator 3, network evaluation indicator 4.

[0067] Taking M=1 as an example, the network evaluation index 5 of the target network at sampling time t5 can be obtained. The sampling time t4 of network evaluation index 4 is earlier than the sampling time t5 of network evaluation index 5. Network evaluation index 5 can be stored in the first and second queues respectively, that is, the first and second queues are updated with network evaluation indices 1 to 5 respectively, and the order of network evaluation indices 1 to 5 in the first and second queues is as follows: network evaluation index 1, network evaluation index 2, network evaluation index 3, network evaluation index 4, network evaluation index 5.

[0068] For example, if the first and second queues are all network evaluation index 1, network evaluation index 2, network evaluation index 3, and network evaluation index 4, then network evaluation index 5 is stored in the first and second queues respectively, including storing network evaluation index 5 at the tail of the first and second queues respectively, so that the first and second queues are updated to network evaluation index 1, network evaluation index 2, network evaluation index 3, network evaluation index 4, and network evaluation index 5 respectively.

[0069] If the first queue includes network evaluation indicators 3 and 4, and the second queue includes network evaluation indicators 1 to 4, wherein the sampling time t1 of network evaluation indicator 1 is earlier than the sampling time t2 of network evaluation indicator 2, the sampling time t2 of network evaluation indicator 2 is earlier than the sampling time t3 of network evaluation indicator 3, and the sampling time t3 of network evaluation indicator 3 is earlier than the sampling time t4 of network evaluation indicator 4, then the order of network evaluation indicators 3 and 4 in the first queue is as follows: network evaluation indicator 3, network evaluation indicator 4, and the order of network evaluation indicators 1 to 4 in the second queue is as follows: network evaluation indicator 1, network evaluation indicator 2, network evaluation indicator 3, network evaluation indicator 4.

[0070] Taking M=3 as an example, we can obtain the network evaluation index 5 at sampling time t5, the network evaluation index 6 at sampling time t6, and the network evaluation index 7 at sampling time t7 of the target network. The sampling time t4 of network evaluation index 4 is earlier than the sampling time t5 of network evaluation index 5, the sampling time t5 of network evaluation index 5 is earlier than the sampling time t6 of network evaluation index 6, and the sampling time t6 of network evaluation index 6 is earlier than the sampling time t7 of network evaluation index 7. We can store network evaluation indices 5 to 7 into the first and second queues respectively, that is, update the first queue with network evaluation indices 3 to 7 and update the second queue with network evaluation indices 1 to 7. The order of network evaluation indices 3 to 7 in the first queue is as follows: network evaluation index 3, network evaluation index 4, network evaluation index 5, network evaluation index 6, network evaluation index 7. The order of network evaluation indices 1 to 7 in the first and second queues is as follows: network evaluation index 1, network evaluation index 2, network evaluation index 3, network evaluation index 4, network evaluation index 5, network evaluation index 6, network evaluation index 7.

[0071] For example, the first queue consists of network evaluation index 3 and network evaluation index 4, and the second queue consists of network evaluation index 1, network evaluation index 2, network evaluation index 3, and network evaluation index 4.

[0072] Network evaluation metrics 5 through 7 are stored in the first and second queues respectively. This includes storing network evaluation metric 5 at the end of the first and second queues respectively, thereby updating the first queue with network evaluation metrics 3, 4, and 5, and updating the second queue with network evaluation metrics 1, 2, 3, 4, and 5. Network evaluation metric 6 is then stored at the end of the first and second queues respectively, thereby updating the first queue with network evaluation metrics 3, 4, 5, and 6. The second queue is updated with network evaluation index 1, network evaluation index 2, network evaluation index 3, network evaluation index 4, network evaluation index 5, and network evaluation index 6. Network evaluation index 7 is stored at the tail of the first and second queues respectively. This is to update the first queue with network evaluation index 3, network evaluation index 4, network evaluation index 5, network evaluation index 6, and network evaluation index 7, and update the second queue with network evaluation index 1, network evaluation index 2, network evaluation index 3, network evaluation index 4, network evaluation index 5, network evaluation index 6, and network evaluation index 7.

[0073] S103, if the number of network evaluation metrics in the target queue reaches the set length of the target queue, the target network is evaluated based on the multiple network evaluation metrics in the target queue, wherein the multiple candidate queues include the target queue.

[0074] It should be noted that the target queue can be some or all of the candidate queues, and the number of target queues is at least one. The target network is evaluated based on multiple network evaluation metrics in the target queue. This can be achieved using any network evaluation method from related technologies, without further limitations. Evaluating the target network based on multiple network evaluation metrics in the target queue can include evaluating the target network based on each or some of the network evaluation metrics in the target queue.

[0075] In one implementation, the target network is evaluated based on multiple network evaluation metrics in the target queue, including obtaining a total network evaluation metric for the target network based on the multiple network evaluation metrics in the target queue, and evaluating the target network based on the total network evaluation metric.

[0076] In some examples, the total network evaluation index of the target network is obtained based on multiple network evaluation metrics in the target queue. This includes at least one processing method such as weighted averaging, weighted summation, or multiplication of the multiple network evaluation metrics in the target queue to obtain the total network evaluation index of the target network.

[0077] In some examples, the target network is evaluated based on the overall network evaluation index of the target network. This includes obtaining the mapping relationship between candidate intervals and candidate evaluation results, determining the target interval in which the overall network evaluation index of the target network is located from multiple candidate intervals, and selecting candidate intervals that have a mapping relationship with the target interval from multiple candidate intervals based on the mapping relationship, which are then used as the target evaluation results of the target network.

[0078] In one implementation, the target network is evaluated, including determining the target network evaluation index and / or the target network evaluation index level.

[0079] In some cases, the target network is evaluated, including determining its throughput and / or throughput levels. Understandably, multiple throughput levels can be pre-defined for the target network; for example, multiple throughput levels could include levels one through four, with each level representing an increasing throughput of the target network. For instance, level one could be an unknown throughput level, level two a congested throughput level, level three a low throughput level, and level four a high throughput level.

[0080] In some cases, the target network is evaluated, including determining the target network's bandwidth and / or bandwidth class.

[0081] In one implementation, the target network is evaluated, including determining the target network's risk level, quality level, risk score, quality score, etc.

[0082] In one implementation, the method further includes calling a target API (Application Programming Interface) to perform network evaluation metrics of the target network at M sampling times and subsequent steps.

[0083] The network evaluation method provided in this disclosure obtains network evaluation indicators of a target network at M sampling times, stores the M network evaluation indicators of the target network into multiple candidate queues, wherein the multiple network evaluation indicators in the candidate queues are sorted from earliest to latest according to the sampling time, and at least two candidate queues have different set lengths. The set length of the candidate queue is the maximum number of indicators allowed to be stored in the candidate queue. If the number of network evaluation indicators in the target queue reaches the set length of the target queue, the target network is evaluated based on the multiple network evaluation indicators in the target queue. The multiple candidate queues include the target queue. Therefore, network evaluation can be automatically triggered when the number of network evaluation indicators in the target queue reaches the set length of the target queue, that is, when the target queue is full. And since at least two candidate queues have different set lengths, network evaluation can be triggered multiple times at certain intervals. Compared with the problem of network evaluation results being updated too frequently due to multiple simultaneous triggers of network evaluation, this solution can reduce the update frequency of network evaluation results, improve the stability of network evaluation, and improve user experience. It is suitable for application scenarios of throughput level evaluation of cellular networks.

[0084] Figure 2 This is a flowchart illustrating a network evaluation method according to another exemplary embodiment, such as... Figure 2 As shown, the network evaluation method of this disclosure includes the following steps.

[0085] S201, obtain the network evaluation index of the target network at M sampling times, where M is a positive integer.

[0086] S202, store the M network evaluation metrics of the target network into multiple candidate queues respectively. The multiple network evaluation metrics in the candidate queues are sorted from early to late according to the sampling time. At least two candidate queues have different set lengths. The set length of the candidate queue is the maximum number of metrics that the candidate queue is allowed to store.

[0087] For details regarding steps S201-S202, please refer to the above embodiments, which will not be repeated here.

[0088] S203, if the number of network evaluation indicators in the target queue reaches the set length of the target queue, obtain the set conditions for the target evaluation result, wherein multiple candidate queues include the target queue.

[0089] It should be noted that the target evaluation results may be some or all of the candidate evaluation results, and the number of target evaluation results must be at least one. The setting conditions for different target evaluation results may be different. No excessive restrictions are placed on the setting conditions for the target evaluation results.

[0090] In one implementation, the method further includes setting conditions for the target evaluation result as follows: multiple network evaluation metrics in the target queue are greater than a set threshold, and / or the statistical values ​​of multiple network evaluation metrics are greater than a set threshold. The statistical values ​​include mean, sum, variance, standard deviation, median, etc.

[0091] In one embodiment, the method further includes setting conditions for the target evaluation result by having multiple network evaluation indicators in the target queue be less than or equal to a set threshold, and / or having the statistical values ​​of multiple network evaluation indicators be less than or equal to the set threshold.

[0092] In one implementation, obtaining the setting conditions for the target evaluation result includes obtaining the mapping relationship between the candidate queue and the setting conditions for the candidate evaluation results; based on the mapping relationship, selecting the setting conditions for the candidate evaluation results that have a mapping relationship with the target queue from the condition library, and using these as the setting conditions for the target evaluation result. Thus, the setting conditions for the target evaluation result can be obtained by taking into account the mapping relationship between the candidate queue and the setting conditions for the candidate evaluation results.

[0093] It is understandable that a candidate queue can have a mapping relationship with the setting conditions of multiple candidate evaluation results, and multiple candidate queues can have a mapping relationship with the setting conditions of a single candidate evaluation result; this is not further restricted here. The condition library includes the setting conditions of multiple candidate evaluation results.

[0094] In some examples, multiple candidate queues include a first queue and a second queue, wherein the set length of the first queue is less than the set length of the second queue. The method also includes establishing a mapping relationship between the first queue and a first evaluation result, and establishing mapping relationships between the second queue and second and third evaluation results, wherein the first evaluation result includes the target network's throughput being greater than or equal to a first set threshold, and / or the target network's throughput level being higher than or equal to a set level; the second and third evaluation results include the target network's throughput being less than the first set threshold, and / or the target network's throughput level being lower than a set level.

[0095] It should be noted that the first to third evaluation results are different. For example, if there are multiple throughput levels including levels one to four, and the throughput of the target network is progressively increased from level one to level four, then the set level can be level four. That is, the first evaluation result includes the target network's throughput being greater than or equal to the first set threshold, and / or, the target network's throughput level is level four; the second evaluation result includes the target network's throughput being less than the seventh set threshold, and / or, the target network's throughput level is level two; and the third evaluation result includes the target network's throughput being less than the first set threshold, and the target network's throughput being greater than or equal to the seventh set threshold, and / or, the target network's throughput level is level three, where the seventh set threshold is less than the first set threshold.

[0096] It should be noted that the conditions for setting the first to third evaluation results can be found in the following embodiments, and will not be repeated here.

[0097] S204, based on multiple network evaluation metrics in the target queue, identifies whether the set conditions for the target evaluation result are met.

[0098] S205, if the conditions for setting the target evaluation result are met, update the evaluation result of the target network to the target evaluation result.

[0099] For example, if the target evaluation result only includes target evaluation result A, and the setting condition for target evaluation result A is that the evaluation index of each network in the target queue is greater than a set threshold, if the evaluation index of each network in the target queue is greater than the set threshold, the setting condition of target evaluation result A is identified, and the evaluation result of the target network is updated to target evaluation result A.

[0100] For example, if the target evaluation result only includes target evaluation result B, and the setting condition for target evaluation result B is that the average value of each network evaluation indicator in the target queue is less than or equal to a set threshold, if the average value of each network evaluation indicator in the target queue is less than the set threshold, the setting condition for target evaluation result B is identified, and the evaluation result of the target network is updated to target evaluation result B.

[0101] S206 If the set conditions for the evaluation results of each target are not met, the evaluation results of the target network remain unchanged.

[0102] For example, if the target evaluation result only includes target evaluation result A, and the setting condition for target evaluation result A is that each network evaluation index in the target queue is greater than a set threshold, if there is at least one network evaluation index in the target queue that is less than or equal to the set threshold, the setting condition for target evaluation result A is not met, and the evaluation result of the target network remains unchanged.

[0103] For example, if the target evaluation results only include target evaluation results A and B, and the setting condition for target evaluation result A is that the evaluation index of each network in the target queue is greater than a set threshold, and the setting condition for target evaluation result B is that the average value of the evaluation index of each network in the target queue is less than or equal to a set threshold, if there is at least one network evaluation index in the target queue that is less than or equal to a set threshold, and the average value of the evaluation index of each network in the target queue is greater than a set threshold, the setting conditions for target evaluation results A and B are not met, and the evaluation results of the target network remain unchanged.

[0104] The network evaluation method provided in the embodiments of this disclosure obtains the setting conditions for the target evaluation result, identifies whether the setting conditions for the target evaluation result are met based on multiple network evaluation indicators in the target queue, and updates the evaluation result of the target network to the target evaluation result if the setting conditions are met; otherwise, the evaluation result of the target network remains unchanged. Thus, it can take into account multiple network evaluation indicators in the target queue, identify whether the setting conditions for the target evaluation result are met, update the evaluation result of the target network to the target evaluation result when the setting conditions are met, and keep the evaluation result of the target network unchanged if the setting conditions for each target evaluation result are not met.

[0105] Figure 3 This is a flowchart illustrating a network evaluation method according to another exemplary embodiment, such as... Figure 3 As shown, the network evaluation method of this disclosure includes the following steps.

[0106] S301, obtain the network evaluation index of the target network at M sampling times, where M is a positive integer.

[0107] S302, store the M network evaluation indicators of the target network into multiple candidate queues respectively. The multiple network evaluation indicators in the candidate queues are sorted from early to late according to the sampling time. At least two candidate queues have different set lengths. The set length of the candidate queue is the maximum number of indicators that the candidate queue is allowed to store.

[0108] For details regarding steps S301-S302, please refer to the above embodiments, which will not be repeated here.

[0109] S303, if the number of network evaluation indicators in the first queue reaches the set length of the first queue, identify the set conditions that meet the first evaluation result based on the multiple network evaluation indicators in the first queue.

[0110] S304, update the evaluation result of the target network to the first evaluation result.

[0111] S305, if the number of network evaluation indicators in the second queue reaches the set length of the second queue, identify the set conditions that meet the second evaluation result based on the multiple network evaluation indicators in the second queue.

[0112] S306, update the evaluation result of the target network to the second evaluation result.

[0113] S307, if the number of network evaluation indicators in the second queue reaches the set length of the second queue, based on the multiple network evaluation indicators in the second queue, identify the set conditions that satisfy the third evaluation result.

[0114] S308, update the evaluation result of the target network to the third evaluation result.

[0115] It should be noted that the multiple candidate queues include a first queue and a second queue, wherein the set length of the first queue is less than the set length of the second queue. The first evaluation result includes the target network's throughput being greater than or equal to a first set threshold, and / or the target network's throughput level being higher than or equal to a set level. The second and third evaluation results include the target network's throughput being less than the first set threshold, and / or the target network's throughput level being lower than a set level. The relevant content of the first to third evaluation results can be found in the above embodiments, and will not be repeated here.

[0116] In one implementation, the network evaluation metric includes at least one transmission rate, either uplink or downlink. The method further includes setting a condition for a first evaluation result as if the average of multiple transmission rates in a first queue is greater than a second preset threshold. Thus, when the average of multiple transmission rates in the first queue is relatively large, the condition for a first evaluation result can be identified.

[0117] The condition for the second evaluation result is that each uplink rate in the second queue is greater than a third preset threshold, and each downlink rate in the second queue is less than or equal to the third preset threshold, wherein the third preset threshold is less than or equal to the second preset threshold. Therefore, when each uplink rate in the second queue is relatively high and each downlink rate is relatively low, the condition for the second evaluation result can be identified.

[0118] The third evaluation result is defined as follows: the average of multiple transmission rates in the second queue is less than or equal to a second preset threshold, and / or at least K transmission rates in the second queue are less than or equal to the second preset threshold, where K is a positive integer. Therefore, when the average of multiple transmission rates in the second queue is small, and / or multiple transmission rates are small, the conditions for satisfying the third evaluation result can be identified.

[0119] It should be noted that there are no strict restrictions on the second and third threshold settings. For example, the third threshold setting can be 0, and the second threshold setting can be 300KB / s (kilobytes per second).

[0120] It should be noted that K is a positive integer less than or equal to the set length of the second queue. For example, if the length of the second queue is 10, then K = 8.

[0121] The network evaluation method provided in the embodiments of this disclosure, if the target queue is a first queue, identifies the conditions for meeting the first evaluation result based on multiple network evaluation indicators in the first queue, and updates the evaluation result of the target network to the first evaluation result. If the target queue is a second queue, it identifies the conditions for meeting the second or third evaluation result based on multiple network evaluation indicators in the second queue, and updates the evaluation result of the target network to the second or third evaluation result. Therefore, when the target queue is a short queue, it can identify whether the conditions for a good network throughput evaluation result are met; when the target queue is a long queue, it can identify whether the conditions for a poor network throughput evaluation result are met.

[0122] Based on any of the above embodiments, the method further includes setting a fourth evaluation result by setting multiple transmission rates in the target queue that are less than or equal to a third preset threshold. The fourth evaluation result includes the target network's throughput being less than an eighth preset threshold, and / or the target network's throughput level being lower than a target level, wherein the eighth preset threshold is less than a first preset threshold, and the target level is lower than a preset level. Therefore, when multiple transmission rates in the target queue are low, the setting condition for the fourth evaluation result can be identified.

[0123] For example, multiple throughput levels, including levels one through four, where each level represents an increasing throughput of the target network, can be defined as level four, with the target level being level one. This means the first evaluation result includes the target network's throughput being greater than or equal to a first set threshold, and / or, the target network's throughput level is level four. The second evaluation result includes the target network's throughput being less than a seventh set threshold and greater than or equal to an eighth set threshold, and / or, the target network's throughput level is level two. The third evaluation result includes the target network's throughput being less than the first set threshold and greater than or equal to a seventh set threshold, and / or, the target network's throughput level is level three, where the seventh set threshold is less than the first set threshold. The fourth evaluation result includes the target network's throughput being less than the eighth set threshold, and / or, the target network's throughput level is level one, where the seventh set threshold is less than the first set threshold, and the eighth set threshold is less than the seventh set threshold.

[0124] Figure 4 This is a flowchart illustrating a network evaluation method according to another exemplary embodiment, such as... Figure 4 As shown, the network evaluation method of this disclosure includes the following steps.

[0125] S401, obtain the network evaluation index of the target network at M sampling times, where M is a positive integer.

[0126] S402, store the M network evaluation metrics of the target network into multiple candidate queues respectively. The multiple network evaluation metrics in the candidate queues are sorted from early to late according to the sampling time. At least two candidate queues have different set lengths. The set length of the candidate queue is the maximum number of metrics that the candidate queue is allowed to store.

[0127] S403, if the number of network evaluation metrics in the target queue reaches the set length of the target queue, the target network is evaluated based on the multiple network evaluation metrics in the target queue, wherein the multiple candidate queues include the target queue.

[0128] For details regarding steps S401-S403, please refer to the above embodiments; they will not be repeated here.

[0129] S404: Retrieve the top N network evaluation metrics from the target queue and return to execute the steps of obtaining the network evaluation metrics of the target network at M sampling times and subsequent steps, where M is a positive integer and N≥M.

[0130] It should be noted that there are no restrictions on N and M. For example, N = M = 1, or N = M = 3, or M = 3 and N = 4.

[0131] For example, taking a candidate queue of 2 as an example, the candidate queues include a first queue and a second queue, where the first queue has a set length of 5 and the second queue has a set length of 10.

[0132] If both the first and second queues include network evaluation indicators 1 to 5, the relevant content of network evaluation indicators 1 to 5 can be found in the above embodiments, and will not be repeated here. The order of network evaluation indicators 1 to 5 in the first and second queues is as follows: network evaluation indicator 1, network evaluation indicator 2, network evaluation indicator 3, network evaluation indicator 4, and network evaluation indicator 5.

[0133] At this point, the number of network evaluation metrics in the first queue is 5, meaning the number of network evaluation metrics in the first queue has reached the set length of the first queue. The target network can then be evaluated based on multiple network evaluation metrics in the first queue, i.e., the target network can be evaluated based on network evaluation metrics 1 to 5. It should be noted that in this embodiment, the first queue is the target queue.

[0134] Taking N=M=1 as an example, the network evaluation index ranked first can be taken out from the first queue, that is, network evaluation index 1 can be taken out from the first queue to update the first queue with network evaluation indices 2 to 5, and then return to execute the process of obtaining the network evaluation index of the target network at one sampling time and its subsequent steps.

[0135] If the first queue includes network evaluation indicators 9 and 10, and the second queue includes network evaluation indicators 1 to 10, the relevant content of network evaluation indicators 1 to 7 can be found in the above embodiment, and will not be repeated here. The sampling time t7 of network evaluation indicator 7 is earlier than the sampling time t8 of network evaluation indicator 8, the sampling time t8 of network evaluation indicator 8 is earlier than the sampling time t9 of network evaluation indicator 9, and the sampling time t9 of network evaluation indicator 9 is earlier than the sampling time t9 of network evaluation indicator 10. 10 The network evaluation indicators 9 and 10 are ranked in the first queue as follows: Network evaluation indicator 9, Network evaluation indicator 10. The network evaluation indicators 1 to 10 are ranked in the second queue as follows: Network evaluation indicator 1, Network evaluation indicator 2, Network evaluation indicator 3, Network evaluation indicator 4, Network evaluation indicator 5, Network evaluation indicator 6, Network evaluation indicator 7, Network evaluation indicator 8, Network evaluation indicator 9, Network evaluation indicator 10.

[0136] At this point, the number of network evaluation metrics in the second queue is 10, meaning the number of network evaluation metrics in the second queue has reached the set length of the second queue. The target network can then be evaluated based on multiple network evaluation metrics in the second queue, i.e., based on network evaluation metrics 1 to 10. It should be noted that in this embodiment, the second queue is the target queue.

[0137] Taking N=M=3 as an example, the top 3 network evaluation metrics can be retrieved from the second queue, that is, network evaluation metrics 1 to 3 can be retrieved from the second queue to update the second queue with network evaluation metrics 4 to 10, and then return to execute the process of obtaining the network evaluation metrics of the target network at 3 sampling times and its subsequent steps.

[0138] The network evaluation method provided in the embodiments of this disclosure, after evaluating the target network, further includes retrieving the top N network evaluation indicators from the target queue and returning to execute the steps of obtaining the network evaluation indicators of the target network at M sampling times and subsequent steps, where M is a positive integer and N≥M. Therefore, after evaluating the target network, indicators with earlier sampling times can be retrieved from the target queue, allowing newly acquired network evaluation indicators to be stored in the target queue, thus continuing to utilize the target queue for network evaluation. This enables multi-round cyclic evaluation of the network, offering advantages such as low labor costs and high evaluation efficiency.

[0139] Figure 5This is a flowchart illustrating a network evaluation method according to another exemplary embodiment, such as... Figure 5 As shown, the network evaluation method of this disclosure includes the following steps.

[0140] S501, obtain the network evaluation index of the target network at M sampling times, where M is a positive integer.

[0141] S502, store the M network evaluation metrics of the target network into multiple candidate queues respectively. The multiple network evaluation metrics in the candidate queues are sorted from early to late according to the sampling time. At least two candidate queues have different set lengths. The set length of the candidate queue is the maximum number of metrics that the candidate queue is allowed to store.

[0142] S503, if the number of network evaluation metrics in the target queue reaches the set length of the target queue, the target network is evaluated based on the multiple network evaluation metrics in the target queue, wherein the multiple candidate queues include the target queue.

[0143] For details regarding steps S501-S503, please refer to the above embodiments; they will not be repeated here.

[0144] S504 If the evaluation result of the target network is the set evaluation result, clear the network evaluation indicators in the remaining candidate queues outside the target queue.

[0145] S505: Retrieve the top N network evaluation metrics from the target queue and return to execute the steps of obtaining the network evaluation metrics of the target network at M sampling times and subsequent steps, where M is a positive integer and N≥M.

[0146] For details regarding step S505, please refer to the above embodiments; they will not be repeated here.

[0147] It should be noted that there are no excessive restrictions on the evaluation results.

[0148] For example, taking a candidate queue of 2 as an example, the candidate queues include a first queue and a second queue, where the first queue has a set length of 5 and the second queue has a set length of 10.

[0149] If both the first and second queues include network evaluation indicators 1 to 5, the relevant content of network evaluation indicators 1 to 5 can be found in the above embodiments, and will not be repeated here. The order of network evaluation indicators 1 to 5 in the first and second queues is as follows: network evaluation indicator 1, network evaluation indicator 2, network evaluation indicator 3, network evaluation indicator 4, and network evaluation indicator 5.

[0150] At this point, the number of network evaluation metrics in the first queue is 5, meaning the number of network evaluation metrics in the first queue has reached the set length of the first queue. The target network can then be evaluated based on multiple network evaluation metrics in the first queue, i.e., the target network can be evaluated based on network evaluation metrics 1 to 5. It should be noted that in this embodiment, the first queue is the target queue.

[0151] If the evaluation result of the target network is the set evaluation result, the network evaluation indicators in the second queue are cleared to update the second queue to an empty queue. Taking N=M=1 as an example, the network evaluation indicator ranked first can be taken from the first queue, that is, network evaluation indicator 1 can be taken from the first queue to update the first queue with network evaluation indicators 2 to 5, and then return to execute the process of obtaining the network evaluation indicators of the target network at one sampling time and its subsequent steps.

[0152] In one implementation, the evaluation result is set to include the target network evaluation index of the target network being greater than or equal to a set threshold, and / or the target network evaluation index level of the target network being higher than or equal to a set level.

[0153] In some examples, the evaluation results are defined as follows: the target network's throughput is greater than or equal to a first set threshold, and / or, the target network's throughput level is higher than or equal to a set level. Therefore, when the target network's throughput is high, and / or the target network's throughput level is high, the network evaluation metrics in queues other than the target queue can be cleared. This extends the time it takes for queues other than the target queue to reach full capacity when network throughput is good, thus extending the duration of the good network throughput evaluation result. This effectively avoids the problem of frequently jumping between good and poor network throughput evaluation results shortly after obtaining a good one, thereby improving the user experience.

[0154] It should be noted that there are no excessive restrictions on the first set threshold or the set level. For example, if there are multiple throughput levels including the first to the fourth level, and the throughput of the target network is represented by the first to the fourth level, then the set level can be the fourth level. That is, the set evaluation result includes the target network throughput being greater than or equal to the first set threshold, and / or the target network throughput level being the fourth level.

[0155] In some examples, the evaluation results are defined as follows: the target network bandwidth is greater than or equal to a fourth set threshold, and / or the target network bandwidth level is higher than or equal to a set level. Therefore, when the target network bandwidth is large, and / or the target network bandwidth level is high, the network evaluation metrics in queues other than the target queue can be cleared. This extends the time it takes for queues other than the target queue to reach full load when the network bandwidth is good, thus extending the duration of the good network bandwidth evaluation result. This effectively avoids the problem of frequently jumping between good and poor network bandwidth evaluation results after obtaining a good bandwidth evaluation result, thereby improving the user experience.

[0156] It should be noted that there are no restrictions on the fourth set threshold or set level. For example, if there are multiple bandwidth levels including the first to the fourth level, and the bandwidth of the target network is progressively increased from the first to the fourth level, then the set level can be the fourth level. That is, the set evaluation result includes the target network bandwidth being greater than or equal to the fourth set threshold, and / or the target network bandwidth level being the fourth level.

[0157] In one implementation, the evaluation result is defined as follows: the target network's quality score is greater than or equal to a fifth predetermined threshold, and / or the target network's quality level is higher than or equal to a predetermined level. Therefore, when the target network's quality score is high, and / or the target network's quality level is high, the network evaluation metrics in the queues other than the target queue can be cleared. This extends the time it takes for the queues other than the target queue to reach full capacity when the network quality is good, thereby extending the duration of the good network quality evaluation result. This effectively avoids the problem of frequently jumping between good and poor network quality evaluation results after obtaining a good network quality evaluation result within a short period, thus improving the user experience.

[0158] It should be noted that there are no restrictions on the fifth set threshold and set level. For example, if there are multiple quality levels including the first to fourth levels, and the quality of the target network is represented by the first to fourth levels in progressively increasing order, then the set level can be the third level. That is, the set evaluation result includes the target network's quality score being greater than or equal to the fifth set threshold, and / or the target network's quality level being the third or fourth level.

[0159] In one implementation, the evaluation results are defined as follows: the target network's risk score is less than a sixth predetermined threshold, and / or the target network's risk level is lower than a predetermined level. Therefore, when the target network's risk score is low, and / or the target network's risk level is low, the network evaluation indicators in the queues other than the target queue can be cleared. This extends the time it takes for the queues other than the target queue to reach full capacity when the network risk is low, thereby extending the duration of the low-risk evaluation result. This effectively avoids the problem of frequently jumping between low and high risk in the network evaluation result after obtaining a low-risk evaluation result, thus improving the user experience.

[0160] It should be noted that there are no excessive restrictions on the sixth threshold and the sixth level. For example, if there are multiple risk levels, including levels one through four, and the risk of the target network is represented by levels one through four, then the level can be set to level two. That is, the evaluation result can include the target network’s risk score being less than the sixth threshold, and / or the target network’s risk level being level one or level two.

[0161] The network evaluation method provided in this disclosure, if the evaluation result of the target network is the set evaluation result, clears the network evaluation indicators in the remaining candidate queues outside the target queue. Therefore, when the evaluation result of the target network is the set evaluation result, the network evaluation indicators in the remaining queues outside the target queue can be cleared, extending the time for the remaining queues outside the target queue to reach full capacity, thereby extending the retention time of the set evaluation result. This effectively avoids the problem of obtaining an evaluation result other than the set evaluation result within a short period after obtaining the set evaluation result, i.e., the network evaluation result is updated too frequently, thus improving the user experience.

[0162] Based on any of the above embodiments, the method further includes preloading the network resources of the target network if the evaluation result of the target network is a first evaluation result. The first evaluation result includes the target network's throughput being greater than or equal to a first preset threshold, and / or the target network's throughput level being higher than or equal to a preset level. Therefore, preloading the network resources of the target network can be performed when the target network's throughput is good, which can shorten the loading time of network resources and improve the loading efficiency of network resources.

[0163] In some examples, the process of preloading network resources for the target network includes identifying whether the electronic device is located in the target area. It should be noted that the target area is not overly limited; for example, it can include areas with poor network stability, such as subways, trains, or remote areas. Therefore, preloading network resources for the target network can be performed when the target network has good throughput and the electronic device is located in the target area, making it suitable for application scenarios with unstable networks.

[0164] Based on any of the above embodiments, the method further includes performing fault handling on the target network if the evaluation result of the target network is a second evaluation result or a third evaluation result, and a currently running application depends on the target network. The second and third evaluation results include the target network's throughput being less than a first preset threshold, and / or the target network's throughput level being lower than a preset level. Therefore, when the target network's throughput is poor and a currently running application depends on the target network, automatic fault handling can be performed on the target network, helping to restore the target network to a state of better throughput in a timely manner.

[0165] Figure 6 This is a block diagram illustrating a network evaluation apparatus according to an exemplary embodiment. (Refer to...) Figure 6 The network evaluation device 100 of this embodiment includes: an acquisition module 110, a storage module 120 and an evaluation module 130.

[0166] The acquisition module 110 is configured to acquire network evaluation metrics of the target network at M sampling times, where M is a positive integer;

[0167] The storage module 120 is configured to store M network evaluation metrics of the target network into multiple candidate queues, wherein the multiple network evaluation metrics in the candidate queues are sorted from earliest to latest according to the sampling time, and at least two of the candidate queues have different set lengths, and the set length of the candidate queue is the maximum number of metrics allowed to be stored in the candidate queue.

[0168] Evaluation module 130 is configured to evaluate the target network based on multiple network evaluation metrics in the target queue if the number of network evaluation metrics in the target queue reaches a set length of the target queue, wherein the multiple candidate queues include the target queue.

[0169] In one embodiment of this disclosure, the evaluation module 130 is further configured to perform: obtaining the set conditions for the target evaluation result; identifying whether the set conditions for the target evaluation result are met based on multiple network evaluation indicators in the target queue; and updating the evaluation result of the target network to the target evaluation result if the set conditions for the target evaluation result are met.

[0170] In one embodiment of this disclosure, the plurality of candidate queues include a first queue and a second queue, wherein the set length of the first queue is less than the set length of the second queue;

[0171] The evaluation module 130 is further configured to perform the following: if the target queue is the first queue, based on multiple network evaluation indicators in the first queue, identify the set conditions that satisfy the first evaluation result, and update the evaluation result of the target network to the first evaluation result; wherein, the first evaluation result includes the throughput of the target network being greater than or equal to a first set threshold, and / or the throughput level of the target network being higher than or equal to a set level.

[0172] In one embodiment of this disclosure, the evaluation module 130 is further configured to perform: if the target queue is the second queue, based on multiple network evaluation indicators in the second queue, identify the set conditions that satisfy the second evaluation result, and update the evaluation result of the target network to the second evaluation result; based on multiple network evaluation indicators in the second queue, identify the set conditions that satisfy the third evaluation result, and update the evaluation result of the target network to the third evaluation result; wherein the second evaluation result and the third evaluation result include the target network's throughput being less than the first set threshold, and / or the target network's throughput level being lower than the set level.

[0173] In one embodiment of this disclosure, the network evaluation metric includes at least one transmission rate selected from uplink rate or downlink rate. The evaluation module 130 is further configured to perform the following: setting the average value of multiple transmission rates in the first queue as greater than a second preset threshold as a setting condition for the first evaluation result; setting each uplink rate in the second queue as greater than a third preset threshold, and each downlink rate in the second queue as less than or equal to the third preset threshold as a setting condition for the second evaluation result, wherein the third preset threshold is less than or equal to the second preset threshold; setting the average value of multiple transmission rates in the second queue as less than or equal to the second preset threshold, and / or setting at least K transmission rates in the second queue as less than or equal to the second preset threshold as a setting condition for the third evaluation result, wherein K is a positive integer.

[0174] In one embodiment of this disclosure, the evaluation module 130 is further configured to perform: obtaining the mapping relationship between the candidate queue and the set conditions of the candidate evaluation results; and based on the mapping relationship, selecting the set conditions of the candidate evaluation results that have a mapping relationship with the target queue from the condition library, and using them as the set conditions of the target evaluation results.

[0175] In one embodiment of this disclosure, the evaluation module 130 is further configured to perform the following: if the set conditions for each of the target evaluation results are not met, keep the evaluation result of the target network unchanged.

[0176] In one embodiment of this disclosure, after evaluating the target network, the evaluation module 130 is further configured to perform: extracting the top N network evaluation metrics from the target queue and returning to perform the steps of obtaining the network evaluation metrics of the target network at M sampling times and subsequent steps, where M is a positive integer and N≥M.

[0177] In one embodiment of this disclosure, after evaluating the target network, the evaluation module 130 is further configured to: if the evaluation result of the target network is a set evaluation result, clear the network evaluation indicators in the remaining candidate queues other than the target queue.

[0178] In one embodiment of this disclosure, the set evaluation result includes the target network's throughput being greater than or equal to a first set threshold, and / or the target network's throughput level being higher than or equal to a set level.

[0179] In one embodiment of this disclosure, the evaluation module 130 is further configured to perform: determining the throughput and / or throughput level of the target network.

[0180] In one embodiment of this disclosure, the target network includes a cellular network.

[0181] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0182] The network evaluation apparatus provided in this disclosure acquires network evaluation indicators of a target network at M sampling times, stores the M network evaluation indicators of the target network into multiple candidate queues, wherein the multiple network evaluation indicators in the candidate queues are sorted from earliest to latest according to the sampling time, and at least two candidate queues have different set lengths. The set length of the candidate queue is the maximum number of indicators allowed to be stored in the candidate queue. If the number of network evaluation indicators in the target queue reaches the set length of the target queue, the target network is evaluated based on the multiple network evaluation indicators in the target queue. The multiple candidate queues include the target queue. Therefore, network evaluation can be automatically triggered when the number of network evaluation indicators in the target queue reaches the set length of the target queue, i.e., when the target queue is full. And since the set lengths of at least two candidate queues are different, network evaluation can be triggered multiple times at certain intervals. Compared with the problem of network evaluation results being updated too frequently due to multiple simultaneous triggers of network evaluation, this solution can reduce the update frequency of network evaluation results, improve the stability of network evaluation, improve user experience, and is suitable for application scenarios of throughput level evaluation of cellular networks.

[0183] Figure 7 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0184] like Figure 7 As shown, the above-mentioned electronic device 200 includes:

[0185] The memory 210 and processor 220 are connected by a bus 230, which connects the different components (including the memory 210 and the processor 220). The memory 210 stores a computer program, which implements the network evaluation method described in this embodiment when the processor 220 executes the program.

[0186] Bus 230 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0187] Electronic device 200 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by electronic device 200, including volatile and non-volatile media, removable and non-removable media.

[0188] Memory 210 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 240 and / or cache memory 250. Electronic device 200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 260 may be used to read and write non-removable, non-volatile magnetic media (… Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 230 via one or more data media interfaces. Memory 210 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0189] A program / utility 280 having a set (at least one) of program modules 270 may be stored, for example, in memory 210. Such program modules 270 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 270 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0190] Electronic device 200 can also communicate with one or more external devices 290 (e.g., keyboard, pointing device, display 291, etc.), and with one or more devices that enable a user to interact with the electronic device 200, and / or with any device that enables the electronic device 200 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 292. Furthermore, electronic device 200 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 293. Figure 7 As shown, network adapter 293 communicates with other modules of electronic device 200 via bus 230. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0191] The processor 220 performs various functional applications and data processing by running programs stored in the memory 210.

[0192] It should be noted that the implementation process and technical principles of the electronic device in this embodiment are explained in the foregoing description of the network evaluation method of this disclosure embodiment, and will not be repeated here.

[0193] The electronic device provided in this embodiment can execute the network evaluation method described above, obtain network evaluation indicators of a target network at M sampling times, and store the M network evaluation indicators of the target network into multiple candidate queues. The multiple network evaluation indicators in the candidate queues are sorted from earliest to latest according to the sampling time. At least two candidate queues have different set lengths, and the set length of the candidate queue is the maximum number of indicators allowed to be stored in the candidate queue. If the number of network evaluation indicators in the target queue reaches the set length of the target queue, the target network is evaluated based on the multiple network evaluation indicators in the target queue. The multiple candidate queues include the target queue. Therefore, network evaluation can be automatically triggered when the number of network evaluation indicators in the target queue reaches the set length of the target queue, i.e., when the target queue is full. Since at least two candidate queues have different set lengths, network evaluation can be triggered multiple times at certain intervals. Compared to the problem of excessively frequent updates to network evaluation results caused by multiple simultaneous triggers, this solution can reduce the update frequency of network evaluation results, improve the stability of network evaluation, and enhance user experience. It is suitable for application scenarios involving throughput level evaluation of cellular networks.

[0194] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the steps of the network evaluation method provided in this disclosure.

[0195] Alternatively, the computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0196] To implement the above embodiments, this disclosure also provides a computer program product, including a computer program, characterized in that the computer program, when executed by a processor of an electronic device, implements the network evaluation method as described above.

[0197] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0198] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A network evaluation method, characterized in that, include: Obtain the network evaluation index of the target network at M sampling times, where M is a positive integer; The M network evaluation metrics of the target network are stored in multiple candidate queues. The multiple network evaluation metrics in the candidate queues are sorted from earliest to latest according to the sampling time. At least two candidate queues have different set lengths. The set length of the candidate queue is the maximum number of metrics that the candidate queue is allowed to store. If the number of network evaluation metrics in the target queue reaches the set length of the target queue, the target network is evaluated based on the multiple network evaluation metrics in the target queue, wherein the multiple candidate queues include the target queue.

2. The method according to claim 1, characterized in that, The evaluation of the target network based on multiple network evaluation metrics in the target queue includes: Conditions for obtaining target evaluation results; Based on multiple network evaluation metrics in the target queue, identify whether the set conditions for the target evaluation result are met; If the set conditions for the target evaluation result are met, the evaluation result of the target network is updated to the target evaluation result.

3. The method according to claim 2, characterized in that, The plurality of candidate queues include a first queue and a second queue, wherein the set length of the first queue is less than the set length of the second queue; The evaluation of the target network based on multiple network evaluation metrics in the target queue includes: If the target queue is the first queue, based on multiple network evaluation metrics in the first queue, the conditions that satisfy the first evaluation result are identified, and the evaluation result of the target network is updated to the first evaluation result; wherein, The first evaluation result includes the target network's throughput being greater than or equal to a first set threshold, and / or the target network's throughput level being higher than or equal to a set level.

4. The method according to claim 3, characterized in that, The evaluation of the target network based on multiple network evaluation metrics in the target queue includes: If the target queue is the second queue, based on multiple network evaluation indicators in the second queue, identify the conditions that meet the second evaluation result, and update the evaluation result of the target network to the second evaluation result; Based on multiple network evaluation metrics in the second queue, the conditions for meeting the third evaluation result are identified, and the evaluation result of the target network is updated to the third evaluation result; wherein, The second evaluation result and the third evaluation result include the target network's throughput being less than the first set threshold, and / or the target network's throughput level being lower than the set level.

5. The method according to claim 4, characterized in that, The network evaluation metric includes at least one transmission rate selected from uplink rate or downlink rate, and the method further includes: The average value of multiple transmission rates in the first queue is greater than a second set threshold, which is used as the setting condition for the first evaluation result. The condition for setting the second evaluation result is that each uplink rate in the second queue is greater than the third set threshold and each downlink rate in the second queue is less than or equal to the third set threshold, wherein the third set threshold is less than or equal to the second set threshold. The average of multiple transmission rates in the second queue is less than or equal to the second set threshold, and / or at least K transmission rates in the second queue are less than or equal to the second set threshold, as the setting condition for the third evaluation result, where K is a positive integer.

6. The method according to claim 2, characterized in that, The conditions for obtaining the target evaluation results include: Obtain the mapping relationship between the candidate queue and the set conditions of the candidate evaluation results; Based on the mapping relationship, the setting conditions for candidate evaluation results that have a mapping relationship with the target queue are selected from the condition library and used as the setting conditions for the target evaluation results.

7. The method according to claim 2, characterized in that, The method further includes: If the set conditions for each of the target evaluation results are not met, the evaluation result of the target network remains unchanged.

8. The method according to any one of claims 1-7, characterized in that, After evaluating the target network, the process further includes: Retrieve the top N network evaluation metrics from the target queue and return to execute the steps of obtaining the network evaluation metrics of the target network at M sampling times and subsequent steps, where M is a positive integer and N≥M.

9. The method according to any one of claims 1-7, characterized in that, After evaluating the target network, the process further includes: If the evaluation result of the target network is the set evaluation result, the network evaluation indicators in the remaining candidate queues other than the target queue are cleared.

10. The method according to claim 9, characterized in that, The set evaluation results include the target network's throughput being greater than or equal to a first set threshold, and / or the target network's throughput level being higher than or equal to a set level.

11. A network evaluation device, characterized in that, include: The acquisition module is configured to acquire network evaluation metrics of the target network at M sampling times, where M is a positive integer; The storage module is configured to store M network evaluation metrics of the target network into multiple candidate queues, wherein the multiple network evaluation metrics in the candidate queues are sorted from earliest to latest according to the sampling time, and at least two of the candidate queues have different set lengths, and the set length of the candidate queue is the maximum number of metrics allowed to be stored in the candidate queue. The evaluation module is configured to evaluate the target network based on multiple network evaluation metrics in the target queue if the number of network evaluation metrics in the target queue reaches a set length of the target queue, wherein the multiple candidate queues include the target queue.

12. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured as follows: The steps of implementing the method according to any one of claims 1-10.

13. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method described in any one of claims 1-10.

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