Method and device for detecting health of a bearer network, electronic equipment and storage medium

Abstract

The application provides a method and device for detecting the health degree of a bearing network, electronic equipment and a storage medium. The method comprises: obtaining a peak time of a bearing network service flow in each first time period and the occurrence frequency of the peak time; constructing a normal distribution graph according to the peak time and the occurrence frequency, and determining a detection time window according to the normal distribution graph; collecting the health degree parameters of the bearing network in the detection time window according to a preset time interval, and outputting the health information of the bearing network according to the health degree parameters. That is, the embodiment of the application determines the health detection time window of the bearing network according to the peak time and the occurrence frequency of the bearing network service flow, and detects the health degree of the bearing network only in the health detection time window, thereby effectively detecting the health degree of the bearing network and reducing the device flow load and storage resource consumption caused by the health degree detection.

Description

TECHNICAL FIELD

[0001] The present application relates to network communication technology, and in particular to a method and device for detecting health degree of a bearer network, an electronic device and a storage medium. BACKGROUND

[0002] The detection of the health degree of the bearer network can reflect whether the network device resources and bandwidth occupancy of the bearer network are sufficient, thereby better reminding the operation and maintenance department to plan the expansion of the network device in advance.

[0003] In the existing detection method of the health degree of the bearer network, high-frequency or low-frequency health detection is usually adopted in the whole period. However, the high-frequency health detection in the whole period will increase the load of the bearer and occupy more network bandwidth resources. In particular, in the case of a large network scale and a large number of devices, network congestion will be caused, and a large amount of health detection data will also occupy a large amount of storage resources. The low-frequency health detection in the whole period may not accurately reflect the traffic peak of the network device, and the effect of the health detection is poor.

[0004] In summary, the prior art has the problem of how to effectively detect the health condition of the bearer network device while reducing the device traffic load and storage resource consumption caused by health detection. SUMMARY

[0005] To solve the above problems, the present application provides a method and device for detecting the health degree of a bearer network, an electronic device and a storage medium.

[0006] In a first aspect, the present application provides a method for detecting the health degree of a bearer network, comprising: obtaining a peak time of bearer network traffic in each of a plurality of first time periods and an occurrence frequency of the peak time; constructing a normal distribution graph according to the peak time and the occurrence frequency, and determining a detection time window according to the normal distribution graph; collecting health degree parameters of the bearer network in the detection time window according to a preset time interval, and outputting health information of the bearer network according to the health degree parameters.

[0007] In other optional embodiments, the obtaining of the peak time of the bearer network traffic in each of the plurality of first time periods and the occurrence frequency of the peak time comprises: obtaining a distribution graph of the bearer network traffic varying with time in each of the plurality of first time periods, and dividing each of the plurality of first time periods into a plurality of second time periods on average; determining a second time period in which the peak time is located in the distribution graph corresponding to each of the plurality of first time periods, and counting the occurrence frequency of the second time period in which the peak time is located.

[0008] In other optional embodiments, the determining the detection time window according to the normal distribution diagram comprises: determining a first probability interval of the normal distribution diagram according to a service type of the bearer network; and determining a corresponding detection time window according to the first probability interval.

[0009] In other optional embodiments, the collecting the health degree parameters of the bearer network at preset time intervals within the detection time window comprises: setting different probability gradient thresholds within the detection time window; setting different preset time intervals according to the different probability gradient thresholds, and collecting the health degree parameters of the bearer network at the different preset time intervals.

[0010] In other optional embodiments, the bearer network service flow is determined by service flow data of a downlink device of the bearer network.

[0011] In other optional embodiments, the method further comprises: performing error checking on the detection time window.

[0012] In other optional embodiments, the error checking on the detection time window comprises: obtaining a distribution diagram of the bearer network service flow changing over time in any one of the first time periods other than the plurality of first time periods; and if a peak time in the distribution diagram in the any one of the first time periods is located outside the detection time window, taking the any one of the first time periods as a starting point, re-executing the steps of obtaining the peak time of the bearer network service flow in each of the plurality of first time periods and the occurrence frequency of the peak time.

[0013] In other optional embodiments, the method further comprises: if the peak time in the distribution diagram in the any one of the first time periods is located within the detection time window, constructing a normal distribution diagram according to the peak times and the occurrence frequencies of the plurality of first time periods and the peak time and the occurrence frequency in the any one of the first time periods.

[0014] In other optional embodiments, the health degree parameters at least comprise one of the following: a bearer number of the bearer network and a bandwidth occupancy rate of the bearer network.

[0015] In other optional embodiments, the method further comprises: judging whether the health degree parameters reach a preset threshold; if yes, outputting a bearer network expansion instruction, and performing expansion processing on the bearer network according to the bearer network expansion instruction.

[0016] In a second aspect, the present application provides a device for detecting health degree of a bearer network, comprising: an acquisition module, configured to acquire a peak time of a traffic flow of the bearer network in each of a plurality of first time periods and a frequency of occurrence of the peak time; a determination module, configured to construct a normal distribution graph according to the peak time and the frequency of occurrence, and determine a detection time window according to the normal distribution graph; and an output module, configured to collect a health degree parameter of the bearer network in the detection time window according to a preset time interval, and output health information of the bearer network according to the health degree parameter.

[0017] In a third aspect, the present application provides an electronic device, comprising: at least one processor and a memory; the memory stores computer-executable instructions; and the at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the method according to any one of the first aspect.

[0018] In a fourth aspect, the present application provides a readable storage medium, wherein the readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method according to any one of the first aspect is implemented.

[0019] The present application provides a method and device for detecting health degree of a bearer network, an electronic device and a storage medium, wherein the method comprises: acquiring a peak time of a traffic flow of the bearer network in each of a plurality of first time periods and a frequency of occurrence of the peak time; constructing a normal distribution graph according to the peak time and the frequency of occurrence, and determining a detection time window according to the normal distribution graph; collecting a health degree parameter of the bearer network in the detection time window according to a preset time interval, and outputting health information of the bearer network according to the health degree parameter; that is, according to the peak time and the frequency of occurrence of the traffic flow of the bearer network, the present application determines a health detection time window of the bearer network, and by performing health degree detection on the bearer network only in the health detection time window, the present application realizes effective detection of the health degree of the bearer network while reducing the consumption of device traffic load and storage resources caused by health degree detection. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A schematic diagram of a system architecture based on the present application;

[0021] Figure 2 A flowchart of a method for detecting health degree of a bearer network provided by the present application;

[0022] Figure 3 A curve distribution graph of traffic flow of a bearer network varying with time in a day provided by the present application;

[0023] Figure 4 A histogram distribution graph of a peak time and a frequency of occurrence of a traffic flow of a bearer network provided by the present application;

[0024] Figure 5 For Figure 4 The histogram corresponding to the time conversion is carried out;

[0025] Figure 6 For the normal distribution provided by the application;

[0026] Figure 7 For another flowchart of the detection method of the health degree of the bearer network provided by the application;

[0027] Figure 8 For the structure diagram of the detection device of the health degree of the bearer network provided by the application;

[0028] Figure 9 For the hardware structure diagram of the electronic device provided by the application. Specific embodiments

[0029] In order to make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application.

[0030] In the case of increasing network size and increasing number of network devices of the existing network, the health detection of the network device becomes a very important topic. The health degree detection of the bearer network can be used to prompt whether the device resource of the operator is insufficient, whether the indexes such as the number of devices and the bandwidth occupancy rate of the device at the peak value reach the threshold value of expansion, so as to better remind the operation and maintenance department to plan the expansion of the network device in advance.

[0031] In the existing detection method of the health degree of the bearer network, high-frequency or low-frequency health detection is usually adopted in the whole period. However, the high-frequency health detection in the whole period will increase the bearing load and occupy more network bandwidth resources, especially in the case of large network size and large number of devices, which will cause network congestion, and a large amount of health detection data will also occupy a large amount of storage resources; the low-frequency health detection in the whole period may not accurately reflect the traffic peak value of the network device, and the effect of the health detection is poor. That is to say, the existing technology has the problem of how to effectively detect the health condition of the bearer network device while reducing the device traffic load and storage resource consumption caused by health detection.

[0032] In view of the above problems, the technical concept of the application is that: by analyzing the traffic variation trend of the bearer network, the time window corresponding to the traffic peak value of the bearer network device is accurately calculated, and then the health of the bearer network device is detected according to the appropriate health detection time interval set by the user in the time window.

[0033] Figure 1A schematic diagram of a system architecture based on which the present application is based is shown in Figure 1 One of the system architectures based on which the present application is based is a system architecture based on an operator service scenario, which includes a health detection server 1, a service gateway server 2, a bearer network network device 3, and a bearer network downlink device 4. The service traffic of the bearer network network device 3 mainly comes from the bearer network downlink device 4 connected thereto, and the bearer network downlink device 4 includes an evolved node B (eNodeB) device (LTE network base station device), a radio network controller (RNC), and a base station controller (BSC) device (3G network base station device).

[0034] In the system architecture, the service network management server 2 obtains the traffic statistics of the bearer network downlink device 4 every day, and sends the traffic statistics to the health detection server 1, so that the health detection server 1 performs the method in each of the embodiments below, thereby realizing health detection of the bearer network network device 3.

[0035] In a first aspect, the embodiment of the present application provides a method for detecting health degree of a bearer network, Figure 2 A flowchart of the method for detecting health degree of a bearer network provided by the present application is shown in

[0036] As shown in Figure 2 The method for detecting health degree of a bearer network includes the following steps.

[0037] Step 101: Obtain a peak time of bearer network service traffic in each first time period and a frequency of occurrence of the peak time.

[0038] Preferably, the first time period is one day, and the bearer network service traffic is mainly determined by the service traffic data of the bearer network downlink device. In this step, the execution subject of the embodiment, such as the health detection server 1 shown in Figure 1 may obtain the traffic data of the bearer network downlink device of each day in a certain number of days from the service gateway server 2 (in order to ensure the effectiveness of the traffic data, the traffic data of more than twelve weeks is generally collected); after the collection is completed, the peak time of the bearer network service traffic of each day can be determined according to the traffic data, and the number of occurrences of the peak time can be determined.

[0039] As an optional embodiment, one implementation of step 101 is as follows: obtaining a distribution diagram of the time-varying of the bearer network service traffic in each of a plurality of first time periods, and dividing each of the first time periods into a plurality of second time periods; determining the second time period in which the peak time in the distribution diagram corresponding to each of the first time periods is located, and counting the occurrence frequency of the second time period in which the peak time is located.

[0040] Specifically, the bearer network service traffic data can be collected at a preset interval (for example, 1 minute), so that a curve diagram of the time-varying of the bearer network service traffic in a day can be obtained, as shown in Figure 3 The curve distribution diagram of the time-varying of the bearer network service traffic in a day provided by the present application needs to be explained that the obtained bearer service traffic data is the superposition of the traffic of the eNodeB, RNC and BSC devices in the bearer network downlink equipment. From Figure 3 It can be seen that the peak time of the bearer network service traffic is about 19:50 in this day, and the same is true for the peak time of each day in a certain number of days, so that the peak time of each day in a certain number of days can be obtained. The set of peak times in a certain number of days can be called a sample population Z. Then, a day is divided into a plurality of second time periods T (for example, 5 minutes). Preferably, in order to improve the division efficiency, the peak time period in which the second time period T needs to be divided can be determined according to the time distribution range of the set of peak times Z (for example, 19:20-20:00). That is, the division of every 5 minutes can be performed only in the time range of 19:20-20:00. For example, taking the earliest time T0 (for example, 19:20) in which the peak time appears in the set Z as the zero point, the first segment is T0-2T to T0-T, the second segment is T0-T to T0, and so on, so that all the times in the set Z are covered, and then the frequency statistics is performed according to the divided time periods. For reference, Figure 4 Figure 4 The present application provides a histogram distribution diagram of the peak time and occurrence frequency of the bearer network service traffic.

[0041] Step 102, constructing a normal distribution diagram according to the peak time and occurrence frequency, and determining a detection time window according to the normal distribution diagram.

[0042] Specifically, a normal distribution curve diagram can be constructed according to the peak time and occurrence frequency. Preferably, in order to facilitate calculation, the time can be converted into a group number according to the previously divided time periods, for example, the time period group number of T0 to T0+T is 0, and the converted coordinate is 0, and so on. For reference, Figure 5 Figure 5 Figure 4 The histogram distribution diagram corresponding to the time conversion, according to Figure 5 ​​​The mean value μ of the sample population Z and the standard deviation σ of the sample population Z can be determined conveniently, and finally the normal distribution function of the sample population Z is obtained according to the normal distribution formula shown in formula (1):

[0043]

[0044] For example, when the mean value of the sample Z is 4.771428571 and the standard deviation is 1.758013721, the normal distribution corresponding to the sample population Z can be obtained according to formula (1) and shown in formula (2) and (3):

[0045]

[0046]

[0047] According to the above formula (2) and (3), the normal distribution curve corresponding to the sample population Z can be drawn, and the group number is converted into the corresponding time period, for example, 0 is converted into T0 to T0+T time, which can be referred to as shown in Figure 6 Figure 6 The normal distribution graph provided by the application.

[0048] After the normal distribution graph shown in Figure 6 is obtained, the detection time window can be determined according to the normal distribution graph. Optionally, one implementation of determining the detection time window according to the normal distribution graph is as follows: determining the first probability interval of the normal distribution graph according to the service type of the bearer network; and determining the corresponding detection time window according to the first probability interval.

[0049] Specifically, different service types of the bearer network correspond to different confidence intervals (i.e. probability intervals of the normal distribution), or the corresponding confidence intervals can be set according to the user's demand, for example, set to 99%, 80%, etc., and then the corresponding detection time window range can be determined according to the determined confidence interval, for example, when the confidence is 99%, the corresponding detection time window is 19:10-20:00, and when the confidence is 80%, the corresponding detection time window is 19:25-19:50.

[0050] In addition, it should be noted that if the start time and end time of the determined detection time window are not the start and end points of the previously divided time periods (T0-nT), the start and end points of the time periods need to be expanded to earlier or later time periods in order to Figure 6 ​For example, when the credibility is set as 0.8, if the start and end points on the corresponding curve are between 19:25-19:30 and 19:45-19:50, in order to comprehensiveness of the time window coverage, the embodiment takes 19:25 as the starting time of the time window and 19:50 as the ending time of the time window.

[0051] Step 103: collecting the health degree parameters of the bearer network according to a preset time interval within the detection time window, and outputting the health information of the bearer network according to the health degree parameters.

[0052] Specifically, after the detection time window is determined, the health detection instruction is sent to the bearer network according to the preset time interval, so that the bearer network sends the collected health degree parameters to the health detection server, and the health detection server outputs the health information of the bearer network according to the health degree parameters.

[0053] Optionally, the collecting the health degree parameters of the bearer network according to a preset time interval within the detection time window in step 103 comprises: setting different probability gradient thresholds within the detection time window; setting different preset time intervals according to the different probability gradient thresholds, and collecting the health degree parameters of the bearer network according to the different preset time intervals.

[0054] Specifically, the health detection time interval can be dynamically set according to the different probabilities of the traffic peak time, for example, in the high probability interval, the health detection time interval should be low, that is, the health detection should be performed at a high frequency; in the low probability interval, the health detection time interval should be high, and the health detection should be performed at a low frequency. Still taking the example of FIG. 2, the health detection time interval should be 20 seconds in the interval with a probability greater than 0.2, the health detection time interval should be 40 seconds in the interval with a probability greater than 0.15 and less than 0.2, and the health detection time interval should be 60 seconds in the interval with a probability greater than 0.1 and less than 0.15. Figure 6 For example, the first gradient threshold can be set as 0.2, that is, in the time period with a probability greater than 0.2, the preset time interval is T z = 20 seconds; in the time period with a probability greater than 0.15 and less than 0.2, the preset time interval can be set as 2T z = 40 seconds; and in the time period with a probability greater than 0.1 and less than 0.15, the preset time interval can be set as 3T z = 60 seconds.

[0055] Optionally, after step 103, the method further comprises: adjusting the devices in the bearer network or adjusting the parameters of the devices in the bearer network according to the health information of the bearer network.

[0056] As an optional embodiment, the health degree parameters at least comprise one of the following: the number of bearers of the bearer network, and the bandwidth occupancy rate of the bearer network; the method further comprises: judging whether the health degree parameters reach a preset threshold; if yes, outputting a bearer network expansion instruction, and performing expansion processing on the bearer network according to the bearer network expansion instruction.

[0057] Specifically, the health detection server collects parameters such as the number of bearers of the bearer network and the bandwidth occupancy rate of the bearer network, and then judges whether the number of bearers or the bandwidth occupancy rate reaches a preset threshold value requiring expansion. If yes, the health detection server outputs an expansion instruction to perform expansion processing on the bearer network. If no, it means that the bearer network resources are sufficient and do not need to be expanded.

[0058] The method for detecting the health degree of the bearer network provided in the embodiment of the application comprises the following steps: acquiring a peak time of a bearer network service flow in each of a plurality of first time periods and a frequency of occurrence of the peak time; constructing a normal distribution graph according to the peak time and the frequency of occurrence, and determining a detection time window according to the normal distribution graph; collecting a health degree parameter of the bearer network in the detection time window according to a preset time interval, and outputting health information of the bearer network according to the health degree parameter. That is, the embodiment of the application determines the health detection time window of the bearer network according to the peak time of the bearer network service flow and the frequency of occurrence, and detects the health degree of the bearer network only in the health detection time window, which reduces the processing pressure and flow load of the health detection server compared with the high-frequency health detection all day long in the prior art, and is more accurate in reflecting the flow peak health effect of the equipment of the bearer network compared with the low-frequency health detection all day long in the prior art. In summary, the embodiment effectively detects the health degree of the bearer network while reducing the flow load and storage resource consumption of the equipment caused by the health degree detection.

[0059] In combination with the foregoing embodiments, Figure 7 The flowchart of another method for detecting the health degree of a bearer network provided in the application is shown in FIG. 5. Figure 7 The method for detecting the health degree of the bearer network comprises the following steps:

[0060] Step 201: acquiring a peak time of a bearer network service flow in each of a plurality of first time periods and a frequency of occurrence of the peak time.

[0061] Step 202: constructing a normal distribution graph according to the peak time and the frequency of occurrence, and determining a detection time window according to the normal distribution graph.

[0062] Step 203: performing error checking on the detection time window.

[0063] Step 204: collecting a health degree parameter of the bearer network in the detection time window according to a preset time interval, and outputting health information of the bearer network according to the health degree parameter.

[0064] The steps 201, 202 and 204 in the embodiment are similar to the implementation modes of the steps 101, 102 and 103 in the foregoing embodiments, and thus are not described herein.

[0065] Different from the foregoing embodiments, in order to further improve the accuracy of the detection time window, in the present embodiment, after the detection time window is determined according to the normal distribution diagram, error checking is performed on the detection time window.

[0066] Specifically, in the network operator service scenario shown in FIG. 1, the statistical bearer network service traffic comes from the traffic input by the bearer network downlink equipment to the bearer network equipment, but the traffic of the bearer network downlink equipment is only the main traffic source of the bearer network equipment, some small proportion of traffic sources are not counted, and some factors affecting the peak traffic time, such as the networking element structure and quantity change, user service behavior, etc., so it is necessary to timely check and correct the error of the health detection time window. Figure 1

[0067] As an optional embodiment, one implementation of step 203 is as follows: obtaining a distribution diagram of the change of the bearer network service traffic with time in any one of the first time periods other than the plurality of first time periods; if the peak time in the distribution diagram in the any one of the first time periods is located outside the detection time window, re-executing the steps of obtaining the peak time of the bearer network service traffic in each of the plurality of first time periods and the occurrence frequency of the peak time, with the any one of the first time periods as the starting point; if the peak time in the distribution diagram in the any one of the first time periods is located within the detection time window, constructing a normal distribution diagram according to the peak time and occurrence frequency of the plurality of first time periods and the peak time and occurrence frequency in the any one of the first time periods.

[0068] Specifically, any day other than a certain number of days can be selected, or a time interval D can be set, and every D days, a day is randomly selected to perform a full-time interval device health detection of 1 minute, and the peak traffic occurrence time Tc is determined according to the obtained traffic change curve. At this time, two situations will occur:

[0069] The first situation: the peak time Tc obtained by sampling is located outside the user minimum confidence probability interval of the normal distribution diagram (i.e. outside the determined detection time window), then the previous detection time window can be discarded, and the detection time window prediction is re-performed with the day as the starting point.

[0070] The second situation: the peak time Tc obtained by sampling is located within the user minimum confidence probability interval (i.e. within the determined detection time window), then the sampling result can be directly added to the sample population Z, and the prediction of the detection time window is continued.

[0071] ​For example, the user's minimum credibility is set to 90%, the pre-acquired detection time window is 19:20-19:55, if the peak time Tc obtained by random sampling is earlier than 19:20 or later than 19:55, the previously predicted detection time window is discarded, and the detection time window prediction is re-performed; if Tc is between 19:20 and 19:55, the result of this sampling is added to the sample, and the detection time window prediction is continued. It should be noted that the embodiment will add the new sample obtained to the sample population Z in time, so that the time prediction of the detection time window is more real-time.

[0072] On the basis of the foregoing embodiment, the detection time window is error-checked, so that the determined detection time window is more accurate, the effective detection of the health degree of the bearer network is further improved, and the device flow load and storage resource consumption caused by the health degree detection are reduced.

[0073] In a second aspect, the embodiment of the present application provides a detection device for the health degree of a bearer network, Figure 8 A structural schematic diagram of the detection device for the health degree of a bearer network provided by the present application is shown in the figure, and the detection device for the health degree of a bearer network comprises: Figure 8 As shown in the figure, the detection device for the health degree of a bearer network comprises:

[0074] The acquisition module 10 is configured to acquire a peak time of a bearer network service flow in each first time period in a plurality of first time periods and an occurrence frequency of the peak time; the determination module 20 is configured to construct a normal distribution graph according to the peak time and the occurrence frequency and determine a detection time window according to the normal distribution graph; and the output module 30 is configured to collect a health degree parameter of the bearer network in the detection time window according to a preset time interval and output health information of the bearer network according to the health degree parameter.

[0075] In other optional embodiments, the acquisition module 10 is specifically configured to acquire a distribution graph of a change of a bearer network service flow with time in each first time period in a plurality of first time periods, and divide each first time period into a plurality of second time periods on average; determine a second time period in which a peak time in the corresponding distribution graph of each first time period is located, and count an occurrence frequency of the second time period in which the peak time is located.

[0076] In other optional embodiments, the determination module 20 is specifically configured to determine a first probability interval of the normal distribution graph according to a service type of the bearer network; and determine a corresponding detection time window according to the first probability interval.

[0077] In other optional embodiments, the output module 30 is specifically configured to: set different probability gradient thresholds in the detection time window; set different preset time intervals according to the different probability gradient thresholds, and collect the health degree parameters of the bearer network according to the different preset time intervals.

[0078] In other optional embodiments, the bearer network service traffic is determined by service traffic data of a downlink device of the bearer network.

[0079] In other optional embodiments, the device further comprises a verification module 40, which is configured to: perform error verification on the detection time window.

[0080] In other optional embodiments, the verification module 40 is specifically configured to: obtain a distribution graph of the bearer network service traffic changing over time in any one of the first time periods other than the plurality of first time periods; if a peak time in the distribution graph in the any one of the first time periods is located outside the detection time window, take the any one of the first time periods as a starting point, and re-perform the steps of obtaining the peak time of the bearer network service traffic in each of the plurality of first time periods and the occurrence frequency of the peak time.

[0081] In other optional embodiments, the verification module 40 is further specifically configured to: if the peak time in the distribution graph in the any one of the first time periods is located within the detection time window, construct a normal distribution graph according to the peak times and the occurrence frequencies of the plurality of first time periods and the peak time and the occurrence frequency in the any one of the first time periods.

[0082] In other optional embodiments, the health degree parameters at least include one of the following: a bearer number of the bearer network device, a bandwidth occupancy rate of the bearer network.

[0083] In other optional embodiments, the output module 30 is further configured to: determine whether the health degree parameters reach a preset threshold; if yes, output a bearer network expansion instruction, and perform expansion processing on the bearer network according to the bearer network expansion instruction.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described bearer network health degree detection device and the corresponding benefits can be referred to the corresponding process in the foregoing method embodiments, and will not be described here.

[0085] The application provides a device for detecting the health degree of a bearing network, comprising an acquisition module, a determination module and an output module.

[0086] In a third aspect, the application provides an electronic device, Figure 9 The hardware structure of the electronic device provided by the application is shown in Figure 9 The hardware structure of the electronic device provided by the application is shown in

[0087] The hardware structure of the electronic device provided by the application is shown in

[0088] In the implementation process, the at least one processor 901 executes the computer execution instructions stored in the memory 902, so that the at least one processor 901 executes the above-mentioned method for detecting the health degree of a bearing network, wherein the processor 901 and the memory 902 are connected through the bus 903.

[0089] The specific implementation process of the processor 901 can refer to the above-mentioned method embodiments, which have similar implementation principles and technical effects, and will not be described here again.

[0090] In the above-mentioned Figure 9 In the above-mentioned

[0091] The memory can contain a high-speed RAM memory, and can also include a non-volatile storage NVM, for example, at least one disk memory.

[0092] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0093] In a fourth aspect, the present application further provides a readable storage medium, wherein the readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method for detecting the health of a bearer network is implemented.

[0094] The readable storage medium described above can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0095] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0096] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the foregoing storage medium includes ROM, RAM, magnetic disk or optical disk and various storage medium that can store program codes.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting the health of a bearer network, characterized in that, include: Obtain the peak time of the bearer network service traffic within each of the multiple first time periods, and the frequency of occurrence of the peak time; A normal distribution map is constructed based on the peak time and frequency of occurrence, and the detection time window is determined based on the normal distribution map; Within the detection time window, different probability gradient thresholds are set; Different preset time intervals are set according to different probability gradient thresholds, and the health parameters of the bearer network are collected according to different preset time intervals; The health information of the bearer network is output based on the health parameters.

2. The method according to claim 1, characterized in that, The step of obtaining the peak time of the bearer network service traffic within each of the multiple first time periods, and the frequency of occurrence of the peak time, includes: Obtain the distribution map of the bearer network service traffic over time in each of the multiple first time periods, and divide each of the first time periods into multiple second time periods on an average basis; Determine the second time period in which the peak moment in the distribution map corresponds to each first time period, and count the frequency of occurrence of the second time period in which the peak moment is located.

3. The method according to claim 1, characterized in that, Determining the detection time window based on the normal distribution graph includes: The first probability interval of the normal distribution graph is determined based on the service type of the bearer network. The corresponding detection time window is determined based on the first probability interval.

4. The method according to any one of claims 1-3, characterized in that, The service traffic of the bearer network is determined by the service traffic data of the downstream devices of the bearer network.

5. The method according to claim 4, characterized in that, The method further includes: performing error verification on the detection time window.

6. The method according to claim 5, characterized in that, The error verification of the detection time window includes: Obtain a distribution map of the bearer network service traffic over time within any first time period other than the plurality of first time periods; If the peak time in the distribution map within any of the first time periods is outside the detection time window, then starting from any of the first time periods, the steps of obtaining the peak time of the bearer network service traffic in each of the multiple first time periods and the frequency of occurrence of the peak time are re-executed.

7. The method according to claim 6, characterized in that, The method further includes: if the peak time in the distribution map within any first time period is located within the detection time window, then a normal distribution map is constructed based on the peak times and frequencies of occurrence of the multiple first time periods, as well as the peak times and frequencies of occurrence within any first time period.

8. The method according to any one of claims 1-3, characterized in that, The health parameter includes at least one of the following: The number of carriers in the bearer network and the bandwidth utilization rate of the bearer network.

9. The method according to claim 8, characterized in that, The method further includes: Determine whether the health parameter has reached a preset threshold; If so, output a bearer network expansion command and perform expansion processing on the bearer network according to the bearer network expansion command.

10. A device for detecting the health of a network, characterized in that, include: The acquisition module is used to acquire the peak time of the bearer network service traffic in each of the multiple first time periods, and the frequency of occurrence of the peak time. The determination module is used to construct a normal distribution map based on the peak time and frequency of occurrence, and to determine the detection time window based on the normal distribution map; The output module is used to set different probability gradient thresholds within the detection time window; set different preset time intervals according to the different probability gradient thresholds, and collect the health parameters of the carrier network according to the different preset time intervals; and output the health information of the carrier network according to the health parameters.

11. An electronic device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method as described in any one of claims 1 to 9.

12. A readable storage medium, characterized in that, The readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the method as described in any one of claims 1 to 9.

Images