A method and device for detecting the operation quality of public services for educational resources

Abstract

The present invention discloses a method and device for detecting the operation quality of an educational resource public service system, belonging to the technical field of information sharing. The method includes: using web crawler probing to perform regular access to the system at a fixed frequency and temporary access with random sampling; obtaining detection index quantities according to the access results; obtaining the operation quality characteristics of the educational resource public service system according to the detection index quantities, including regular operation quality characteristics and recent operation quality characteristics; adjusting the monitoring frequency level according to the original state results and operation quality characteristic results corresponding to the default monitoring frequency level, entering S1 and increasing the frequency of temporary access; analyzing the reasons for system anomalies according to the detection index quantities and operation quality characteristic results, and adding anomaly labels to the regular operation quality characteristics and recent operation quality characteristics respectively according to the anomaly reasons. The present invention can effectively detect and evaluate the operation quality of the educational resource public service cloud.

Description

Technical Field

[0001] The present invention belongs to the field of information sharing technology, and more specifically, relates to a method and device for detecting the operation quality of public services of educational resources. Background Art

[0002] With the development of society, how to provide a better educational environment has become a focus of public attention. Sharing educational resources is an important means to effectively improve the level and quality of teaching. With the development of information technology, cloud computing services have been applied to social education. This has enabled traditional independent service systems that target different users and contain different types of resources to interconnect and form a loose alliance system. This has built an open and flexible public service for educational resources, realizing the joint construction and sharing of high-quality educational resources.

[0003] At the same time, the quality assessment of public education resource services has also garnered widespread attention. Scientific and rational service quality assessment methods can promote the continuous improvement and enhancement of public education resource services, ensuring strong quality assurance for the services users enjoy. However, evaluating the quality of public education resource services remains challenging. First, due to the loosely organized structure of public education resource services, a comprehensive and holistic operational quality analysis within the system is difficult. Second, due to differences in the target audiences and resource types of each system, the temporal and spatial characteristics of their services vary. Overcoming these system differences and developing fair, comprehensive, and holistic operational quality analyses requires further attention and research. Summary of the Invention

[0004] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a method and device for detecting the operation quality of educational resource public services. Its purpose is to use a distributed crawler system independent of the educational resource public services to detect and evaluate the operation quality of each educational resource public service cloud system, thereby solving the technical problem of difficulty in detecting the operation quality of educational resource public services.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for detecting the operation quality of public education resource services is provided, comprising:

[0006] S1: Using a web crawler to perform regular visits to the public education resource system and random sampling temporary visits;

[0007] S2: Obtain detection indicators based on normalized access results and temporary access results, including: service duration ratio, anomaly ratio, maximum anomaly duration ratio, system average page response time, system page response level, regional average page response time, regional anomaly ratio, and access result confidence level.

[0008] S3: Based on the detected metric quantity, perform an operation quality characterization on the educational resource public service system to obtain an operation quality characterization result including a normalized operation quality characterization and a recent operation quality characterization;

[0009] S4: Adjust the monitoring frequency level according to the original status result corresponding to the default monitoring frequency level and the operation quality characterization result, enter S1, and increase the frequency of the temporary access;

[0010] S5: Analyze the abnormal reasons of the educational resource public service system according to the detected metric quantity in S2 and the operation quality characterization result in S3, and add abnormal labels to the normalized operation quality characterization and the recent operation quality characterization respectively according to the abnormal reasons.

[0011] In one embodiment, after S2, the method further includes:

[0012] Calculate an evaluation index according to the detected metric quantity to provide a reference for the operation quality evaluation of the educational resource public service system; the evaluation index includes: system service ability, system response level.

[0013] In one embodiment, S2 includes:

[0014] Within a preset time period, if the access results are abnormal states for more than 2 consecutive times, take the difference between the earliest recorded time and the latest recorded time as the duration of the abnormal state; then its maximum value is the maximum duration of the abnormal state;

[0015] The provided service duration ratio = (preset time period - duration of abnormal state) / length of time period * 100%;

[0016] The abnormal occurrence ratio = number of times with abnormal access results / total number of accesses * 100%;

[0017] The maximum duration ratio of abnormal state = maximum duration of abnormal state / length of time period * 100%;

[0018] If the access result of the system is a normal state, then the system average page response duration = average value of page response durations; if the access results of the system are all abnormal states, then the system average page response duration is 0;

[0019] The system page response rank is: rank of the system average page response duration within the IP place of origin / number of systems within the IP place of origin;

[0020] If the access result of the system is in a normal state, the average page response time of the region = the average value of the page response time; if the access results of the system are all in an abnormal state, the average page response time of the region is 0;

[0021] The proportion of abnormal times in the region is: the number of abnormal states within the IP location / the total number of accesses * 100%;

[0022] The confidence level of the access result = the number of abnormal states / the total number of accesses * 100%.

[0023] In one embodiment, the S3 includes:

[0024] Obtaining the normal operation quality characteristics of the education resource public service system based on the set of detection metric quantities obtained from all historical data;

[0025] Obtaining the recent operation quality characteristics based on the set of detection metric quantities obtained from the data within a recent time period, including: all contents of the long-term portrait and the deviation degree between the recent operation quality characteristics and the normal operation quality characteristics.

[0026] In one embodiment, the S3 includes:

[0027] The set of detection metric quantities includes: the overall system state, system type, average access speed, and status label;

[0028] The overall system state is: normal or abnormal;

[0029] The system type is: fully connected type, periodic maintenance type, holiday maintenance type, or other type;

[0030] The average access speed: the average access speed is the average value of the average page response time of the system;

[0031] The status label includes: the label time and the possible reasons for causing the system state.

[0032] In one embodiment, the abnormal labels in the S5 include: network fluctuation, network congestion, network interruption of the monitoring system, insufficient system capacity, system maintenance, system problems, and unknown reasons.

[0033] According to another aspect of the present invention, there is provided an operation quality detection device for an education resource public service system, which is used to execute the operation quality detection method, including:

[0034] A web crawler module, which is used to monitor the monitoring frequency level of the education resource public service system in real time,

[0035] A task scheduling module, which is used to increase temporary access in addition to normal access when the monitoring frequency level rises;

[0036] An analysis and processing module, which is used to obtain the detection index quantity, perform operation quality characteristics on the education resource public service system according to the detection index quantity, and obtain an operation quality characteristic result including normal operation quality characteristics and recent operation quality characteristics; the frequency of the temporary access is associated with the monitoring frequency level; it is also used to adjust the monitoring frequency level according to the original state result corresponding to the default monitoring frequency level and the operation quality characteristic result; analyze the abnormal reasons of the education resource public service system according to the detection index quantity and the operation quality characteristic result, and add abnormal labels to the normal operation quality characteristics and the recent operation quality characteristics respectively according to the abnormal reasons.

[0037] According to another aspect of the present invention, an operation quality detection system is provided, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the method are implemented.

[0038] Generally speaking, compared with the prior art through the above technical solutions conceived by the present invention, the following beneficial effects can be obtained: The solution based on web crawler probing can obtain the current state without affecting the normal service provision of the system, and solves the problems existing in the detection of service quality. Then perform operation quality characteristics on the system, combine the normal portrait result and the recent portrait result, calculate the dynamically changing monitoring frequency level, and feedback it to the monitoring system, so that the systems with abnormal performance are focused on, and the monitoring resources are better utilized. Make a preliminary inference on the abnormal reasons of the system, which is beneficial to subsequent problem diagnosis and analysis of the system. Description of the Drawings

[0039] Figure 1 It is a flowchart of an operation quality detection method for an education resource public service provided by the present invention;

[0040] Figure 2 It is a flowchart of obtaining an abnormal label provided by the present invention;

[0041] Figure 3 It is a system framework diagram of an operation quality detection device for an education resource public service provided by the present invention. Detailed Embodiments

[0042] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0043] As Figure 1 shown, the present invention provides a method for detecting the operation quality of public education resource services, including:

[0044] S1: Regularly access the public education resource service system at a fixed frequency and conduct temporary random sampling access using web crawler probing;

[0045] Specifically, when there is no historical data, the monitoring frequency level of all systems is defaulted to the lowest level 1. At a fixed frequency, randomly access the cloud system page at regular intervals to obtain its status information and page response time. The status information includes normal service provision, maintenance, inaccessible, etc. Among them, the maintenance state and the inaccessible state are collectively referred to as abnormal states. When the page is in an abnormal state, take a screenshot of the page as evidence. When the monitoring frequency level increases, in addition to regular access, temporary access is added, and the temporary access frequency changes with the monitoring frequency level.

[0046] Preferably, in step S1, the time of regularly accessing the cloud system should have a certain coverage. The data should cover 24 hours a day and seven days a week. Beneficial effects: The sampling data with relatively good time coverage is conducive to discovering the periodicity of system state changes, avoiding the contingency brought by sampling at fixed times, and making the subsequent data analysis results more scientific and reliable.

[0047] Among them, when there is no historical data, the monitoring frequency level of all systems is defaulted to the lowest level 1. Regularly access the cloud system page randomly at a frequency of once a day to obtain its status information and page response time. The access time should ensure that each moment has the possibility of being selected, which is conducive to subsequent data analysis. The status information includes normal service provision, maintenance, inaccessible, etc. Among them, the maintenance state and the inaccessible state are collectively referred to as abnormal states. When the page is in an abnormal state, take a screenshot of the page as evidence. When the monitoring frequency level increases, in addition to regular access, temporary access is added, and the temporary access frequency changes with the monitoring frequency level. The specific access frequency will be specifically described in the monitoring frequency level section.

[0048] S2: Obtain the detection metric quantities, including: proportion of service provision duration, proportion of abnormal occurrence times, proportion of maximum abnormal state duration, system average page response duration, system page response level, regional average page response duration, regional abnormal occurrence proportion, and access result confidence. The frequency of temporary access is associated with the monitoring frequency level.

[0049] Specifically, according to the normal access and temporary access status results obtained in S1, calculate the detection metric quantities, including but not limited to: proportion of service provision duration, proportion of abnormal occurrence times, proportion of maximum abnormal state duration, system average page response duration, system page response level, regional average page response duration, regional abnormal occurrence proportion, access result confidence, etc.

[0050] Preferably, the calculation of the detection metrics in step S2 is based on a certain period. For example, for the service provision duration, it can be calculated on a daily basis to obtain the daily proportion of service provision duration; it can also be statistically calculated on a weekly or monthly basis to obtain the weekly or monthly proportion of service provision duration. The choice of time period can be determined according to the actual situation or analysis requirements.

[0051] Beneficial effects: When the system provides services well externally, the monitoring frequency level is low, and the status data obtained from access is less, a longer time granularity can be considered, such as calculating the detection metric quantities on a monthly basis, making the results more objectively credible; while in the case of a higher system monitoring frequency level and more data volume, a shorter time period can be considered, such as days or weeks.

[0052] S3: Characterize the operation quality of the educational resource public service system according to the detection metric quantities, and obtain the operation quality characteristic results including normal operation quality characteristics and recent operation quality characteristics.

[0053] Specifically, in S3, according to the detection metric quantities obtained in S2, characterize the operation quality of the system. According to the time dimension of the data source, it can be divided into normal operation quality characteristics and recent operation quality characteristics. The calculation of the normal operation quality characteristics is based on the set of detection metric quantities obtained from all historical data, and the contents included are not limited to: overall system status, system type, average access speed, status labels, etc. The calculation of the recent operation quality characteristics is based on the data within a recent time period, which includes all the contents of the long-term portrait, and also includes the deviation degree between the recent operation quality characteristics and the normal operation quality characteristics.

[0054] S4: Adjust the monitoring frequency level based on the original status result and the operation quality characteristic result corresponding to the default monitoring frequency level. Specifically, in S4, adjust the monitoring frequency level of the system according to the original status result and the operation quality characteristic result obtained in step S3. The monitoring frequency level is divided into levels 1 to 5, which affect the temporary access frequency outside the normal access. The specific corresponding conditions and the temporary access frequency are as follows:

[0055] Level 1: The initial monitoring frequency level, without temporary access; when the system is detected to be in an abnormal state, the attention level will change to level 2;

[0056] Level 2: Add a randomly timed temporary access once each in the two time periods of 0:00 - 12:00 and 12:00 - 23:59 every day; when the overall status of the system in the recent operation quality characteristics becomes abnormal, the attention level will change to level 3;

[0057] Level 3: Starting from 0:00 every day, add a randomly timed temporary access every 6 hours, for a total of 4 times; when the overall status of the system in the recent operation quality characteristics becomes normal, the attention level will change to level 2; when the deviation degree is higher than the threshold, the attention level will change to level 4.

[0058] Level 4: Starting from 0:00 every day, add a randomly timed temporary access every 4 hours, for a total of 6 times; when the overall status of the system in the recent operation quality characteristics becomes normal, the attention level will change to level 2; when the deviation degree is lower than the threshold, the attention level will change to level 3; when the overall status of the system in the normal operation quality characteristics becomes abnormal, the attention level will change to level 5.

[0059] Level 5: Starting from 0:00 every day, add a randomly timed temporary access every 2 hours, for a total of 12 times; when the overall status of the system in the normal operation quality characteristics becomes normal, the attention level will change to level 4.

[0060] S5: Analyze the abnormal reasons of the education resource public service system based on the detection index quantity and the operation quality characteristic result, and add abnormal labels to the normal operation quality characteristic and the recent operation quality characteristic respectively according to the abnormal reasons.

[0061] Specifically, in S5, infer the abnormal reasons based on the detection index quantity in step S2 and the operation quality characteristic result in step S3, and add abnormal labels to the recent operation quality characteristic or the normal operation quality characteristic according to the specific abnormal reasons. The abnormal labels include but are not limited to: network fluctuation, network congestion, network interruption of the monitoring system, insufficient system capacity, system maintenance, system problems, and unknown reasons, etc.

[0062] In one embodiment, after S2, the method further includes:

[0063] Calculate the evaluation index based on the detected index quantity to provide a reference for evaluating the operation quality of the education resource public service system. The evaluation index includes: system service ability, system response level.

[0064] Specifically, in S6, calculate the evaluation index according to the detected index quantity obtained in S2 to provide a reference for the subsequent evaluation of the operation quality of the education resource public service. The evaluation index includes but is not limited to: system service ability, system response level. Among them, for system service ability: the indicators include the proportion of service-providing duration, the proportion of abnormal times, and the proportion of the maximum abnormal state duration. Determine the index weights according to the combined weighting method and use the linear weighted model for index calculation. For system response level: the indicators include the average page response duration of the system and the page response gear of the system. Determine the index weights according to the combined weighting method and use the linear weighted model for index calculation.

[0065] In one embodiment, S2 includes:

[0066] Within a preset time period, if the access results for more than 2 consecutive times are in the abnormal state, take the difference between the earliest recorded time and the latest recorded time as the abnormal state duration. Then its maximum value is the maximum abnormal state duration.

[0067] Proportion of service-providing duration = (preset time period - abnormal state duration) / time period length * 100%.

[0068] Proportion of abnormal times = number of times with abnormal access results / total number of accesses * 100%.

[0069] Proportion of the maximum abnormal state duration = maximum abnormal state duration / time period length * 100%.

[0070] If the access result of the system is in the normal state, then the average page response duration of the system = average value of the page response durations. If the access results of the system are all in the abnormal state, then the average page response duration of the system is 0.

[0071] The system page response gear is: ranking of the average page response duration of the system within the IP location / number of systems within the IP location.

[0072] If the access result of the system is in the normal state, then the regional average page response duration = average value of the page response durations. If the access results of the system are all in the abnormal state, then the regional average page response duration is 0;

[0073] Regional proportion of abnormal times is: number of times in the abnormal state within the IP location / total number of accesses * 100%;

[0074] Access result confidence level = number of times in the abnormal state / total number of accesses * 100%.

[0075] Among them, according to the normalized access and temporary access status results obtained in S1, detection metric quantities are calculated, including but not limited to: the proportion of service providing duration, the proportion of abnormal times, the proportion of the maximum abnormal status duration, the average system page response duration, the system page response gear, the average regional page response duration, the proportion of regional abnormal times, the confidence level of access results, etc. The specific calculation formulas are as follows:

[0076] Proportion of service providing duration: Within a certain time period, if the access results of more than 2 consecutive times are in abnormal status, take the earliest and the latest recording times, and the difference between them is the abnormal status duration; Proportion of service providing duration = (time period length - abnormal status duration) / time period length * 100%.

[0077] Proportion of abnormal times: Within a certain time period, for the access results of this system, Proportion of abnormal times = number of times with abnormal access results / total number of accesses * 100%

[0078] Proportion of the maximum abnormal status duration: Within a certain time period, if the access results of more than 2 consecutive times are in abnormal status, take the earliest and the latest recording times, and the difference between them is the abnormal status duration, and its maximum value is the maximum abnormal status duration; Proportion of the maximum abnormal status duration = maximum abnormal status duration / time period length * 100%. For example, taking a day as the time period, it is measured that the system is in the normal service providing state at 3:00 and 22:00 on that day; while it is in the abnormal state at 6:00, 8:00, 14:00, and 23:00, then it is considered that the maximum abnormal status duration on that day is 14:00 - 10:00 = 4 hours, and the proportion of the maximum abnormal status duration is 4 hours / 24 hours * 100% = 16.67%. If there are no access results with more than 2 consecutive abnormal times within the time period, then this value is considered 0.

[0079] Average system page response duration: Within a certain time period, for the access results of this system in the normal service providing state, Average system page response duration = average value of page response durations. If all the access results of the system are in abnormal status within this time period, then this value is considered 0.

[0080] Average regional page response duration: Within a certain time period, for the access results of all systems with the IP belonging to this region in the normal service providing state, Average regional page response duration = average value of page response durations. If all the access results of this region are in abnormal status within this time period, then this value is considered 0.

[0081] System page response gear: Ranking of average system page response duration within the IP belonging region / number of systems within the IP belonging region

[0082] Proportion of abnormal times in a region: For the access results of all systems in the IP's location within a certain time period, the proportion of abnormal times in a region = number of abnormal status times / total number of access times * 100%.

[0083] Confidence level of access results: For the access results of all systems within a certain time period, the confidence level of access results = number of abnormal status times / total number of access times * 100%.

[0084] In one embodiment, S3 includes:

[0085] Obtain the normal operation quality characteristics of the education resource public service system based on the set of detection metric quantities obtained from all historical data.

[0086] Obtain the recent operation quality characteristics based on the set of detection metric quantities obtained from the data within the most recent time period. It includes all the contents of the long-term portrait and the degree of deviation between the recent operation quality characteristics and the normal operation quality characteristics.

[0087] Specifically, the calculation of the recent operation quality characteristics is based on the data within the most recent time period, which includes all the contents of the long-term portrait and also includes the degree of deviation between the recent operation quality characteristics and the normal operation quality characteristics (hereinafter referred to as the degree of deviation). The time period is defined manually and can be 1 week, 1 month, etc.

[0088] The calculation methods of the overall system state, system type, average access speed, and status label are basically the same as those of the normal operation quality characteristics. It is worth mentioning that the threshold at this time is determined manually with reference to the results of the normal operation quality characteristics.

[0089] Degree of deviation: In the normal operation quality characteristics, based on all historical data, the average value A1 of the proportion of service-providing duration, the average value A2 of the proportion of abnormal times, and the average value A3 of the proportion of the maximum abnormal status duration can be calculated; in the recent operation quality characteristics, based on the data within the most recent time period, the average value B1 of the proportion of service-providing duration, the average value B2 of the proportion of abnormal times, and the average value B3 of the proportion of the maximum abnormal status duration can be calculated.

[0090] In one embodiment, in S3:

[0091] The set of detection metric quantities includes: the overall system state, system type, average access speed, and status label;

[0092] The overall system state is: normal or abnormal;

[0093] The system type is: fully connected type, periodic maintenance type, holiday maintenance type, or other type;

[0094] Average access speed: The average access speed is the mean value of the average page response time of the system;

[0095] The status label includes: label time and possible reasons for the system status.

[0096] Specifically, according to the detection index quantities obtained in S2, the operation quality characteristics of the system are carried out. According to the time dimension of the data source, it can be divided into normal operation quality characteristics and recent operation quality characteristics. The calculation of the normal operation quality characteristics is based on the set of detection index quantities obtained from all historical data, and the contents included include but are not limited to: system overall status, system type, average access speed, status label, etc. The specific calculation method is as follows:

[0097] System overall status: There are two types of system overall status, normal or abnormal. If the mean value of the access result confidence and the mean value of the service duration ratio are higher than the threshold, and the mean value of the abnormal frequency ratio and the mean value of the maximum abnormal status duration ratio are lower than the threshold, the system overall status is considered normal, otherwise it is considered abnormal. This threshold is determined manually with reference to all system performances. The system overall status is updated every time period.

[0098] System type: The system type is divided into three categories: fully connected type, periodic maintenance type, holiday maintenance type and other type. According to the data in the service duration ratio set, calculate the mean value and variance. If the mean value is 100%, the system type is the fully connected type; if the mean value is not 100% and the variance is less than the threshold, the system type is the periodic maintenance type; if the system does not meet the conditions of the fully connected type and the periodic maintenance type, the difference between the holiday data mean value and the weekday data mean value is used to measure the difference in system performance between holidays and weekdays. If the difference is higher than the threshold, it is considered to be the holiday maintenance type. If none of the above three types of conditions are met, the system is considered to be of other type.

[0099] Average access speed: The average access speed is the mean value of the average page response time of the system.

[0100] Status label: It includes label time and possible reasons for the system status, which will be described in detail in the subsequent steps.

[0101] In one of the embodiments, the abnormal labels in S5 include: network fluctuation, network congestion, monitoring system network interruption, system capacity shortage, system maintenance, system problems and unknown reasons.

[0102] S5. Based on the detection index quantity in step S2 and the operation quality characteristic results in step S3, speculate on the abnormal reasons, and add abnormal labels to the recent operation quality characteristics or normalized operation quality characteristics according to the specific abnormal reasons. The abnormal labels include but are not limited to: network fluctuation, network congestion, network interruption of the monitoring system, insufficient system capabilities, system maintenance, system problems, and unknown reasons, etc. The specific speculation method is as Figure 2 shown, and the label description and subsequent processing are as follows:

[0103] Network fluctuation: If the proportion of service providing duration of the system during specific dates, such as holidays like Spring Festival and National Day, is lower than the threshold, then add network fluctuation to the abnormal labels of the normalized operation quality characteristics; there is no subsequent processing step.

[0104] Network congestion: If the proportion of abnormal times corresponding to the region where the system IP belongs is higher than the threshold, then add network congestion to the abnormal labels of the recent operation quality characteristics; there is no subsequent processing step.

[0105] Insufficient system capabilities: If the average access speed in the normalized operation quality characteristics of the system increases by more than ε for three consecutive times, then add network congestion to the abnormal labels of the normalized operation quality characteristics; there is no subsequent processing step. ε is defined manually, for example, 20%.

[0106] Network interruption of the monitoring system: If the confidence level of the access result is lower than the threshold, then add network interruption of the monitoring system to the abnormal labels of the recent operation quality characteristics; there is no subsequent processing step.

[0107] System maintenance: If the proportion of service providing duration of the system is lower than the threshold and by identifying the screenshot of the abnormal state, it is found that the system is in the maintenance state, then add system maintenance to the abnormal labels of the recent operation quality characteristics; if the maintenance end time can be identified from the screenshot, then access the system once every half hour after the end moment of the maintenance. If the access result is a normal service providing state, the label content is changed to system maintenance (confirmed). If the access result is abnormal or the end time cannot be identified, the label content is changed to system maintenance (doubtful).

[0108] System problems: If the proportion of service providing duration of the system is lower than the threshold and by identifying the screenshot of the abnormal state, it is found that the system is not in the maintenance state, then add system problems to the abnormal labels of the recent operation quality characteristics; there is no subsequent processing step.

[0109] Unknown reasons: For reasons other than the above situations, add unknown reasons to the abnormal labels of the normalized operation quality characteristics and the recent operation quality characteristics; there is no subsequent processing step.

[0110] As Figure 3 shown, the present invention also provides an operation quality detection device for an educational resource public service system, which is used to execute the operation quality detection method, including:

[0111] A web crawler module for real-time monitoring of the monitoring frequency level of the education resource public service system;

[0112] Specifically, the web crawler module: uses a distributed web crawler to access the corresponding education resource public service cloud system, obtains the status information of the cloud system, and takes screenshots of the system pages that are unable to provide external services.

[0113] A task scheduling module for increasing temporary access in addition to regular access when the monitoring frequency level rises. Among them, regular access is carried out to ensure that the access frequency is appropriate and the access time coverage is wide without affecting the normal service of the public service cloud system; and when an abnormality in the system status is detected, the crawler module is scheduled to initiate temporary access according to the degree of abnormality.

[0114] An analysis and processing module for obtaining detection metric quantities, obtaining the operation quality characteristics of the education resource public service system based on the detection metric quantities, and obtaining an operation quality characteristic result including normal operation quality characteristics and recent operation quality characteristics. It is also used to adjust the monitoring frequency level according to the original status result and the operation quality characteristic result corresponding to the default monitoring frequency level. Analyze the abnormal reasons of the education resource public service system according to the detection metric quantities and the operation quality characteristic results, and add abnormal labels to the normal operation quality characteristics and recent operation quality characteristics respectively. The frequency of temporary access is associated with the monitoring frequency level.

[0115] Specifically, calculate the corresponding detection metric quantities according to the access result data; according to the time dimension, generate the normal operation quality characteristics of the system based on all historical data, and generate the recent operation quality characteristics of the system based on recent data. Calculate the degree of concern according to the original data, detection metric quantities and operation quality characteristic results, and feedback the degree of concern to the task scheduling module to increase temporary access; calculate an evaluation index based on the detection metric quantities for reference in the subsequent evaluation of the education resource public service quality.

[0116] According to another aspect of the present invention, a running quality detection system is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0117] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting the operation quality of public services for educational resources, characterized in that, Including: S1: Use web crawler probing to perform regular access to the educational resource public service system at a fixed frequency and temporary access with random sampling; S2: Obtain detection metric quantities based on the results of regular access and temporary access, including: proportion of service-providing duration, proportion of abnormal times, proportion of maximum abnormal status duration, average system page response duration, system page response grade, average regional page response duration, proportion of regional abnormal times, and access result confidence level; S3: Calculate the public service operation quality characteristics of the educational resource public service system based on the detection metric quantities, and obtain results including regular operation quality characteristics and recent operation quality characteristics; S4: Adjust the monitoring frequency level according to the original status result corresponding to the default monitoring frequency level and the operation quality characteristic result, enter S1, and increase the frequency of the temporary access; S5: Analyze the abnormal reasons of the educational resource public service according to the detection metric quantities in S2 and the public service operation quality characteristic results in S3, and add abnormal labels to the regular operation quality characteristics and the recent operation quality characteristics respectively according to the abnormal reasons; The S3 includes: calculating the public service operation quality characteristics of the educational resource public service system based on the set of detection metric quantities obtained from all historical data to obtain the regular operation quality characteristics; calculating the operation quality characteristics based on the set of detection metric quantities obtained from the data within a recent time period to obtain the recent operation quality characteristics, which includes: all contents of the regular operation quality characteristics and the deviation degree of the recent operation quality characteristics from the regular operation quality characteristics.

2. The operation quality detection method of the educational resource public service system according to claim 1, characterized in that After the S2, the method further includes: Calculating an evaluation index according to the detection metric quantities to provide a reference for the operation quality evaluation of the educational resource public service; the evaluation index includes: system service ability, system response level.

3. The operation quality detection method of the public service of educational resources according to claim 1, wherein The S2 includes: Within a preset time period, if the access results are abnormal states for more than 2 consecutive times, take the difference between the earliest recording time and the latest recording time as the abnormal state duration; then its maximum value is the maximum abnormal state duration; The proportion of service-providing duration = (preset time period - abnormal state duration) / time period length * 100%; The proportion of abnormal times = number of times with abnormal access results / total number of accesses * 100%; The proportion of maximum abnormal state duration = maximum abnormal state duration / time period length * 100%; If the access result of the system is a normal state, then the average system page response duration = average value of the page response durations; if the access results of the system are all abnormal states, then the average system page response duration is 0; The system page response grade is: ranking of the average system page response duration within the IP location / number of systems within the IP location; If the access result of the system is in a normal state, then the average page response time of the region = the average value of the page response times; if the access results of the system are all in abnormal states, then the average page response time of the region is 0; The proportion of abnormal times in the region is: the number of abnormal states within the IP location of origin / the total number of accesses * 100%; The confidence level of the access result = the number of abnormal states / the total number of accesses * 100%.

4. The method for detecting the operation quality of the public service of educational resources according to claim 1, wherein The S3 includes: The set of detection metric quantities includes: the overall system state, system type, average access speed, and status label; The overall system state is: normal or abnormal; The system type is: fully connected type, periodic maintenance type, holiday maintenance type, or other type; The average access speed: The average access speed is the mean of the average page response times of the system; The status label includes: label time and possible reasons for causing the system state.

5. The method for detecting the operation quality of the public service of educational resources according to claim 1, wherein The abnormal labels in the S5 include: network fluctuations, network congestion, network interruption of the monitoring system, insufficient system capabilities, system maintenance, system problems, and unknown reasons.

6. An operating quality detection device for public services of educational resources, characterized in that, For implementing the operation quality detection method according to any one of claims 1-5, it includes: A network crawler module, configured to perform regular access at a fixed frequency and temporary access by random sampling on the education resource public service system using network crawler probing; A task scheduling module, configured to increase temporary access in addition to regular access when the monitoring frequency level increases; the frequency of the temporary access is associated with the monitoring frequency level; An analysis and processing module, configured to obtain detection metric quantities according to regular access results and temporary access results, perform operation quality characterization on the education resource public service system according to the detection metric quantities, and obtain an operation quality characterization result including regular operation quality characteristics and recent operation quality characteristics; it is also configured to adjust the monitoring frequency level according to the original state result corresponding to the default monitoring frequency level and the operation quality characterization result; analyze the abnormal reasons of the education resource public service system according to the detection metric quantities and the operation quality characterization result, and add abnormal labels to the regular operation quality characteristics and the recent operation quality characteristics respectively according to the abnormal reasons.

7. An operation quality detection system, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

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