CN114625615A - Cloud ecological data monitoring system - Google Patents
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Abstract
The invention discloses a cloud ecological data monitoring system, which comprises a data input module, a data analysis block, an error analysis module and a data verification module, the data early warning module sends out early warning to the existing abnormal data risk, the safety of the data is greatly improved through the analysis of the triggering range and conditions, and the problems of interference caused by alarms in data monitoring and system blockage caused by abnormal data are solved.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to a cloud ecological data monitoring system.
Background
Cloud ecological data generated in a cloud ecological system can help an operator to provide better service and reflect the real states of products and users, operation decision is guided and service growth is driven by dividing the cloud ecological data, a data monitoring system for analyzing data abnormity is continuously improved along with continuous expansion of cloud ecological scale, in the prior art, the data abnormity is observed by monitoring and analyzing the cloud ecological data, and then early warning and warning are sent to the existing risks according to the analysis results of data acquisition, data mining and data analysis by data early warning, when the cloud ecological data has problems, the monitoring system quickly reflects and informs monitoring personnel at the first time, but the quantitative index of the cloud ecological data and the fluctuation range of the index are properly selected, and the data monitoring can only quickly find the large range of the data abnormity, the method has the advantages that specific problems cannot be solved accurately, if all links are split into fine particles and triggered in automatic monitoring, once one link is abnormal and gives an alarm, other indexes related to the fineness can be affected to give an alarm, so that too many indexes give an alarm, interference can be caused to monitoring personnel, data monitoring and data analysis are important links of operation, data monitoring is well done, the problem that bugs occur in products and influence users is reduced, and major accidents are reduced.
Disclosure of Invention
In view of the above situation and in order to overcome the defects of the prior art, an object of the present invention is to provide a cloud ecological data monitoring system, in which a data analysis module analyzes an early warning process in the data monitoring system to obtain a probability analysis result, the data analysis module obtains a task amount when data is abnormal according to a trigger range of data monitoring and a data tuple in the early warning process, and then obtains the probability analysis result according to the task amount, so as to avoid interference caused by a trigger condition for sending an early warning when the data is abnormal, thereby improving monitoring strength of cloud ecological data.
The cloud ecological data monitoring system comprises a data input module, a data analysis module, an error analysis module, a data verification module, a data early warning module, a cloud storage database, a cloud computing module and a data output module, wherein the data input module receives cloud ecological data and transmits the cloud ecological data to the cloud storage database, the data analysis module performs data analysis on the cloud ecological data by establishing a data analysis model to obtain an evaluation analysis result, the error analysis module performs error analysis according to the evaluation analysis result of the data analysis module, the data verification module compares the input cloud ecological data to detect data abnormity, and the data early warning module gives an early warning for the existing data abnormity risk;
the system management process specifically comprises the following steps:
1) the data input module is an input end of the cloud ecological data, the data input module preprocesses the cloud ecological data of the input data to obtain grade data, and the data input module performs data segmentation on the cloud ecological data to obtain different data streams S1，S2，S3，...，SNWherein N is the number of data streams, the data streams are composed of data tuples, and the tuples are recorded as
M is the number of tuples, different tuples are subjected to grade division to obtain grade number k and grade data, and the data input module sends the different grade data to the data analysis module;
2) the data analysis module receives different grade data and carries out evaluation analysis to obtain an evaluation analysis result, and the data analysis module establishes an evaluation analysis model according to the grade data, wherein the specific analysis process is as follows:
step 1, firstly, a data analysis module evaluates and analyzes data tuples in each classified grade data, and a characteristic subset of each data tuple in the same grade data is acquired to obtain ki i∈[0，n]The weight ω is obtained by analysis based on the subset of features1，ω2，ω3，...，ωnThe entropy of one level data is recorded as c ═ Σi∈nωikiComputing a correlation vector between feature subsets of feature tuples
ri＝cos(ki，kj) i，j∈[0，n]；
Step 2, the data analysis module analyzes the initial range to the cross triggering range of all data tuples in the level data, and records the initial range of data monitoring in the data monitoring as (f)1，f2，f3...fz) After the initial range is determined, the data in the data tuples are updated through acquisition, and the initial range f is analyzed according to the acquisition and processing period of the datai i∈[1，z]Carrying out probability analysis on the data tuples in the data set to obtain a probability analysis result, wherein the specific analysis process is as follows:
c＝∑i∈nωiki，
wherein Y (k) is the average alarm time delay of the data tuples at the k acquisition time, and when the alarm of data abnormality occurs, the alarm time delay generated by the alarm target is YtAnd omega is the sampling period of the sample,
r 'is the number of initial ranges in a classification level, R' is a classification index of the number of data tuples, one data tuple passes through the average input information entropy c of a data input module, a sampling period count k, a data tuple alarm rate I (k) at the acquisition time k, an alarm response rate O (k) at the acquisition time k, and an alarm error rate E (k) at the acquisition time k, Y (k) -Yt，
When data abnormity occurs, the monitoring system carries out interference analysis on a cross trigger area which generates interference in an alarm trigger area, and cross trigger processed by the system is carried outA, a is the number of weight changes, fi∩fjIs fiAnd fjK is the count of the sampling period from the system operation, and the ratio of the task amount in the cross-trigger area to the task amount in the non-cross-trigger area is recorded as the trigger error probability CiI represents the number of data streams;
step 3, the data analysis module analyzes the triggering error probabilities in all the alarm areas to obtain probability analysis results, and sends the probability analysis results to the error analysis module;
step 4, the error analysis module analyzes according to the probability analysis result and the cross trigger range to obtain an error analysis result, and the data analysis module sends the probability analysis result and the error analysis result to the data verification module;
3) the data verification module verifies the cloud ecological data according to the received probability analysis result and the error analysis result, when data abnormity occurs, the data verification module rapidly confirms the range of the data abnormity and conducts data abnormity alarm verification according to the confirmed range to obtain data early warning information, and meanwhile, the data verification module sends the data early warning information to the data early warning module;
4) and the data early warning module sends out early warning to monitoring personnel according to the data early warning information sent by the data verification module.
The data verification module in the step 3) determines the optimal self-adaptability by adopting a self-adaptive scheduling algorithm according to the data abnormal range and establishes a structured equation model according to the probability analysis result and the error analysis result to perform verification analysis on the cloud ecological data, wherein the analysis process is as follows:
w(t)＝(wmax-wmin)Ps(t)+wmin，
w (t) is a function of the fluctuation of the weights in a data stream over time, wmaxIs the maximum value of the weight fluctuation, wminAs a minimum value of the weight fluctuation, Ps(t) error rate of input time variation of data, and adaptation between all data streamsThe iteration of the weights between tuples analyzes the adaptation process,
wherein, FmaxIs the maximum value of the trigger range in the event of data anomalies in the data stream, FavgIs the average value of the trigger ranges, F' is the larger one of the cross-trigger ranges, K1，K2Is a normal number not greater than 1, the structured analysis process includes a factor analysis process and a path analysis process, and the weight represents the strength and error rate P of the factor analysis paths(t) represents a path analysis process.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages;
1. a data input module of the system classifies input cloud ecological data into level data comprising different data tuples, and a data analysis module analyzes an alarm triggering process of the cloud ecological data, firstly, the data analysis module analyzes the data tuples in each level data to obtain a correlation vector of a feature subset among the data tuples and a weight in the input, and the weight of the feature subset of the data tuples of the cloud ecological data can change along with the acquisition, the input and the processing of the cloud ecological data, so that the analysis and processing capacity of a monitoring system changes when data abnormity occurs, secondly, the data analysis module analyzes an initial range and a cross triggering range of triggering alarms in data monitoring when the data abnormity occurs to obtain a triggering error probability, and controls a triggering condition in the data monitoring by analyzing the triggering range in the triggering process, therefore, the problem of alarm interference caused by undersize triggering links is avoided.
2. The system comprises a data analysis module, a data verification module, a cloud ecological data monitoring module and a data analysis module, wherein the data analysis module analyzes the offending range, and the information entropy and the feature subset of the data change continuously in the continuous input process of the data, so that the weight change is caused.
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FIG. 1 is an overall block diagram of the system;
fig. 2 is an overall flow chart of the present system.
Detailed Description
The foregoing and other aspects, features and advantages of the invention will be apparent from the following more particular description of embodiments of the invention, as illustrated in the accompanying drawings in which reference is made to figures 1-2. The structural contents mentioned in the following embodiments are all referred to the attached drawings of the specification.
A cloud ecological data monitoring system comprises a data input module, a data analysis module, an error analysis module, a data check module, a data early warning module, a cloud storage database, a cloud computing module and a data output module, wherein the acquisition and data processing of cloud ecological data play an important role in a cloud ecological system, the operation decision and the service growth are guided through the analysis of the cloud ecological data, the whole system is stuck and broken when data abnormity occurs due to the huge data quantity of the cloud ecological data, the data monitoring system for analyzing the data abnormity in the prior art is continuously perfected, the monitoring system determines the range of a monitoring target through methods of single analysis, combined analysis, user scene analysis and modeling analysis, and then carries out early warning according to the monitoring range, but when the triggering range of an alarm is larger, the range which needs to be checked when the data abnormity is eliminated is larger, when the alarm triggering range is small, a tiny link triggers a data abnormity alarm to enable the alarm and the alarm to generate interference, the data input module receives cloud ecological data and transmits the cloud ecological data to the cloud storage database, the data analysis module performs data analysis on the cloud ecological data by establishing a data analysis model to obtain an evaluation analysis result, the error analysis module performs error analysis according to the evaluation analysis result of the data analysis module, the data verification module compares the input cloud ecological data to detect data abnormity, and the data early warning module gives an early warning for the existing data abnormity risk;
the system management process specifically comprises the following steps:
1) the data input module is an input end of the cloud ecological data, the data input module preprocesses the cloud ecological data of the input data to obtain grade data, and the data input module performs data segmentation on the cloud ecological data to obtain different data streams S1，S2，S3，...，SNWherein N is the number of data streams, the data streams are composed of data tuples, and the tuples are recorded as
M is the number of tuples, different tuples are subjected to grade division to obtain grade number k and grade data, and the data input module sends the different grade data to the data analysis module;
2) the data analysis module receives different grade data and carries out evaluation analysis to obtain an evaluation analysis result, and the data analysis module establishes an evaluation analysis model according to the grade data, wherein the specific analysis process is as follows:
step 1, firstly, a data analysis module evaluates and analyzes data tuples in each classified grade data, and a characteristic subset of each data tuple in the same grade data is acquired to obtain ki i∈[0，n]The weight ω is obtained by analysis based on the subset of features1，ω2，ω3，...，ωnThe entropy of one level data is recorded as c ═ Σi∈nωikiComputing a correlation vector between feature subsets of feature tuples
ri＝cos(ki，kj) i，j∈[0，n]；
Step 2, the data analysis module analyzes the initial range to the cross triggering range of all data tuples in the level data and monitors the dataThe initial range of data monitoring is noted as (f)1，f2，f3...fz) After the initial range is determined, the data in the data tuples are updated through acquisition, and the initial range f is analyzed according to the acquisition and processing period of the datai i∈[1，z]Carrying out probability analysis on the data tuples in the data set to obtain a probability analysis result, wherein the specific analysis process is as follows:
c＝∑i∈nωiki，
wherein Y (k) is the average alarm time delay of the data tuples at the acquisition time k, and when an alarm of data abnormity occurs, the alarm time delay generated by the alarm target is YtAnd omega is the sampling period of the sample,
r 'is the number of initial ranges in a classification level, R' is a classification index of the number of data tuples, one data tuple passes through the average input information entropy c of a data input module, a sampling period count k, a data tuple alarm rate I (k) at the acquisition time k, an alarm response rate O (k) at the acquisition time k, and an alarm error rate E (k) at the acquisition time k, Y (k) -Yt，
When data abnormity occurs, the monitoring system carries out interference analysis on a cross trigger area which generates interference in an alarm trigger area, and the task quantity Q (k) of cross trigger processed by the system is represented by a weight change number fi∩fjIs fiAnd fjData elements within the cross-trigger area ofThe total number of feature subsets of the group, K is the count of the sampling period from the beginning of system operation, and the ratio of the task quantities in the cross trigger area to the non-cross trigger area is recorded as the trigger error probability CiI represents the number of data streams;
step 3, the data analysis module analyzes the triggering error probabilities in all the alarm areas to obtain probability analysis results, and sends the probability analysis results to the error analysis module;
step 4, the error analysis module analyzes according to the probability analysis result and the cross triggering range to obtain an error analysis result, and the data analysis module sends the probability analysis result and the error analysis result to the data verification module;
3) the data verification module verifies the cloud ecological data according to the received probability analysis result and the error analysis result, when data abnormity occurs, the data verification module rapidly confirms the range of the data abnormity and conducts data abnormity alarm verification according to the confirmed range to obtain data early warning information, and meanwhile, the data verification module sends the data early warning information to the data early warning module;
4) and the data early warning module sends out early warning to monitoring personnel according to the data early warning information sent by the data verification module.
The data verification module in the step 3) determines the optimal self-adaptive degree by adopting a self-adaptive scheduling algorithm according to the data abnormal range and different data tuples, and a structured equation model is established according to the probability analysis result and the error analysis result to verify and analyze the cloud ecological data, wherein the analysis process is as follows:
w(t)＝(wmax-wmin)Ps(t)+wmin，
w (t) is a function of the fluctuation of the weights in a data stream over time, wmaxIs the maximum value of the weight fluctuation, wminAs a minimum value of the weight fluctuation, Ps(t) error rates of input time variations of the incoming data, and then analysis of the adaptation process based on iterations of weights between the adaptive tuples between all data streams,
wherein, FmaxIs the maximum value of the trigger range in the event of data anomalies in the data stream, FavgIs the average value of the trigger ranges, F' is the larger one of the cross-trigger ranges, K1，K2Is a normal number not greater than 1, the structured analysis process includes a factor analysis process and a path analysis process, and the weight represents the strength and error rate P of the factor analysis paths(t) represents a path analysis process.
The error analysis module is used for researching the characteristic subset of the data tuples according to the probability analysis result to obtain the change value of the weight, then analyzing the incidence relation of the data tuples in the initial range, the cloud ecological data are abnormally changed in the transmission process, the data monitoring range is narrowed through the data analysis module, the data analysis module is used for analyzing the alarm process when the data abnormality occurs in the associated data tuples, and the error analysis module is used for analyzing the errors in the alarm process.
The cloud computing module is in a distributed computing mode and is used for carrying out data analysis on data without data abnormality, the cloud computing module is used for carrying out data analysis on the cloud ecological data to obtain a data operation result, and the data output module is used for outputting the corresponding cloud ecological data and the data analysis result.
The data output module is a data output end of the system, the data output module sends the early warning information issued by the data early warning module to monitoring personnel, and the monitoring personnel process the cloud ecological data with abnormal data according to the early warning information.
When the system is used specifically, the system mainly comprises a data input module, a data analysis block, an error analysis module, a data check module, a data early warning module, a cloud storage database, a cloud computing module and a data output module, wherein the data input module receives cloud ecological data and transmits the cloud ecological data to the cloud storage database, the data analysis module analyzes the cloud ecological data by establishing a data analysis model to obtain an evaluation analysis result, the data analysis module analyzes an alarm triggering process of the cloud ecological data, firstly, the data analysis module analyzes data tuples in each grade data to obtain a relevant vector of a feature subset among the data tuples and weight in input, then analyzes an initial range and a cross triggering range of a triggering alarm in data monitoring according to the occurrence of data abnormity to obtain triggering error probability, and controls triggering conditions in the data monitoring through the analysis of the triggering range in the triggering process, therefore, the problem of alarm interference caused by undersize triggering links is solved, the error analysis module carries out error analysis according to the evaluation analysis result of the data analysis module, the data verification module compares the input cloud ecological data to detect data abnormity, the data verification module is a module for verifying the cloud ecological data, in the verification process, the weight change representing the influence of factors and the error rate representing path analysis are considered in the verification process at the same time, correct abnormity analysis is carried out on the data through the self-adaptive analysis process in the verification process, the accuracy of cloud ecological data monitoring is improved, the problem of system blockage caused by data abnormity is reduced, and finally, the data early warning module gives early warning to the existing abnormal risk of the data.
While the invention has been described in further detail with reference to specific embodiments thereof, it is not intended that the invention be limited to the specific embodiments thereof; for those skilled in the art to which the present invention pertains and related technologies, the extension, operation method and data replacement should fall within the protection scope of the present invention based on the technical solution of the present invention.
Claims (5)
1. A cloud ecological data monitoring system is characterized by comprising a data input module, a data analysis module, an error analysis module, a data verification module, a data early warning module, a cloud storage database, a cloud computing module and a data output module, wherein the data input module receives cloud ecological data and transmits the cloud ecological data to the cloud storage database, the data analysis module performs data analysis on the cloud ecological data by establishing a data analysis model to obtain an evaluation analysis result, the error analysis module performs error analysis according to the evaluation analysis result of the data analysis module, the data verification module compares the input cloud ecological data to detect data abnormity, and the data early warning module gives an early warning for the existing data abnormity risk;
the system management process specifically comprises the following steps:
1) the data input module is an input end of the cloud ecological data, the data input module preprocesses the cloud ecological data of the input data to obtain grade data, and the data input module performs data segmentation on the cloud ecological data to obtain different data streams S1，S2，S3，...，SNWherein N is the number of data streams, the data streams are composed of data tuples, and the tuples are recorded as
M is the number of tuples, different tuples are subjected to level division to obtain a level number k and level data, and the data input module sends the different level data to the data analysis module;
2) the data analysis module receives different grade data and carries out evaluation analysis to obtain an evaluation analysis result, and the data analysis module establishes an evaluation analysis model according to the grade data, wherein the specific analysis process is as follows:
step 1, firstly, a data analysis module evaluates and analyzes data tuples in each classified grade data, and a characteristic subset of each data tuple in the same grade data is acquired to obtain ki i∈[0，n]The weight ω is obtained by analysis based on the subset of features1，ω2，ω3，...，ωnThe entropy of one level data is recorded as c ═ Σi∈nωikiComputing a correlation vector between feature subsets of feature tuples
Step 2, the data analysis module re-equals the number of stagesAnalyzing the initial range of all data tuples in the data to the cross trigger range, and recording the initial range of the data monitoring in the data monitoring as (f)1，f2，f3...fz) After the initial range is determined, the data in the data tuples are updated through acquisition, and the initial range f is analyzed according to the acquisition and processing period of the datai i∈[1，z]Carrying out probability analysis on the data tuples in the data set to obtain a probability analysis result, wherein the specific analysis process is as follows:
c＝∑i∈nωiki，
wherein Y (k) is the average alarm time delay of the data tuples at the acquisition time k, and when an alarm of data abnormity occurs, the alarm time delay generated by the alarm target is YtAnd omega is the sampling period of the sample,
r' is the number of initial ranges in a classification level, a data tuple is a classification index of the number, a data tuple passes through the average input information entropy c of the data input module, a sampling period count k, a data tuple alarm rate I (k) at the acquisition time k, an alarm response rate O (k) at the acquisition time k, and an alarm error rate E (k) at the acquisition time k, Y (k) -Yt，
When data abnormity happens, the monitoring system carries out interference analysis on a cross trigger area which generates interference in an alarm trigger area, and the cross trigger area is processed by the systemThe task quantity Q (k) triggered by the fork, a is the weight change number, fi∩fjIs fiAnd fjK is the count of the sampling period from the system operation, and the ratio of the task amount in the cross-trigger area to the task amount in the non-cross-trigger area is recorded as the trigger error probability CiI represents the number of data streams;
step 3, the data analysis module analyzes the triggering error probabilities in all the alarm areas to obtain probability analysis results, and sends the probability analysis results to the error analysis module;
step 4, the error analysis module analyzes according to the probability analysis result and the cross trigger range to obtain an error analysis result, and the data analysis module sends an evaluation analysis result including the probability analysis result and the error analysis result to the data verification module;
3) the data verification module verifies the cloud ecological data according to the received evaluation analysis result and the error analysis result, when data abnormity occurs, the data verification module quickly confirms the range of the data abnormity, data abnormity alarm verification is carried out according to the confirmed range to obtain data early warning information, and meanwhile, the data verification module sends the data early warning information to the data early warning module;
4) and the data early warning module sends out early warning to monitoring personnel according to the data early warning information sent by the data verification module.
2. The cloud ecological data monitoring system according to claim 1, wherein the data verification module in step 3) determines the optimal self-adaptive degree by adopting a self-adaptive degree algorithm according to the data abnormal range to determine different data tuples, and a structured equation model is established according to the probability analysis result and the error analysis result to perform verification analysis on the cloud ecological data, wherein the analysis process is as follows:
w(t)＝(wmax-wmin)Ps(t)+wmin，
w (t) is a function of the fluctuation of the weights in a data stream over time, wmaxIs the most weight fluctuatingLarge value, wminAs the minimum value of the weight fluctuation, Ps(t) error rates of input time variations of the incoming data, and then analysis of the adaptation process based on iterations of weights between the adaptive tuples between all data streams,
wherein, FmaxIs the maximum value of the trigger range in the event of data anomalies in the data stream, FavgIs the average value of the trigger ranges, F' is the larger one of the cross-trigger ranges, K1，K2Is a normal number not greater than 1, the structured analysis process includes a factor analysis process and a path analysis process, and the weight represents the strength and error rate P of the factor analysis paths(t) represents a path analysis process.
3. The cloud ecological data monitoring system according to claim 1, wherein the error analysis module studies the feature subset of the data tuples according to the probability analysis result to obtain a change value of the weight, and then performs association relation analysis on the data tuples in the initial range, the cloud ecological data has data abnormal change in the transmission process, the data monitoring range is narrowed through the data analysis module, the data analysis module analyzes the alarm process when the data abnormal occurs in the associated data tuples, and the error analysis module analyzes the error in the alarm process.
4. The cloud ecological data monitoring system according to claim 1, wherein the cloud computing module is in a distributed computing mode, performs data analysis on data without data abnormality, performs data analysis on the cloud ecological data to obtain a data operation result, and outputs corresponding cloud ecological data and the data analysis result through the data output module.
5. The cloud ecological data monitoring system according to claim 1, wherein the data output module is a data output end of the system, the data output module sends the early warning information issued by the data early warning module to a monitoring person, and the monitoring person processes the cloud ecological data with abnormal data according to the early warning information.
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