CN117670508A - Real-time monitoring and early warning method and system for three-party data information platform - Google Patents
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Abstract
The invention provides a real-time monitoring and early warning method and a system of a three-party data information platform, comprising the following steps: collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by a three-party data platform; determining the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index under the preset dimension based on the historical data source score, and establishing an early warning rule based on the ring ratio increasing and decreasing amplitude and the absolute value change rate; determining whether the data source score is abnormal according to the early warning rule, and carrying out early warning pushing after the data abnormality occurs; and acquiring an early warning result under the early warning rule within a preset time, analyzing the early warning result, ensuring the comprehensiveness of the establishment of the early warning rule, and carrying out abnormal analysis on third party data according to the early warning rule so as to conveniently and timely early warn faults, quickly making a coping scheme, optimizing the early warning rule and improving the accuracy of the early warning rule.
Description
Technical Field
The invention relates to the technical field of data information monitoring, in particular to a real-time monitoring and early warning method and system of a three-party data information platform.
Background
In the background of big data age, the data resource is an important infrastructure for enterprise management, in the field of financial credit, the external third party data is an important data resource of a financial institution, and the dependency of a grading data product on the acquired data reflects the necessity of monitoring and early warning of the third party data. In order to avoid that when the third party data has production faults, the real-time grading data production system is in fault or paralyzed due to response lag, so that economic loss is generated, a calling mechanism needs to establish a perfect real-time monitoring and early warning mechanism, the failure of the externally acquired data is effectively and timely early warned, a response scheme is rapidly made, and the operation efficiency is improved.
Disclosure of Invention
The invention provides a real-time monitoring and early warning method and a real-time monitoring and early warning system for a three-party data information platform, which establish an accurate and perfect real-time monitoring and early warning mechanism and improve the operation efficiency.
A real-time monitoring and early warning method of a three-party data information platform comprises the following steps:
step 1: collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by a three-party data platform;
step 2: determining the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index under the preset dimension based on the historical data source score, and establishing an early warning rule based on the ring ratio increasing and decreasing amplitude and the absolute value change rate;
step 3: determining whether the data source score is abnormal according to the early warning rule, and carrying out early warning pushing after the data abnormality occurs;
step 4: and acquiring an early warning result under the early warning rule within a preset time, analyzing the early warning result, and optimizing the early warning rule according to the analysis result.
Preferably, in step 1, collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by the three-party data platform includes:
scoring rules of different client institutions, scoring products, model versions and time point information are obtained on a three-party data platform;
and scoring the data sources corresponding to the different client institutions, the scoring products, the model versions and the time point information based on the scoring rules to obtain data source scores.
Preferably, after scoring the data sources corresponding to the different client institutions, the scoring products, the model versions and the time point information, the method further comprises:
performing anomaly detection on the scored data sources, and extracting target data sources with scoring missing;
and filling the target data source according to the mapping relation to obtain the score of the target data source.
Preferably, in step 2, determining the ring ratio increase and decrease amplitude and the change rate of the absolute value of each preset index in the preset dimension based on the historical data source score includes:
dividing the historical data source scores based on the preset dimensions to obtain related data source scores corresponding to each preset dimension;
and under the preset dimension, analyzing the related data source scores according to the preset indexes, and determining the ring ratio increase and decrease amplitude and the absolute value change rate under each preset index.
Preferably, the analyzing the related data source scores according to the preset indexes to determine the ring ratio increase and decrease amplitude and the change rate of the absolute value under each preset index includes:
the preset indexes comprise scoring call quantity, scoring equipartition, scoring ratable rate and scoring abnormal request rate;
the scoring call quantity is divided into a call quantity and an effective call quantity, and the call quantity determines the sum of all calls of each client mechanism to different scoring products and model versions according to the related data source scores; the effective call amount is the sum of the specific call and the normal call is determined;
the scores are equally divided into the average value of the normal data source scores in the related data source scores;
the scoring ratable rate is the ratio between the effective call volume and the call volume;
the scoring abnormal request rate is the ratio of the abnormal call volume to the call volume of scoring invalid and overtime scoring;
setting a scoring period and setting an interval time under the scoring period;
and calculating the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index based on the scoring period and the interval time.
Preferably, in step 2, the establishing an early warning rule based on the ring ratio increasing and decreasing amplitude and the change rate of the absolute value includes:
acquiring early warning information of a historical data source, and determining the reference ring ratio increasing and decreasing amplitude and the reference change rate of the data abnormality according to the early warning information from the ring ratio increasing and decreasing amplitude and the change rate of the absolute value;
determining important coefficients of different preset indexes to preset dimensions based on the characteristics of the preset dimensions;
determining a first early warning threshold value of each preset index in the preset dimension based on the history monitoring information of the preset dimension;
based on the important coefficient, correcting the first early warning threshold value to obtain a second early warning threshold value;
based on the characteristics of the preset dimensions, determining the association characteristics among the preset dimensions, and based on the association characteristics, determining the association relation among different preset dimensions in the same preset index;
based on the association relation, determining a value constraint relation between the same preset index under different preset dimensions;
determining a value difference range between second early warning thresholds of the same preset index of different preset dimensions based on the value constraint relation;
extracting a second early warning threshold to be adjusted, wherein the second early warning threshold does not meet the value difference range, and adjusting the second early warning threshold to be adjusted based on the association relation between preset indexes corresponding to the second early warning threshold to be adjusted and the value difference range;
acquiring the determined preset ring ratio increase and decrease amplitude and the preset change rate of the adjusted second early warning threshold;
respectively judging whether the difference between the preset ring ratio increasing and decreasing amplitude and the preset change rate and the difference between the reference ring ratio increasing and decreasing amplitude and the reference change rate are within a preset range or not;
if yes, taking the preset ring ratio increasing and decreasing amplitude and the preset change rate as threshold values of the early warning rules, and establishing the early warning rules;
otherwise, the new value difference range is re-determined, and the second early warning threshold to be adjusted is adjusted until the preset ring ratio increase and decrease amplitude and the preset change rate determined by the adjusted second early warning threshold meet the requirements.
Preferably, in step 3, determining whether the data source score is abnormal according to the early warning rule includes:
determining a ring ratio amplitude increasing and decreasing threshold value and an absolute value change rate threshold value of a preset index under each preset dimension based on the early warning rule;
classifying the change rates of the monitored cyclic ratio amplitude increasing and decreasing and the monitored absolute value based on the cyclic ratio amplitude increasing and decreasing threshold and the change rate threshold of the absolute value, and determining early warning sub-content corresponding to a preset index in each preset dimension under each level;
establishing a data early warning sub-content table based on the ring ratio amplitude increasing and decreasing threshold value and the change rate threshold value of the absolute value;
carrying out correlation analysis on any two early warning sub-contents, determining whether correlation exists between the two early warning sub-contents, and if so, carrying out merging analysis on the two early warning sub-contents to determine the corresponding relation type of the two early warning sub-contents;
based on the relation type, combining the early warning sub-contents to obtain early warning contents;
updating the data early warning sub-content table based on the early warning content to obtain an early warning content table;
acquiring the actual ring ratio increase and decrease amplitude and the actual change rate of the data source score under each preset dimension and preset index;
matching the actual ring ratio increasing and decreasing amplitude and the actual change rate with the early warning content table, and judging whether the matching is successful or not;
if yes, determining that the data source score is abnormal, and determining actual early warning content according to a matching result;
otherwise, determining that the data source score is not abnormal.
Preferably, in step 3, after occurrence of data abnormality, early warning pushing is performed, including:
determining actual early warning content according to the matching result, and generating an alarm mail according to the actual early warning content;
and pushing the alarm mail to a mailbox of a monitoring person.
Preferably, in step 4, an early warning result under the early warning rule in a preset time is obtained, the early warning result is analyzed, and the early warning rule is optimized according to the analysis result, including:
based on the early warning result, determining early warning times in preset time and data checking results corresponding to each early warning;
determining the accurate value of each early warning according to the early warning result and the data checking result;
based on the early warning times and the accurate values, obtaining an analysis result, taking early warning results, of which the accurate values are larger than preset accurate values, in the analysis result as positive samples, and taking other early warning results as negative samples;
determining the accuracy of the early warning rule based on the positive sample and the negative sample;
taking the early warning result corresponding to the positive sample as an iteration rule group, and determining iteration times according to the accuracy;
and carrying out iterative optimization on the early warning rule according to the iterative times based on the iterative rule group to obtain an optimized early warning rule.
A real-time monitoring and early warning system of a three-party data information platform comprises:
the data source module is used for collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by the three-party data platform;
the rule design module is used for determining the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index in preset dimensions based on the historical data source scores, and establishing an early warning rule based on the ring ratio increasing and decreasing amplitude and the absolute value change rate;
the early warning pushing module is used for determining whether the data source score is abnormal according to the early warning rule, and carrying out early warning pushing after the data abnormality occurs;
the rule optimization module is used for acquiring the early warning result under the early warning rule within preset time, analyzing the early warning result and optimizing the early warning rule according to the analysis result.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a real-time monitoring and early warning method of a three-party data information platform in an embodiment of the invention;
FIG. 2 is another flow chart of a real-time monitoring and early warning method of a three-party data information platform according to an embodiment of the invention;
fig. 3 is a block diagram of a real-time monitoring and early warning system of a three-party data information platform in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1
The embodiment of the invention provides a real-time monitoring and early warning method of a three-party data information platform, as shown in fig. 1, comprising the following steps:
step 1: collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by a three-party data platform;
step 2: determining the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index under the preset dimension based on the historical data source score, and establishing an early warning rule based on the ring ratio increasing and decreasing amplitude and the absolute value change rate;
step 3: determining whether the data source score is abnormal according to the early warning rule, and carrying out early warning pushing after the data abnormality occurs;
step 4: and acquiring an early warning result under the early warning rule within a preset time, analyzing the early warning result, and optimizing the early warning rule according to the analysis result.
In this embodiment, the data source score is a data score corresponding to the different customer institutions, scored products, model versions, and time point information.
In this embodiment, the preset dimension is, for example, a customer institution unit, a product service code unit, a product version unit, or a three-party data platform unit.
In this embodiment, the preset dimension includes a slave client organization unit, a product service encoding unit, a product version unit, a three-party data platform unit, and the like.
In this embodiment, the preset indexes include calculating the score call amount, score average, score ratable rate, score hit rate, score abnormal request rate, and the like by minutes, hours, and days.
In this embodiment, the increase and decrease of the ring ratio is an increase or decrease of the corresponding value of the preset index in n periods.
In this embodiment, the rate of change of the absolute value is a rate of change of the absolute value of the numerical value between one cycle of the corresponding numerical value of the preset index.
In this embodiment, the early warning rule is designed from a preset dimension based on the data source score, and the determining the ring ratio increase and decrease amplitude and the change rate of the absolute value at the preset index based on the early warning rule may be, for example: technical indexes such as scoring call quantity, scoring equipartition, scoring ratable rate, scoring hit rate, scoring abnormal request rate and the like are calculated according to minutes, hours and days, and the change of the ring ratio increase and decrease amplitude and the change rate of absolute value of the technical indexes is monitored from dimensions such as a customer institution unit, a product service coding unit, a product version unit, a three-party data platform unit and the like.
In this embodiment, the optimization of the early warning rule may be performed by using parameters such as the triggering frequency, the accuracy rate, the misjudgment rate, and the like of the early warning rule as optimization indexes.
The beneficial effects of above-mentioned design scheme are: the method has the advantages that the pre-set dimension and the pre-set index are selected to determine the pre-warning rule, the establishment comprehensiveness of the pre-warning rule is guaranteed, a basis is provided for the evaluation of the third party data, the third party data is subjected to abnormal analysis according to the pre-warning rule, and then the pre-warning push is issued, so that faults can be conveniently and timely pre-warned efficiently, problems are quickly checked, a response scheme is quickly made, the operation efficiency is improved, finally, the pre-warning result under the pre-warning rule is analyzed, the pre-warning rule is optimized, and the accuracy of the pre-warning rule is improved.
Example 2
Based on embodiment 1, the embodiment of the invention provides a real-time monitoring and early warning method for a three-party data information platform, in step 1, the step of collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by the three-party data platform comprises the following steps:
scoring rules of different client institutions, scoring products, model versions and time point information are obtained on a three-party data platform;
and scoring the data sources corresponding to the different client institutions, the scoring products, the model versions and the time point information based on the scoring rules to obtain data source scores.
The beneficial effects of above-mentioned design scheme are: the three-party data platform is utilized to score according to scoring rules of different client institutions, scoring products, model versions and time point information, so that a foundation is provided for early warning analysis of the three-party data.
Example 3
Based on embodiment 2, the embodiment of the invention provides a real-time monitoring and early warning method for a three-party data information platform, which further comprises the following steps after scoring data sources corresponding to different client institutions, scoring products, model versions and time point information:
performing anomaly detection on the scored data sources, and extracting target data sources with scoring missing;
and filling the target data source according to the mapping relation to obtain the score of the target data source.
In this embodiment, since there are a large number of special cases other than normal scoring, such as scoring timeout, group miss, etc., for 2 data source scoring, it is necessary to fill all special cases with a specified value.
In this embodiment, the mapping relationship is preset according to a scoring rule.
The beneficial effects of above-mentioned design scheme are: and filling the target data sources with the scores missing, so that the comprehensiveness and rationality of the scores of the data sources are ensured.
Example 4
Based on embodiment 1, the embodiment of the invention provides a real-time monitoring and early warning method for a three-party data information platform, as shown in fig. 2, in step 2, based on historical data source scores, determining the ring ratio increase and decrease amplitude and the absolute value change rate of each preset index under preset dimensions, including:
step 2-1: dividing the historical data source scores based on the preset dimensions to obtain related data source scores corresponding to each preset dimension;
step 2-2: and under the preset dimension, analyzing the related data source scores according to the preset indexes, and determining the ring ratio increase and decrease amplitude and the absolute value change rate under each preset index.
In this embodiment, the preset dimension is, for example, a dimension of a customer institution unit, a product service coding unit, a product version unit, a three-party data platform unit, and the like, and the data source score related to the preset dimension is obtained, so that the analysis of the preset index under the dimension is facilitated.
The beneficial effects of above-mentioned design scheme are: the data source score is analyzed by selecting preset dimensions and preset indexes, the ring ratio increase and decrease amplitude and the change rate of absolute values are obtained through calculation, and a basis is provided for the establishment of early warning rules.
Example 5
Based on embodiment 4, the embodiment of the invention provides a real-time monitoring and early warning method for a three-party data information platform, which is used for analyzing the scores of related data sources according to the preset indexes and determining the increase and decrease of the ring ratio and the change rate of the absolute value under each preset index, and comprises the following steps:
the preset indexes comprise scoring call quantity, scoring equipartition, scoring ratable rate and scoring abnormal request rate;
the scoring call quantity is divided into a call quantity and an effective call quantity, and the call quantity determines the sum of all calls of each client mechanism to different scoring products and model versions according to the related data source scores; the effective call amount is the sum of the specific call and the normal call is determined;
the scores are equally divided into the average value of the normal data source scores in the related data source scores;
the scoring ratable rate is the ratio between the effective call volume and the call volume;
the scoring abnormal request rate is the ratio of the abnormal call volume to the call volume of scoring invalid and overtime scoring;
setting a scoring period and setting an interval time under the scoring period;
and calculating the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index based on the scoring period and the interval time.
The beneficial effects of above-mentioned design scheme are: : the values of all preset indexes in every preset dimension are obtained by definitely calculating every preset index, so that the preset indexes can accurately represent the data source scores, and a foundation is provided for the establishment of early warning rules.
Example 6
Based on embodiment 1, the embodiment of the invention provides a real-time monitoring and early warning method for a three-party data information platform, in step 2, an early warning rule is established based on the ring ratio increase and decrease amplitude and the change rate of an absolute value, and the method comprises the following steps:
acquiring early warning information of a historical data source, and determining the reference ring ratio increasing and decreasing amplitude and the reference change rate of the data abnormality according to the early warning information from the ring ratio increasing and decreasing amplitude and the change rate of the absolute value;
determining important coefficients of different preset indexes to preset dimensions based on the characteristics of the preset dimensions;
determining a first early warning threshold value of each preset index in the preset dimension based on the history monitoring information of the preset dimension;
based on the important coefficient, correcting the first early warning threshold value to obtain a second early warning threshold value;
based on the characteristics of the preset dimensions, determining the association characteristics among the preset dimensions, and based on the association characteristics, determining the association relation among different preset dimensions in the same preset index;
based on the association relation, determining a value constraint relation between the same preset index under different preset dimensions;
determining a value difference range between second early warning thresholds of the same preset index of different preset dimensions based on the value constraint relation;
extracting a second early warning threshold to be adjusted, wherein the second early warning threshold does not meet the value difference range, and adjusting the second early warning threshold to be adjusted based on the association relation between preset indexes corresponding to the second early warning threshold to be adjusted and the value difference range;
acquiring the determined preset ring ratio increase and decrease amplitude and the preset change rate of the adjusted second early warning threshold;
respectively judging whether the difference between the preset ring ratio increasing and decreasing amplitude and the preset change rate and the difference between the reference ring ratio increasing and decreasing amplitude and the reference change rate are within a preset range or not;
if yes, taking the preset ring ratio increasing and decreasing amplitude and the preset change rate as threshold values of the early warning rules, and establishing the early warning rules;
otherwise, the new value difference range is re-determined, and the second early warning threshold to be adjusted is adjusted until the preset ring ratio increase and decrease amplitude and the preset change rate determined by the adjusted second early warning threshold meet the requirements.
In this embodiment, the importance coefficient is used to represent the importance degree of the preset index on the preset dimension, for example, when the preset dimension is a customer mechanism unit, the preset index is a call quantity, which can more embody the feature of the customer mechanism unit, and the importance coefficient corresponding to the call quantity is larger.
In this embodiment, the first early warning threshold is, for example, weighted according to the importance coefficient, to obtain a second early warning threshold.
In this embodiment, the reference ring ratio increase and decrease amplitude and the reference rate of change have been historically determined to have been effective.
In this embodiment, the characteristics of the preset dimension are used to represent attributes of the preset dimension, such as customer institutions, scored products, and the like.
In this embodiment, the value constraint relationship is that, for example, a difference between a value of a preset index score average for a client organization in a preset dimension and a value of a preset index score average for a bone score in a preset dimension does not exceed a preset value.
In this embodiment, the adjustment of the second early warning threshold to be adjusted ensures that the adjusted second early warning threshold can more accurately reflect the scoring characteristic of the data source, and ensures the accuracy of establishing the early warning rule.
The beneficial effects of above-mentioned design scheme are: the early warning threshold is determined according to the association relation between preset dimensions, and the early warning threshold is judged and adjusted by taking the increase and decrease of the reference ring ratio and the reference change rate determined by the historical data source as references, so that the data source abnormality judgment can be better represented by the increase and decrease of the set ring ratio and the preset change rate determined finally according to the early warning threshold, and the accuracy of the early warning rule is ensured.
Example 7
Based on embodiment 1, the embodiment of the invention provides a real-time monitoring and early warning method for a three-party data information platform, in step 3, according to the early warning rule, determining whether the data source score is abnormal or not includes:
determining a ring ratio amplitude increasing and decreasing threshold value and an absolute value change rate threshold value of a preset index under each preset dimension based on the early warning rule;
classifying the change rates of the monitored cyclic ratio amplitude increasing and decreasing and the monitored absolute value based on the cyclic ratio amplitude increasing and decreasing threshold and the change rate threshold of the absolute value, and determining early warning sub-content corresponding to a preset index in each preset dimension under each level;
establishing a data early warning sub-content table based on the ring ratio amplitude increasing and decreasing threshold value and the change rate threshold value of the absolute value;
carrying out correlation analysis on any two early warning sub-contents, determining whether correlation exists between the two early warning sub-contents, and if so, carrying out merging analysis on the two early warning sub-contents to determine the corresponding relation type of the two early warning sub-contents;
based on the relation type, combining the early warning sub-contents to obtain early warning contents;
updating the data early warning sub-content table based on the early warning content to obtain an early warning content table;
acquiring the actual ring ratio increase and decrease amplitude and the actual change rate of the data source score under each preset dimension and preset index;
matching the actual ring ratio increasing and decreasing amplitude and the actual change rate with the early warning content table, and judging whether the matching is successful or not;
if yes, determining that the data source score is abnormal, and determining actual early warning content according to a matching result;
otherwise, determining that the data source score is not abnormal.
In this embodiment, the number of the change rate thresholds of the cyclic ratio increasing and decreasing threshold and the absolute value is plural, for example, a first early warning sub-content corresponding between the first cyclic ratio increasing and decreasing threshold and the absolute value and a second cyclic ratio increasing and decreasing threshold and the absolute value, a second early warning sub-content corresponding to less than the first cyclic ratio increasing and decreasing threshold and the absolute value, and a third early warning sub-content corresponding to the second cyclic ratio increasing and decreasing threshold and the absolute value.
In this embodiment, there may be a correlation between values of preset indexes used between different preset dimensions, so correlation analysis needs to be performed on any two pre-warning sub-contents.
In this embodiment, the relationship type may be, for example, a superimposed relationship, an exponential relationship, a cancellation relationship, or the like.
In this embodiment, the early warning content table corresponds to one early warning content for a plurality of ranges of the amplitude reduction threshold and the absolute value.
The beneficial effects of above-mentioned design scheme are: by analyzing the early warning rules, the association relation between the ring ratio increase and decrease threshold value and the change rate threshold value of the absolute value and different preset dimensions is determined, comprehensive early warning content is further determined, the comprehensiveness and the accuracy of the early warning content are guaranteed, and the accuracy of early warning data abnormality is improved.
Example 8
Based on embodiment 7, the embodiment of the invention provides a real-time monitoring and early warning method for a three-party data information platform, in step 3, after data abnormality occurs, early warning pushing is performed, which comprises the following steps:
determining actual early warning content according to the matching result, and generating an alarm mail according to the actual early warning content;
and pushing the alarm mail to a mailbox of a monitoring person.
The beneficial effects of above-mentioned design scheme are: mail is sent to monitoring personnel in time at a data airport, so that rapid response and data problem elimination are facilitated.
Example 9
Based on embodiment 1, the embodiment of the invention provides a real-time monitoring and early warning method for a three-party data information platform, in step 4, an early warning result under the early warning rule in a preset time is obtained, the early warning result is analyzed, and the early warning rule is optimized according to the analysis result, including:
based on the early warning result, determining early warning times in preset time and data checking results corresponding to each early warning;
determining the accurate value of each early warning according to the early warning result and the data checking result;
the calculation formula of the accurate value F of the current early warning is as follows:
wherein P is S The early warning analysis value representing the early warning result of the current early warning is (0, 1), and P Y An investigation analysis value representing the investigation result of the data according to the current early warning is (0, 1), alpha S The value of the abnormal data range corresponding to the early warning result of the current early warning is (0, 1), alpha Y Taking the value of the abnormal data range value corresponding to the current early-warning data checking resultIs (0, 1);
based on the early warning times and the accurate values, obtaining an analysis result, taking early warning results, of which the accurate values are larger than preset accurate values, in the analysis result as positive samples, and taking other early warning results as negative samples;
determining the accuracy of the early warning rule based on the positive sample and the negative sample;
the calculation formula of the accuracy G of the early warning rule is as follows:
wherein K is a Represents a first weight value, K b Represents a second weight value, n represents the number of positive samples, m represents the number of negative samples, F ai Represents the exact value corresponding to the i positive sample, F bj Representing the exact value corresponding to the jth negative sample;
taking the early warning result corresponding to the positive sample as an iteration rule group, and determining iteration times according to the accuracy;
and carrying out iterative optimization on the early warning rule according to the iterative times based on the iterative rule group to obtain an optimized early warning rule.
In this embodiment, the values of the first weight and the second weight are (0, 1), and are determined by the value of the preset accurate value, and the larger the preset accurate value is, the corresponding K a The smaller K b The larger but K a Greater than K b 。
In this embodiment, the higher the accuracy, the fewer the corresponding number of iterations.
In this embodiment, the accuracy value represents the accuracy of one early warning result.
In this embodiment, the accuracy represents the overall accuracy of all the early warning results determined according to the early warning rule in a preset time.
In this embodiment, the larger the abnormal data range is, the larger the value is.
The beneficial effects of above-mentioned design scheme are: the early warning result under the early warning rule in the preset time is obtained, the early warning result and the actual data checking result are compared and analyzed, the early warning rule is optimized according to the analysis result, and the accuracy of the early warning rule is improved.
Example 10
A real-time monitoring and early warning system of a three-party data information platform, as shown in figure 3, comprises:
the data source module is used for collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by the three-party data platform;
the rule design module is used for determining the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index in preset dimensions based on the historical data source scores, and establishing an early warning rule based on the ring ratio increasing and decreasing amplitude and the absolute value change rate;
the early warning pushing module is used for determining whether the data source score is abnormal according to the early warning rule, and carrying out early warning pushing after the data abnormality occurs;
the rule optimization module is used for acquiring the early warning result under the early warning rule within preset time, analyzing the early warning result and optimizing the early warning rule according to the analysis result.
The beneficial effects of above-mentioned design scheme are: the method has the advantages that the pre-set dimension and the pre-set index are selected to determine the pre-warning rule, the establishment comprehensiveness of the pre-warning rule is guaranteed, a basis is provided for the evaluation of the third party data, the third party data is subjected to abnormal analysis according to the pre-warning rule, and then the pre-warning push is issued, so that faults can be conveniently and timely pre-warned efficiently, problems are quickly checked, a response scheme is quickly made, the operation efficiency is improved, finally, the pre-warning result under the pre-warning rule is analyzed, the pre-warning rule is optimized, and the accuracy of the pre-warning rule is improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The real-time monitoring and early warning method for the three-party data information platform is characterized by comprising the following steps of:
step 1: collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by a three-party data platform;
step 2: determining the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index under the preset dimension based on the historical data source score, and establishing an early warning rule based on the ring ratio increasing and decreasing amplitude and the absolute value change rate;
step 3: determining whether the data source score is abnormal according to the early warning rule, and carrying out early warning pushing after the data abnormality occurs;
step 4: and acquiring an early warning result under the early warning rule within a preset time, analyzing the early warning result, and optimizing the early warning rule according to the analysis result.
2. The method for real-time monitoring and early warning of a three-party data information platform according to claim 1, wherein in step 1, collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by the three-party data platform comprises:
scoring rules of different client institutions, scoring products, model versions and time point information are obtained on a three-party data platform;
and scoring the data sources corresponding to the different client institutions, the scoring products, the model versions and the time point information based on the scoring rules to obtain data source scores.
3. The method for real-time monitoring and early warning of a three-party data information platform according to claim 2, wherein after scoring the data sources corresponding to the different client institutions, the scoring products, the model versions and the time point information, the method further comprises:
performing anomaly detection on the scored data sources, and extracting target data sources with scoring missing;
and filling the target data source according to the mapping relation to obtain the score of the target data source.
4. The method for real-time monitoring and early warning of a three-party data information platform according to claim 1, wherein in step 2, determining the ring ratio increase and decrease amplitude and the change rate of the absolute value of each preset index in the preset dimension based on the historical data source score comprises:
dividing the historical data source scores based on the preset dimensions to obtain related data source scores corresponding to each preset dimension;
and under the preset dimension, analyzing the related data source scores according to the preset indexes, and determining the ring ratio increase and decrease amplitude and the absolute value change rate under each preset index.
5. The method for real-time monitoring and early warning of a three-party data information platform according to claim 4, wherein analyzing the scores of the related data sources according to the preset indexes to determine the increase and decrease of the ring ratio and the change rate of the absolute value under each preset index comprises the following steps:
the preset indexes comprise scoring call quantity, scoring equipartition, scoring ratable rate and scoring abnormal request rate;
the scoring call quantity is divided into a call quantity and an effective call quantity, and the call quantity determines the sum of all calls of each client mechanism to different scoring products and model versions according to the related data source scores; the effective call amount is the sum of the specific call and the normal call is determined;
the scores are equally divided into the average value of the normal data source scores in the related data source scores;
the scoring ratable rate is the ratio between the effective call volume and the call volume;
the scoring abnormal request rate is the ratio of the abnormal call volume to the call volume of scoring invalid and overtime scoring;
setting a scoring period and setting an interval time under the scoring period;
and calculating the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index based on the scoring period and the interval time.
6. The real-time monitoring and early warning method of the three-party data information platform according to claim 1, wherein in the step 2, an early warning rule is established based on the ring ratio increasing and decreasing amplitude and the change rate of the absolute value, and the method comprises the following steps:
acquiring early warning information of a historical data source, and determining the reference ring ratio increasing and decreasing amplitude and the reference change rate of the data abnormality according to the early warning information from the ring ratio increasing and decreasing amplitude and the change rate of the absolute value;
determining important coefficients of different preset indexes to preset dimensions based on the characteristics of the preset dimensions;
determining a first early warning threshold value of each preset index in the preset dimension based on the history monitoring information of the preset dimension;
based on the important coefficient, correcting the first early warning threshold value to obtain a second early warning threshold value;
based on the characteristics of the preset dimensions, determining the association characteristics among the preset dimensions, and based on the association characteristics, determining the association relation among different preset dimensions in the same preset index;
based on the association relation, determining a value constraint relation between the same preset index under different preset dimensions;
determining a value difference range between second early warning thresholds of the same preset index of different preset dimensions based on the value constraint relation;
extracting a second early warning threshold to be adjusted, wherein the second early warning threshold does not meet the value difference range, and adjusting the second early warning threshold to be adjusted based on the association relation between preset indexes corresponding to the second early warning threshold to be adjusted and the value difference range;
acquiring the determined preset ring ratio increase and decrease amplitude and the preset change rate of the adjusted second early warning threshold;
respectively judging whether the difference between the preset ring ratio increasing and decreasing amplitude and the preset change rate and the difference between the reference ring ratio increasing and decreasing amplitude and the reference change rate are within a preset range or not;
if yes, taking the preset ring ratio increasing and decreasing amplitude and the preset change rate as threshold values of the early warning rules, and establishing the early warning rules;
otherwise, the new value difference range is re-determined, and the second early warning threshold to be adjusted is adjusted until the preset ring ratio increase and decrease amplitude and the preset change rate determined by the adjusted second early warning threshold meet the requirements.
7. The method for real-time monitoring and early warning of a three-party data information platform according to claim 1, wherein in step 3, determining whether the data source score is abnormal according to the early warning rule comprises:
determining a ring ratio amplitude increasing and decreasing threshold value and an absolute value change rate threshold value of a preset index under each preset dimension based on the early warning rule;
classifying the change rates of the monitored cyclic ratio amplitude increasing and decreasing and the monitored absolute value based on the cyclic ratio amplitude increasing and decreasing threshold and the change rate threshold of the absolute value, and determining early warning sub-content corresponding to a preset index in each preset dimension under each level;
establishing a data early warning sub-content table based on the ring ratio amplitude increasing and decreasing threshold value and the change rate threshold value of the absolute value;
carrying out correlation analysis on any two early warning sub-contents, determining whether correlation exists between the two early warning sub-contents, and if so, carrying out merging analysis on the two early warning sub-contents to determine the corresponding relation type of the two early warning sub-contents;
based on the relation type, combining the early warning sub-contents to obtain early warning contents;
updating the data early warning sub-content table based on the early warning content to obtain an early warning content table;
acquiring the actual ring ratio increase and decrease amplitude and the actual change rate of the data source score under each preset dimension and preset index;
matching the actual ring ratio increasing and decreasing amplitude and the actual change rate with the early warning content table, and judging whether the matching is successful or not;
if yes, determining that the data source score is abnormal, and determining actual early warning content according to a matching result;
otherwise, determining that the data source score is not abnormal.
8. The method for real-time monitoring and early warning of a three-party data information platform according to claim 7, wherein in step 3, after occurrence of data abnormality, early warning pushing is performed, comprising:
determining actual early warning content according to the matching result, and generating an alarm mail according to the actual early warning content;
and pushing the alarm mail to a mailbox of a monitoring person.
9. The method for real-time monitoring and early warning of a three-party data information platform according to claim 1, wherein in step 4, an early warning result under the early warning rule in a preset time is obtained, the early warning result is analyzed, and the early warning rule is optimized according to the analysis result, comprising:
based on the early warning result, determining early warning times in preset time and data checking results corresponding to each early warning;
determining the accurate value of each early warning according to the early warning result and the data checking result;
based on the early warning times and the accurate values, obtaining an analysis result, taking early warning results, of which the accurate values are larger than preset accurate values, in the analysis result as positive samples, and taking other early warning results as negative samples;
determining the accuracy of the early warning rule based on the positive sample and the negative sample;
taking the early warning result corresponding to the positive sample as an iteration rule group, and determining iteration times according to the accuracy;
and carrying out iterative optimization on the early warning rule according to the iterative times based on the iterative rule group to obtain an optimized early warning rule.
10. The utility model provides a real-time supervision early warning system of three party data information platform which characterized in that includes:
the data source module is used for collecting data source scores corresponding to different client institutions, scoring products, model versions and time point information by the three-party data platform;
the rule design module is used for determining the ring ratio increasing and decreasing amplitude and the absolute value change rate of each preset index in preset dimensions based on the historical data source scores, and establishing an early warning rule based on the ring ratio increasing and decreasing amplitude and the absolute value change rate;
the early warning pushing module is used for determining whether the data source score is abnormal according to the early warning rule, and carrying out early warning pushing after the data abnormality occurs;
the rule optimization module is used for acquiring the early warning result under the early warning rule within preset time, analyzing the early warning result and optimizing the early warning rule according to the analysis result.
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