CN116523038A - Data monitoring method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a data monitoring method, a data monitoring device, computer equipment and a storage medium, and relates to the technical field of big data. The method comprises the following steps: processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data, constructing an isolated forest corresponding to each monitoring index according to each target transaction data based on an isolated forest algorithm, determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index, and determining the abnormal transaction data in each original transaction data according to the comprehensive health value of each target transaction data. The method can simplify the data monitoring process.
Description
Technical Field
The present disclosure relates to the field of big data technologies, and in particular, to a data monitoring method, a data monitoring device, a computer device, and a storage medium.
Background
Along with the gradual development of the financial market, a large number of transaction services with different transaction characteristics appear, and in order to ensure the accuracy of transaction data, a data monitoring method appears. According to the current data monitoring method, different monitoring rules are set for each transaction task, data monitoring is carried out according to the monitoring rules, and then monitored abnormal transaction data are directly sent to operation and maintenance personnel for processing.
However, since the types of transaction services are rapidly growing, the current data monitoring method is adopted, and the corresponding monitoring rules need to be reconfigured for the newly added transaction services, so that the whole data monitoring process is complicated.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a data monitoring method, apparatus, computer device and storage medium to simplify the data monitoring process.
In a first aspect, the present application provides a data monitoring method. The method comprises the following steps:
processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data;
determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
In one embodiment, processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data includes:
Determining index data of the original transaction data under each monitoring index aiming at each original transaction data in the original transaction data set; and splicing the index data of the original transaction data under each monitoring index to obtain target transaction data corresponding to the original transaction data.
In one embodiment, based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data, including:
aiming at each monitoring index, constructing an index data set according to index data of each target transaction data under the monitoring index; and selecting different index data from the index data set as partition points, and partitioning the index data set by adopting the partition points to obtain an isolated forest corresponding to the monitoring index.
In one embodiment, determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index includes:
aiming at each target transaction data, determining an index health value of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index; and taking the sum of the index health values of the target transaction data under each monitoring index as the comprehensive health value of the target transaction data.
In one embodiment, determining the indicator health value of the target transaction data under each monitoring indicator according to the isolated forest corresponding to each monitoring indicator includes:
determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index; and determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data under each monitoring index.
In one embodiment, determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index includes:
aiming at each monitoring index, determining the height value of the target transaction data under each isolated tree according to the tree structure of each isolated tree in the isolated forest corresponding to the monitoring index; and taking the average value of the height values of the target transaction data under each isolated tree as the average height of the target transaction data under the monitoring index.
In one embodiment, the method further comprises:
comparing the index health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index with the health threshold value corresponding to each monitoring index; and taking the monitoring index with the index health value smaller than the corresponding health threshold value as an abnormal factor corresponding to the abnormal transaction data.
In a second aspect, the present application further provides a data monitoring device. The device comprises:
the data processing module is used for processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
the forest construction module is used for constructing an isolated forest corresponding to each monitoring index according to each target transaction data based on an isolated forest algorithm;
the health value determining module is used for determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and the abnormality determining module is used for determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data;
Determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data;
determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
Processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data;
determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
According to the data monitoring method, the device, the computer equipment and the storage medium, the monitoring index is introduced to process each original transaction data in the original transaction data set so as to extract the data to be monitored in each original transaction data, namely the target transaction data corresponding to each original transaction data, so that when a new transaction service appears, the data to be monitored in the original transaction data corresponding to the new transaction service can be extracted only by the monitoring index, the monitoring rule is not required to be reconfigured, and the data monitoring process is simplified; further, an isolated forest algorithm is adopted to analyze each target transaction data from a plurality of monitoring indexes so as to determine the health value of each target transaction data, and further, according to the health value of each target transaction data, the abnormal transaction data in each original transaction data can be accurately determined, so that a mode is provided for accurately extracting the abnormal transaction data from a large amount of original transaction data.
Drawings
FIG. 1 is a diagram of an application environment for a data monitoring method in one embodiment;
FIG. 2A is a flow chart of a method of data monitoring in one embodiment;
FIG. 2B is a block diagram of an orphan tree in one embodiment;
FIG. 3 is a flow diagram of determining an integrated health value in one embodiment;
FIG. 4 is a flow chart of determining indicator health values in one embodiment;
FIG. 5 is a flow chart illustrating determining anomaly factors in one embodiment;
FIG. 6 is a flow chart of a data monitoring method in another embodiment;
FIG. 7 is a block diagram of a data monitoring device in one embodiment;
FIG. 8 is a block diagram of a data monitoring device in another embodiment;
FIG. 9 is a block diagram of a data monitoring device in yet another embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The data monitoring method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process, such as raw transaction data. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. For example, the server 104 processes each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data; based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data; determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index; further, according to the comprehensive health value of each target transaction data, determining abnormal transaction data in each original transaction data; further, the server 104 transmits the abnormal transaction data to the terminal 102. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and internet of things devices. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
Along with the gradual development of the financial market, a large number of transaction services with different transaction characteristics appear, and in order to ensure the accuracy of acquired transaction data, a data monitoring method appears. According to the current data monitoring method, different monitoring rules are set for each transaction task, data monitoring is carried out according to the monitoring rules, and then monitored abnormal transaction data are directly sent to operation and maintenance personnel for processing.
However, since the types of transaction services are rapidly growing, the current data monitoring method is adopted, and the corresponding monitoring rules need to be reconfigured for the newly added transaction services, so that the whole data monitoring process is complicated.
Based on this, in one embodiment, as shown in fig. 2A, a data monitoring method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
s201, processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data.
The monitoring index is a preset index and is used for monitoring key data in the original transaction data. Optionally, in the quotation system, the monitoring index may be one or more of indexes such as CPU usage, memory usage, disk I/O, network traffic, packet loss rate, transaction amount, response time, and transaction success rate.
The original transaction data refers to untreated data generated by various businesses when the businesses conduct transactions; the original transaction data set is a set of original transaction data, and comprises at least two original transaction data; the target transaction data refers to data which is obtained by processing the original transaction data and contains data corresponding to the monitoring index.
Optionally, because the types of the transaction services are different, the obtained original transaction data also have corresponding differences, and in order to unify the formats of the original transaction data corresponding to the various transaction services, the monitoring index can be preset according to the data formats in the original transaction data corresponding to the various transaction services. It can be understood that the original transaction data corresponding to various transaction services includes index data corresponding to the monitoring index.
Optionally, a data processing model including at least one monitoring index may be constructed, and then each original transaction data in the original transaction data set is sequentially input into the trained data processing model, and the data processing model directly outputs target transaction data corresponding to each original transaction data based on the input original transaction data and parameters of the data processing model.
Or, determining index data of the original transaction data under each monitoring index aiming at each original transaction data in the original transaction data set; and splicing the index data of the original transaction data under each monitoring index to obtain target transaction data corresponding to the original transaction data.
The index data refers to data corresponding to each monitoring index contained in the original transaction data.
Specifically, after the original transaction data set is acquired, the original transaction data is analyzed for each original transaction data, so that index data of the original transaction data under each monitoring index can be directly determined. After the index data of the original transaction data under each monitoring index is obtained, the index data of the original transaction data under each monitoring index can be spliced according to a set format, and then target transaction data corresponding to the original transaction data can be obtained.
For example, in a quotation system, the monitoring metrics are the amount of transactions, response time, and success rate of transactions. The acquired original transaction data is acquired time, 2023-03-06:16; transaction amount, 60; response time, 10ms; the transaction success rate is 100 percent, and the target transaction data transaction amount can be obtained by splicing the index data of the original transaction data under each monitoring index, 60; response time, 10ms; transaction success rate, 100% ".
S202, constructing an isolated forest corresponding to each monitoring index according to each target transaction data based on an isolated forest algorithm.
Optionally, after the target transaction data are obtained, the target transaction data can be directly input into a forest construction model trained based on an isolated forest algorithm, and the forest construction model can directly construct an isolated forest corresponding to each monitoring index based on the input target transaction data and parameters of the forest construction model.
Or, for each monitoring index, constructing an index data set according to index data of each target transaction data under the monitoring index; based on an isolated forest algorithm, constructing an isolated forest corresponding to the monitoring index according to the index data set. The method comprises the steps of constructing an isolated forest corresponding to the monitoring index according to an index data set based on an isolated forest algorithm, selecting different index data from the index data set as partition points, and dividing the index data set by adopting the partition points to obtain the isolated forest corresponding to the monitoring index.
Specifically, after target transaction data is obtained, for each monitoring index, index data of each target transaction data under the monitoring index can be extracted to form a corresponding index data set; then, a dividing point can be randomly generated, and the index data set is divided into a left sub-node set and a right sub-node set according to the comparison result of the dividing point and each data in the index data set; further, the method is adopted to continue to divide the data set with the data quantity of not 1 in the left sub-node set and the right sub-node set until the data quantity in each sub-node set is 1, and then an isolated tree corresponding to the monitoring index can be constructed. It can be understood that, because the segmentation is random, each segmentation mode can obtain a corresponding isolated tree, and therefore each monitoring index can construct a corresponding isolated forest.
For example, referring to fig. 2B, an isolated tree in an isolated forest is constructed for an index data set {61,60,59,55,42} of a monitored index of a transaction amount by selecting index data 55 as a cut point P in a current data set, placing index data smaller than the cut point P in a right sub-node set, and placing the rest of index data in a left sub-node set to obtain a left sub-node set {61,60,59,55} and a right sub-node set {42}; subsequently, index data 60 is selected as a cut point P1, resulting in a left child node set {61,60} and a right child node set {59,55}; for the left sub-node set {61,60}, selecting index data 61 as a cutting point P2, and generating a left sub-node set {61} and a right sub-node set {60}; for the right sub-node set {59,55}, the index data 59 is selected as the cut point P3, and the left sub-node set {59} and the right sub-node set {55} are generated after cutting, at which time the orphan tree construction is completed.
S203, determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index.
Wherein, the comprehensive health value is an index for comprehensively measuring whether the target transaction data has abnormality from a plurality of monitoring index dimensions; further, a larger overall health value represents a lower likelihood of anomalies in the target transaction data, i.e., a higher degree of data health.
Optionally, after constructing an isolated forest corresponding to each monitoring index, determining an index health value of each target transaction data under the monitoring index corresponding to each isolated forest according to a structure of each isolated tree in each isolated forest; further, for each target transaction data, according to the index health value of the target transaction data under each monitoring index, the comprehensive health value of the target transaction data can be determined.
S204, determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
Specifically, after the comprehensive health value of each target transaction data is obtained, the comprehensive health value of each target transaction data can be compared with a preset health threshold, and if the comprehensive health value of a certain target transaction data is smaller than the health threshold, the original transaction data is abnormal transaction data; otherwise, the original transaction data is normal transaction data.
Further, after the abnormal transaction data is determined, the determined abnormal transaction data can be directly sent to a terminal held by operation and maintenance personnel, and the operation and maintenance personnel is prompted to process in time.
It can be understood that, since the monitoring index is preset according to the data format of the original transaction data, for each newly-appearing transaction service, the original transaction data of the transaction service includes index data corresponding to each monitoring index, so that when a new transaction service appears, it is not necessary to reconfigure the new monitoring index for the newly-appearing transaction service, i.e. the method is adapted to all transaction service scenarios.
In the data monitoring method, the monitoring index is introduced to process each original transaction data in the original transaction data set so as to extract the data to be monitored in each original transaction data, namely the target transaction data corresponding to each original transaction data, so that when a new transaction service appears, the data to be monitored in the original transaction data corresponding to the new transaction service can be extracted only by the monitoring index, the monitoring rule is not required to be reconfigured, and the data monitoring process is simplified; further, an isolated forest algorithm is adopted to analyze each target transaction data from a plurality of monitoring indexes so as to determine the health value of each target transaction data, and further, according to the health value of each target transaction data, the abnormal transaction data in each original transaction data can be accurately determined, so that a mode is provided for accurately extracting the abnormal transaction data from a large amount of original transaction data.
In order to ensure the accuracy of the determination of the abnormal transaction data, on the basis of the above embodiment, a determination manner of the comprehensive health value is provided in this embodiment, as shown in fig. 3, and specifically includes the following steps:
s301, determining an index health value of each monitoring index of each target transaction data according to the isolated forest corresponding to each monitoring index.
Optionally, after constructing the isolated forest corresponding to each monitoring index, for each target transaction data, a height value of each index data in the target transaction data in each isolated tree of the corresponding isolated forest may be determined according to a structure of each isolated tree in the isolated forest corresponding to each monitoring index.
And determining the index health value of the target transaction data under the monitoring index according to the height value of the index data corresponding to the monitoring index in the target transaction data in each corresponding isolated tree of the isolated forest aiming at each monitoring index. For example, the height value of a certain target transaction data under the monitoring index of the transaction amount is 3, 3 and 4, and the maximum value of the height value can be selected as the index health value of the transaction amount, namely the index health value of the target transaction data under the transaction amount is 4.
S302, taking the sum of index health values of the target transaction data under each monitoring index as the comprehensive health value of the target transaction data.
Further, after determining the indicator health value of the target transaction data under each monitoring indicator, the indicator health values of the target transaction data under each monitoring indicator may be added, and the obtained result may be used as the comprehensive health value of the target transaction data.
In the embodiment, the target transaction data safety can be analyzed from multiple dimensions by introducing the index health value of the target transaction data under each monitoring index, so that the accuracy of the comprehensive health value determination of each target transaction data is ensured, and the accuracy of the abnormal transaction data determination is further ensured.
In order to ensure the accuracy of the target transaction data in the indicator health value under each monitoring indicator, the method for determining the indicator health value is provided in this embodiment on the basis of the above embodiment, as shown in fig. 4, and specifically includes the following steps:
s401, determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index.
Optionally, for each monitoring index, determining a height value of the target transaction data under each isolated tree according to a tree structure of each isolated tree in the isolated forest corresponding to the monitoring index; and taking the average value of the height values of the target transaction data under each isolated tree as the average height of the target transaction data under the monitoring index.
Specifically, for each monitoring index, the height value of the target transaction data under the monitoring index under each isolated tree, namely the height value of the target transaction data under each isolated tree, can be determined according to the tree structure of each isolated tree in the isolated forest corresponding to the monitoring index and the position of the target transaction data under the monitoring index in each isolated tree. For example, with continued reference to fig. 2B, in an orphan tree, the height value of the target transaction data under the orphan tree, to which the index data 42 corresponds, is 1; the height value of the target transaction data corresponding to the index data 61, 60, 59, 55 under the island tree is 3.
Further, the height value of the target transaction data under each isolated tree can be counted, and then the average value of the height values of the target transaction data under each isolated tree is used as the average height of the target transaction data under the monitoring index.
S402, determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data under each monitoring index.
It can be understood that, because the importance degree and the fluctuation degree of each monitoring index are different, in order to ensure the accuracy of the index health value of the target transaction data under each monitoring index, the weight corresponding to each monitoring index can be determined according to the importance degree of the monitoring index and the fluctuation degree of the index data corresponding to the monitoring index. For example, in a quotation system, the importance of the transaction success rate is higher, and the transaction success rate is not easy to fluctuate, so that the transaction success rate can be given higher weight; the response time is susceptible to fluctuations from the network and the quotation system, so that lower weight can be given to the response time.
Optionally, after determining the weight of each monitoring index, each monitoring index is used for each target transaction data The weight of the index is multiplied by the average height of the target transaction data under each monitoring index to obtain the index health value of the target transaction data under each monitoring index. For example, in a quotation system, the weight of the transaction amount is 0.4, the weight of the response time is 0.1, and the weight of the transaction success rate is 0.5; in the target transaction data, the index health value corresponding to the transaction amount is A 1 The corresponding index health value of response time is A 2 The index health value corresponding to the transaction success rate is A 3 Thus, the overall health value of the target transaction data is 0.4 a 1 +0.1*A 2 +0.5*A 3 。
Optionally, since the average height variation range of the target transaction data under each monitoring index is larger, and the determination of the health value of the subsequent index is affected to a certain extent, the average height of each index data in the isolated tree can be normalized by referring to the formula (1); and then determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data after normalization processing under each monitoring index aiming at each target transaction data.
Wherein H is i,j Representing the average height of the ith target transaction data after normalization processing under the monitoring index j; h is a i,j Representing the average height of the ith target transaction data under the monitoring index j;representing the maximum average height in each target transaction data under the monitoring index j; />Representing the smallest average height in each target transaction data under the monitor index j.
In this embodiment, by introducing the weight of each monitoring index, the index health value of the target transaction data under each monitoring index can be determined according to the information such as the importance degree of each monitoring index, so as to ensure the accuracy of the index health value of the target transaction data under each monitoring index.
In order to improve the timeliness of abnormal processing of transaction data, after abnormal transaction data is determined, on the basis of the above embodiment, an abnormal factor determining manner is provided in this embodiment, as shown in fig. 5, and specifically includes the following steps:
s501, comparing the index health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index with the health threshold value corresponding to each monitoring index.
Optionally, for each monitoring index, a health threshold corresponding to the monitoring index may be preset according to the range of the historical health value under each monitoring index. The historical health value refers to the health value of historical target transaction data under each monitoring index.
Furthermore, after the abnormal transaction data is determined, the health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index can be compared with the health threshold value corresponding to each monitoring index, so as to obtain a comparison result.
S502, taking the monitoring index with the index health value smaller than the corresponding health threshold value as an abnormal factor corresponding to abnormal transaction data.
The abnormal factors can comprise monitoring indexes corresponding to index data which causes the abnormality of the original transaction data; further, the anomaly factor may include only one monitoring index, or may include a plurality of monitoring indexes.
Specifically, for each abnormal transaction data, after obtaining a comparison result of an index health value of target transaction data corresponding to the abnormal transaction data under each monitoring index and a health threshold corresponding to each monitoring index, taking the monitoring index with the index health value smaller than the corresponding health threshold as an abnormal factor in the abnormal transaction data.
Further, after determining the abnormal factors in the abnormal transaction data, the abnormal transaction data and the abnormal factors in the abnormal transaction data can be simultaneously sent to the terminal of the operation and maintenance personnel, so that the operation and maintenance personnel can check conveniently.
In this embodiment, by comparing the indicator health value corresponding to each monitoring indicator in the abnormal transaction data with the health threshold value corresponding to each monitoring indicator, an abnormal factor is introduced, so that the operation and maintenance personnel can quickly locate the abnormal cause, and the timeliness of abnormal processing of the transaction data is improved.
Fig. 6 is a schematic flow chart of a data monitoring method in another embodiment, and on the basis of the foregoing embodiment, this embodiment provides an alternative example of the data monitoring method. With reference to fig. 6, the specific implementation procedure is as follows:
s601, determining index data of the original transaction data under each monitoring index aiming at each original transaction data in the original transaction data set, and splicing the index data of the original transaction data under each monitoring index to obtain target transaction data corresponding to the original transaction data.
S602, for each monitoring index, constructing an index data set according to index data of each target transaction data under the monitoring index, and constructing an isolated forest corresponding to the monitoring index according to the index data set based on an isolated forest algorithm.
The method comprises the steps of constructing an isolated forest corresponding to the monitoring index according to an index data set based on an isolated forest algorithm, selecting different index data from the index data set as partition points, and dividing the index data set by adopting the partition points to obtain the isolated forest corresponding to the monitoring index.
S603, determining the index health value of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index aiming at each target transaction data, and taking the sum of the index health values of the target transaction data under each monitoring index as the comprehensive health value of the target transaction data.
Specifically, for each monitoring index, determining the height value of the target transaction data under each isolated tree according to the tree structure of each isolated tree in the isolated forest corresponding to the monitoring index; taking the average value of the height values of the target transaction data under each isolated tree as the average height of the target transaction data under the monitoring index; and determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data under each monitoring index.
S604, determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
S605, comparing the index health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index with the health threshold value corresponding to each monitoring index, and taking the monitoring index with the index health value smaller than the corresponding health threshold value as the abnormal factor corresponding to the abnormal transaction data.
The specific process of S601 to S605 may refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, and will not be described herein.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a data monitoring device for realizing the above related data monitoring method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the data monitoring device or devices provided below may be referred to the limitation of the data monitoring method hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 7, there is provided a data monitoring apparatus 1 including: a data processing module 10, a forest construction module 20, a health value determination module 30, and an anomaly determination module 40, wherein:
the data processing module 10 is configured to process each original transaction data in the original transaction data set according to at least one monitoring index, so as to obtain target transaction data corresponding to each original transaction data;
the forest construction module 20 is configured to construct an isolated forest corresponding to each monitoring index according to each target transaction data based on an isolated forest algorithm;
the health value determining module 30 is configured to determine a comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
the anomaly determination module 40 is configured to determine anomaly transaction data in each of the original transaction data according to the comprehensive health value of each of the target transaction data.
In one embodiment, the data processing module 10 is specifically configured to:
determining index data of the original transaction data under each monitoring index aiming at each original transaction data in the original transaction data set; and splicing the index data of the original transaction data under each monitoring index to obtain target transaction data corresponding to the original transaction data.
In one embodiment, as shown in FIG. 8, the forest construction module 20 includes:
a data set construction unit 21 for constructing an index data set according to the index data of each target transaction data under the monitoring index for each monitoring index;
the forest construction unit 22 is configured to select different index data from the index data set as the division points, and divide the index data set by using the division points to obtain an isolated forest corresponding to the monitoring index.
In one embodiment, as shown in FIG. 9, the health value determination module 30 includes, on the basis of FIG. 7:
a first determining unit 31, configured to determine, for each target transaction data, an indicator health value of the target transaction data under each monitoring indicator according to the isolated forest corresponding to each monitoring indicator;
the second determining unit 32 is configured to use the sum of the indicator health values of the target transaction data under each monitoring indicator as the integrated health value of the target transaction data.
In one embodiment, the first determining unit 31 includes:
the average height determining subunit is used for determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index;
And the health value determining subunit is used for determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data under each monitoring index.
In one embodiment, the average height determination subunit is specifically configured to:
aiming at each monitoring index, determining the height value of the target transaction data under each isolated tree according to the tree structure of each isolated tree in the isolated forest corresponding to the monitoring index; and taking the average value of the height values of the target transaction data under each isolated tree as the average height of the target transaction data under the monitoring index.
In one embodiment, the data monitoring device 1 further comprises a factor determination module, specifically configured to:
comparing the health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index with the health threshold value corresponding to each monitoring index; and taking the monitoring index with the index health value smaller than the corresponding health threshold value as an abnormal factor corresponding to the abnormal transaction data.
The respective modules in the data monitoring apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data such as original transaction data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data monitoring method.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data;
determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
In one embodiment, when the processor executes logic in the computer program for processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data, the following steps are specifically implemented:
Determining index data of the original transaction data under each monitoring index aiming at each original transaction data in the original transaction data set; and splicing the index data of the original transaction data under each monitoring index to obtain target transaction data corresponding to the original transaction data.
In one embodiment, when the processor executes the logic of the isolated forest corresponding to each monitoring index according to each target transaction data based on the isolated forest algorithm in the computer program, the following steps are specifically implemented:
aiming at each monitoring index, constructing an index data set according to index data of each target transaction data under the monitoring index; and selecting different index data from the index data set as partition points, and partitioning the index data set by adopting the partition points to obtain an isolated forest corresponding to the monitoring index.
In one embodiment, when the processor executes logic in the computer program for determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index, the following steps are specifically implemented:
aiming at each target transaction data, determining an index health value of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index; and taking the sum of the index health values of the target transaction data under each monitoring index as the comprehensive health value of the target transaction data.
In one embodiment, when the processor executes logic in the computer program for determining the indicator health value of the target transaction data under each monitoring indicator according to the isolated forest corresponding to each monitoring indicator, the following steps are specifically implemented:
determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index; and determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data under each monitoring index.
In one embodiment, the processor executes logic in the computer program for determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index, and specifically implements the following steps:
aiming at each monitoring index, determining the height value of the target transaction data under each isolated tree according to the tree structure of each isolated tree in the isolated forest corresponding to the monitoring index; and taking the average value of the height values of the target transaction data under each isolated tree as the average height of the target transaction data under the monitoring index.
In one embodiment, the following steps are embodied when the processor executes a computer program:
Comparing the index health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index with the health threshold value corresponding to each monitoring index; and taking the monitoring index with the index health value smaller than the corresponding health threshold value as an abnormal factor corresponding to the abnormal transaction data.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data;
determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
In one embodiment, the code logic for processing each original transaction data in the set of original transaction data according to at least one monitoring indicator to obtain the target transaction data corresponding to each original transaction data is executed by the processor, and specifically implements the following steps:
Determining index data of the original transaction data under each monitoring index aiming at each original transaction data in the original transaction data set; and splicing the index data of the original transaction data under each monitoring index to obtain target transaction data corresponding to the original transaction data.
In one embodiment, when the code logic of constructing the isolated forest corresponding to each monitoring index according to each target transaction data based on the isolated forest algorithm in the computer program is executed by the processor, the following steps are specifically implemented:
aiming at each monitoring index, constructing an index data set according to index data of each target transaction data under the monitoring index; and selecting different index data from the index data set as partition points, and partitioning the index data set by adopting the partition points to obtain an isolated forest corresponding to the monitoring index.
In one embodiment, the code logic in the computer program for determining the integrated health value of each target transaction data according to the isolated forest corresponding to each monitoring index is executed by the processor, and specifically implements the following steps:
aiming at each target transaction data, determining an index health value of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index; and taking the sum of the index health values of the target transaction data under each monitoring index as the comprehensive health value of the target transaction data.
In one embodiment, the code logic in the computer program for determining the indicator health value of the target transaction data under each monitoring indicator according to the isolated forest corresponding to each monitoring indicator is executed by the processor, and specifically implements the following steps:
determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index; and determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data under each monitoring index.
In one embodiment, the code logic in the computer program for determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index is executed by the processor, and specifically implements the following steps:
aiming at each monitoring index, determining the height value of the target transaction data under each isolated tree according to the tree structure of each isolated tree in the isolated forest corresponding to the monitoring index; and taking the average value of the height values of the target transaction data under each isolated tree as the average height of the target transaction data under the monitoring index.
In one embodiment, the code logic in the computer program, when executed by the processor, performs the steps of:
Comparing the index health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index with the health threshold value corresponding to each monitoring index; and taking the monitoring index with the index health value smaller than the corresponding health threshold value as an abnormal factor corresponding to the abnormal transaction data.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data;
determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
In one embodiment, when the computer program is executed by the processor to process each original transaction data in the original transaction data set according to at least one monitoring index to obtain the target transaction data corresponding to each original transaction data, the following steps are specifically implemented:
Determining index data of the original transaction data under each monitoring index aiming at each original transaction data in the original transaction data set; and splicing the index data of the original transaction data under each monitoring index to obtain target transaction data corresponding to the original transaction data.
In one embodiment, when the computer program is executed by the processor to construct an isolated forest corresponding to each monitoring index according to each target transaction data based on an isolated forest algorithm, the following steps are specifically implemented:
aiming at each monitoring index, constructing an index data set according to index data of each target transaction data under the monitoring index; and selecting different index data from the index data set as partition points, and partitioning the index data set by adopting the partition points to obtain an isolated forest corresponding to the monitoring index.
In one embodiment, when the computer program is executed by the processor to determine the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index, the following steps are specifically implemented:
aiming at each target transaction data, determining an index health value of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index; and taking the sum of the index health values of the target transaction data under each monitoring index as the comprehensive health value of the target transaction data.
In one embodiment, when the computer program is executed by the processor to determine the index health value of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index, the following steps are specifically implemented:
determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index; and determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data under each monitoring index.
In one embodiment, the computer program is executed by the processor to determine the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index, and specifically implement the following steps:
aiming at each monitoring index, determining the height value of the target transaction data under each isolated tree according to the tree structure of each isolated tree in the isolated forest corresponding to the monitoring index; and taking the average value of the height values of the target transaction data under each isolated tree as the average height of the target transaction data under the monitoring index.
In one embodiment, the computer program, when executed by a processor, performs the steps of:
Comparing the index health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index with the health threshold value corresponding to each monitoring index; and taking the monitoring index with the index health value smaller than the corresponding health threshold value as an abnormal factor corresponding to the abnormal transaction data.
It should be noted that, the data (including, but not limited to, original transaction data, monitoring indexes, etc.) related to the present application are all data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (11)
1. A method of data monitoring, the method comprising:
processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
based on an isolated forest algorithm, constructing an isolated forest corresponding to each monitoring index according to each target transaction data;
determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
And determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
2. The method of claim 1, wherein the processing each original transaction data in the original transaction data set according to the at least one monitoring indicator to obtain target transaction data corresponding to each original transaction data comprises:
determining index data of the original transaction data under each monitoring index aiming at each original transaction data in the original transaction data set;
and splicing the index data of the original transaction data under each monitoring index to obtain target transaction data corresponding to the original transaction data.
3. The method of claim 1, wherein the constructing an isolated forest corresponding to each monitoring index based on the isolated forest algorithm according to each target transaction data comprises:
aiming at each monitoring index, constructing an index data set according to index data of each target transaction data under the monitoring index;
and selecting different index data from the index data set as dividing points, and dividing the index data set by adopting the dividing points to obtain an isolated forest corresponding to the monitoring index.
4. A method according to claim 3, wherein determining the integrated health value of each target transaction data based on the isolated forest corresponding to each monitoring indicator comprises:
aiming at each target transaction data, determining an index health value of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index;
and taking the sum of the index health values of the target transaction data under each monitoring index as the comprehensive health value of the target transaction data.
5. The method of claim 4, wherein determining the indicator health value of the target transaction data under each monitoring indicator according to the isolated forest corresponding to each monitoring indicator comprises:
determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index;
and determining the index health value of the target transaction data under each monitoring index according to the weight of each monitoring index and the average height of the target transaction data under each monitoring index.
6. The method of claim 5, wherein determining the average height of the target transaction data under each monitoring index according to the isolated forest corresponding to each monitoring index comprises:
Aiming at each monitoring index, determining the height value of the target transaction data under each isolated tree according to the tree structure of each isolated tree in the isolated forest corresponding to the monitoring index;
and taking the average value of the height values of the target transaction data under each isolated tree as the average height of the target transaction data under the monitoring index.
7. The method according to claim 4, wherein the method further comprises:
comparing the index health value of the target transaction data corresponding to the abnormal transaction data under each monitoring index with the health threshold value corresponding to each monitoring index;
and taking the monitoring index with the index health value smaller than the corresponding health threshold value as an abnormal factor corresponding to the abnormal transaction data.
8. A data monitoring device, the device comprising:
the data processing module is used for processing each original transaction data in the original transaction data set according to at least one monitoring index to obtain target transaction data corresponding to each original transaction data;
the forest construction module is used for constructing an isolated forest corresponding to each monitoring index according to each target transaction data based on an isolated forest algorithm;
The health value determining module is used for determining the comprehensive health value of each target transaction data according to the isolated forest corresponding to each monitoring index;
and the abnormality determining module is used for determining abnormal transaction data in the original transaction data according to the comprehensive health value of each target transaction data.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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