CN109684311A - Abnormal deviation data examination method and device - Google Patents
Abnormal deviation data examination method and device Download PDFInfo
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- CN109684311A CN109684311A CN201811488042.9A CN201811488042A CN109684311A CN 109684311 A CN109684311 A CN 109684311A CN 201811488042 A CN201811488042 A CN 201811488042A CN 109684311 A CN109684311 A CN 109684311A
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Abstract
The present invention provides a kind of abnormal deviation data examination method and device, this method is applied to technical field of data processing, which comprises obtains pending data；Isolated woods is generated according to the pending data；The depth-averaged value of each data point in the pending data is determined based on the isolated woods；If the depth-averaged value of a certain data point is greater than predetermined depth value, it is determined that the data point is abnormal data.Abnormal deviation data examination method and device provided by the invention can be realized the quick detection of pending data exceptional value.
Description
Technical field
The invention belongs to technical field of data processing, are to be related to a kind of abnormal deviation data examination method and dress more specifically
It sets.
Background technique
In reality, since mistake or natural mistake will lead to generation data outliers, in the environment of multi-data source
Under, there is data exception and generate the probability of data collision greatly increasing.How to handle these exceptional values is data cleansing institute
The important topic faced.
In data handling, especially when making Function Fitting, the appearance of abnormal point not only can significantly change function
The effect of fitting, and the gradient of function can also be made unusual gradient occur sometimes, it is easy to lead to the termination of algorithm, thus shadow
Ring the functional relation between research variable.In order to effectively avoid loss caused by these abnormal points, it would be desirable to take certain
Method it is handled.But in the case where multi-data source, big data quantity, lack in the prior art it is a kind of quickly detect it is different
The method of constant value.
Summary of the invention
The purpose of the present invention is to provide a kind of abnormal deviation data examination method and devices, existing in the prior art to solve
The technical issues of anomaly data detection can not quickly be carried out.
The embodiment of the present invention in a first aspect, providing a kind of abnormal deviation data examination method, which comprises
Obtain pending data；
Isolated woods is generated according to the pending data；
The depth-averaged value of each data point in the pending data is determined based on the isolated woods；
If the depth-averaged value of a certain data point is greater than predetermined depth value, it is determined that the data point is abnormal data.
The second aspect of the embodiment of the present invention, provides a kind of anomaly data detection device, and described device includes:
Data acquisition module, for obtaining pending data；
Generation module, for generating isolated woods according to the pending data；
Spider module, for determining the depth-averaged of each data point in the pending data based on the isolated woods
Value；
Detection module, if the depth-averaged value for data point is greater than predetermined depth value, it is determined that the data point is different
Regular data.
The third aspect of the embodiment of the present invention, provides a kind of terminal device, including memory, processor and is stored in
In the memory and the computer program that can run on the processor, when the processor executes the computer program
The step of realizing above-mentioned abnormal deviation data examination method.
The fourth aspect of the embodiment of the present invention, provides a kind of computer readable storage medium, described computer-readable to deposit
Storage media is stored with computer program, and the computer program realizes above-mentioned abnormal deviation data examination method when being executed by processor
The step of.
The beneficial effect of abnormal deviation data examination method and device provided by the invention is: abnormal data provided by the invention
Detection method and device first establish isolated tree, the isolated tree composition of multiple dimensions from multiple dimensions respectively according to pending data
Isolated woods reuses pending data and traverses the depth-averaged value that isolated woods determines pending data, according to the depth-averaged value
Abnormal data is determined to realize the quick detection to abnormal data.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skill in the art without creative efforts, can also be attached according to these
Figure obtains other attached drawings.
Fig. 1 is the flow diagram for the abnormal deviation data examination method that one embodiment of the invention provides；
Fig. 2 be another embodiment of the present invention provides abnormal deviation data examination method flow diagram；
Fig. 3 is the flow diagram for the abnormal deviation data examination method that yet another embodiment of the invention provides；
Fig. 4 is the flow diagram for the abnormal deviation data examination method that further embodiment of this invention provides；
Fig. 5 is the flow diagram for the abnormal deviation data examination method that further embodiment of this invention provides；
Fig. 6 is the structural block diagram for the anomaly data detection device that one embodiment of the invention provides；
Fig. 7 is the schematic block diagram for the terminal device that one embodiment of the invention provides.
Specific embodiment
In order to which technical problems, technical solutions and advantages to be solved are more clearly understood, tie below
Accompanying drawings and embodiments are closed, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only
To explain the present invention, it is not intended to limit the present invention.
Referring to FIG. 1, the flow diagram of the abnormal deviation data examination method provided for one embodiment of the invention.This method packet
It includes:
S101: pending data is obtained.
In the present embodiment, pending data is divided according to dimension, the pending data of each dimension can give birth to
At an isolated tree, the more isolated trees that the pending data of all dimensions generates form isolated woods.
S102: isolated woods is generated according to pending data.
In the present embodiment, using pending data as training data, multiple isolated trees, multiple isolated tree structures can be trained
At isolated woods.
S103: the depth-averaged value of each data point in pending data is determined based on isolated woods.
In the present embodiment, that completes exceptional data point isolates required division number, that is, the number cut has been greater than
At the isolated required division number of normal data points, therefore can be according to number is divided, i.e., each data point is in the depth isolated in woods
Value determines whether the data point is abnormal data, and the embodiment of the present invention uses depth-averaged value to improve the detection of abnormal data standard
True rate.
Wherein, woods being isolated to be made of more isolated trees, each data point corresponds to a depth value in every isolated tree,
The average value of all isolated tree depth values is the depth-averaged value of data point in isolated woods.For example, including in an isolated woods
There is n isolated tree, depth value of some data point in n isolated tree is respectively D1, D2, D3……Dn, then the depth of woods is isolated
Average value D=(D1+D2+D3……+Dn-1+Dn)÷n。
S104: if the depth-averaged value of a certain data point is greater than predetermined depth value, it is determined that the data point is abnormal data.
In the present embodiment, if the depth-averaged value of data point is greater than default average value, that is, illustrate the data point isolated
Position of the woods compared with shallow-layer, it is determined that the data point is abnormal data.
As can be seen from the above description, abnormal deviation data examination method provided in an embodiment of the present invention is first distinguished according to pending data
Isolated tree is established from multiple dimensions, the isolated tree of multiple dimensions forms isolated woods, reuses pending data and traverses isolated woods
The depth-averaged value for determining pending data determines abnormal data according to the depth-averaged value to realize to the fast of abnormal data
Speed detection.
Please also refer to Fig. 1 and Fig. 2, Fig. 2 is the process for the abnormal deviation data examination method that another embodiment of the application provides
Schematic diagram.On the basis of the above embodiments, step S102 can be described in detail are as follows:
S201: the root node of every isolated tree is determined according to pending data.
It in the present embodiment, can be directly using pending data as sample number if the data volume of pending data is smaller
According to the root node for being put into isolated tree.If the data volume of pending data is larger, part can be randomly choosed from pending data
Data are put into the root node of isolated tree as sample data.
S202: data are carried out to pending data according to default cut point and cut to obtain left child and the right side of every isolated tree
Child generates isolated woods.
In the present embodiment, data cutting each time, i.e., the division of left child and right child are all corresponding in sample data
One default cut point needs constantly to carry out data cutting until left child and the right side to sample data for the foundation for completing isolated tree
Child meets default cutting condition.Wherein, all isolated trees form an isolated woods.
A kind of specific reality please also refer to Fig. 1 and Fig. 2, as abnormal deviation data examination method provided in an embodiment of the present invention
Apply mode.On the basis of the above embodiments, step S102 can also be described in detail are as follows:
S203: if in left child and right child nodes only including a data point, stop data cutting.
In the present embodiment, default cutting condition is in the node of left child and right child only comprising a data
Point stops data cutting, completes the foundation of isolated tree when the data cutting to sample data reaches the default cutting condition.
Please also refer to Fig. 1 and Fig. 3, Fig. 3 is the process for the abnormal deviation data examination method that yet another embodiment of the invention provides
Schematic diagram, on the basis of the above embodiments, this method can also include:
S301: the dimension distribution of pending data is determined.
It in the present embodiment, is the accuracy for guaranteeing anomaly data detection, the embodiment of the present invention treats place using isolated woods
Reason data are detected, rather than are detected using isolated tree to pending data.In order to establish isolated woods, need from different dimensional
Degree establishes isolated tree, and multiple isolated trees form isolated woods, to improve the accuracy of anomaly data detection.
S302: default cut point corresponding to each dimension is determined according to dimension distribution.
In the present embodiment, default cut point corresponding to each dimension is the collection of the every layer data cut point of an isolated tree
It closes.
Please also refer to Fig. 1 and Fig. 4, Fig. 4 is the process for the abnormal deviation data examination method that the another embodiment of the application provides
Schematic diagram.On the basis of the above embodiments, step S302 can be described in detail are as follows:
S401: initial cut point is generated at random in default dimension.
In the present embodiment, the data value of the initial cut generated at random the point is greater than pending data in default dimension
Minimum value, and it is less than the maximum value of pending data in default dimension.
S402: analogue data cutting is carried out to pending data based on initial cut point.
In the present embodiment, analogue data cutting is carried out to pending data using the initial cut point generated at random, directly
The result cut to analogue data meets default cutting result, then stops analogue data cutting.It wherein, can if data volume is larger
The partial data randomly selected in pending data carries out analogue data cutting.
S403: if analogue data cutting result meets default cutting result, it is determined that initial cut point is default cut point.
In the present embodiment, default cutting result is the data volume in left child and the data volume difference in right child
Absolute value is not more than the 5% of root node data amount.When the result of analogue data cutting meets aforementioned default cutting result, then stop
Only analogue data is cut, and is cut using current initial cut point as default cut point.
Please also refer to Fig. 1 to Fig. 5, Fig. 5 is the process for the abnormal deviation data examination method that the another embodiment of the application provides
Schematic diagram.On the basis of the above embodiments, the cutting method of data cutting or analogue data cutting includes:
S501: if data point in pending data is less than default cut point or initial cut point, it is determined that data point is
The left child of present node.
S502: if the data point in pending data is not less than default cut point or initial cut point, it is determined that data point
For the right child of present node.
In the present embodiment, the cutting method of data cutting, i.e., the division methods of left child and right child are as follows: if sample number
Data point in is less than cut point, it is determined that the data point is the left child of current root node.If the data in sample data
Point is greater than or equal to cut point, then using the data point as the right child of current root node.Wherein, sample data can be to be processed
Data can also be the partial data randomly selected in pending data.Cut point is aforementioned default cut point or initial cut point.
Corresponding to the abnormal deviation data examination method of foregoing embodiments, Fig. 6 is the abnormal data that one embodiment of the invention provides
The structural block diagram of detection device.For ease of description, only parts related to embodiments of the present invention are shown.With reference to Fig. 6, the dress
Set may include: data acquisition module 10, generation module 20, spider module 30 and detection module 40.
Wherein, data acquisition module 10, for obtaining pending data.
Generation module 20, for generating isolated woods according to pending data.
Spider module 30, for determining the depth-averaged value of each data point in pending data based on isolated woods.
Detection module 40, if the depth-averaged value for a certain data point is greater than predetermined depth value, it is determined that the data point
For abnormal data.
With reference to Fig. 6, in another embodiment of the present invention, generation module 20 may include:
Root node determination unit 21, for determining the root node of every isolated tree according to pending data.
Child node determination unit 22 cuts to obtain every orphan for carrying out data to pending data according to default cut point
The left child and right child of vertical tree generate isolated woods.
With reference to Fig. 6, in yet another embodiment of the present invention, generation module 20 can also include:
Judging unit 23, if stopping data cutting for only including a data point in left child and right child nodes.
With reference to Fig. 6, in yet another embodiment of the present invention, anomaly data detection device can also include:
Dimension determining module 50, for determining that the dimension of pending data is distributed.
Cut point determining module 60, for determining default cut point corresponding to each dimension according to dimension distribution.
With reference to Fig. 6, in yet another embodiment of the present invention, cut point determining module 60 may include:
First determination unit 61, for generating initial cut point at random in default dimension, the data value of cut point is greater than
The minimum value of pending data in default dimension, and it is less than the maximum value of pending data in default dimension.
Simulating cut unit 62, for carrying out analogue data cutting to pending data based on initial cut point.
Second determination unit 63, if meeting default cutting result for analogue data cutting result, it is determined that initial cut
Point is default cut point.
With reference to Fig. 6, in yet another embodiment of the present invention, the cutting method packet of data cutting or analogue data cutting
It includes:
If the data point in pending data is less than default cut point or initial cut point, it is determined that data point is to work as prosthomere
The left child of point.
If the data point in pending data is not less than default cut point or initial cut point, it is determined that data point is current
The right child of node.
Referring to Fig. 7, Fig. 7 is a kind of schematic block diagram for terminal device that one embodiment of the invention provides.Sheet as shown in Figure 7
Terminal 600 in embodiment may include: one or more processors 601, one or more input equipments 602, one or more
A output equipment 603 and one or more memories 604.Above-mentioned processor 601, input equipment 602, then output equipment 603 and
Memory 604 completes mutual communication by communication bus 605.Memory 604 is for storing computer program, computer journey
Sequence includes program instruction.Processor 601 is used to execute the program instruction of the storage of memory 604.Wherein, processor 601 is configured
For operating the function of each module/unit in above-mentioned each Installation practice, such as mould shown in Fig. 6 below caller instruction execution
The function of block 10 to 60.
It should be appreciated that in embodiments of the present invention, alleged processor 601 can be central processing unit (Central
Processing Unit, CPU), which can also be other general processors, digital signal processor (Digital
Signal Processor, DSP), specific integrated circuit (Application Specific Integrated Circuit,
ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic
Device, discrete gate or transistor logic, discrete hardware components etc..General processor can be microprocessor or this at
Reason device is also possible to any conventional processor etc..
Input equipment 602 may include that Trackpad, fingerprint adopt sensor (for acquiring the finger print information and fingerprint of user
Directional information), microphone etc., output equipment 603 may include display (LCD etc.), loudspeaker etc..
The memory 604 may include read-only memory and random access memory, and to processor 601 provide instruction and
Data.The a part of of memory 604 can also include nonvolatile RAM.For example, memory 604 can also be deposited
Store up the information of device type.
In the specific implementation, processor 601 described in the embodiment of the present invention, input equipment 602, output equipment 603 can
Execute realization described in the first embodiment and second embodiment of abnormal deviation data examination method provided in an embodiment of the present invention
The implementation of terminal described in the embodiment of the present invention also can be performed in mode, and details are not described herein.
A kind of computer readable storage medium is provided in another embodiment of the invention, and computer readable storage medium is deposited
Computer program is contained, computer program includes program instruction, and above-described embodiment side is realized when program instruction is executed by processor
All or part of the process in method can also instruct relevant hardware to complete by computer program, and computer program can
It is stored in a computer readable storage medium, the computer program is when being executed by processor, it can be achieved that above-mentioned each method
The step of embodiment.Wherein, computer program includes computer program code, and computer program code can be source code shape
Formula, object identification code form, executable file or certain intermediate forms etc..Computer-readable medium may include: that can carry meter
Any entity or device of calculation machine program code, recording medium, USB flash disk, mobile hard disk, magnetic disk, CD, computer storage, only
Read memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electricity load
Wave signal, telecommunication signal and software distribution medium etc..It should be noted that the content that computer-readable medium includes can root
Increase and decrease appropriate is carried out according to the requirement made laws in jurisdiction with patent practice, such as in certain jurisdictions, according to vertical
Method and patent practice, computer-readable medium do not include be electric carrier signal and telecommunication signal.
Computer readable storage medium can be the internal storage unit of the terminal of aforementioned any embodiment, such as terminal
Hard disk or memory.Computer readable storage medium is also possible to the External memory equipment of terminal, such as the grafting being equipped in terminal
Formula hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card
(Flash Card) etc..Further, computer readable storage medium can also both include the internal storage unit of terminal or wrap
Include External memory equipment.Computer readable storage medium is for storing other program sum numbers needed for computer program and terminal
According to.Computer readable storage medium can be also used for temporarily storing the data that has exported or will export.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware
With the interchangeability of software, each exemplary composition and step are generally described according to function in the above description.This
A little functions are implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It is considered as beyond the scope of this invention.
It is apparent to those skilled in the art that for convenience of description and succinctly, the end of foregoing description
The specific work process at end and unit, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.
In several embodiments provided herein, it should be understood that disclosed terminal and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of unit, only
A kind of logical function partition, there may be another division manner in actual implementation, for example, multiple units or components can combine or
Person is desirably integrated into another system, or some features can be ignored or not executed.In addition, it is shown or discussed it is mutual it
Between coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or communication link of device or unit
It connects, is also possible to electricity, mechanical or other form connections.
Unit may or may not be physically separated as illustrated by the separation member, shown as a unit
Component may or may not be physical unit, it can and it is in one place, or may be distributed over multiple networks
On unit.It can select some or all of unit therein according to the actual needs to realize the mesh of the embodiment of the present invention
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, is also possible to two or more units and is integrated in one unit.It is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.
More than, only a specific embodiment of the invention, but scope of protection of the present invention is not limited thereto, and it is any to be familiar with
Those skilled in the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or substitutions,
These modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be wanted with right
Subject to the protection scope asked.
Claims (10)
1. a kind of abnormal deviation data examination method characterized by comprising
Obtain pending data；
Isolated woods is generated according to the pending data；
The depth-averaged value of each data point in the pending data is determined based on the isolated woods；
If the depth-averaged value of a certain data point is greater than predetermined depth value, it is determined that the data point is abnormal data.
2. abnormal deviation data examination method as described in claim 1, which is characterized in that the isolated woods includes multiple isolated trees,
It is described to include: according to the isolated woods of pending data generation
The root node of every isolated tree is determined according to the pending data；
Data are carried out to the pending data according to default cut point to cut to obtain the left child and right child of every isolated tree,
Generate the isolated woods.
3. abnormal deviation data examination method as claimed in claim 2, which is characterized in that described to be generated according to the pending data
Isolated woods, further includes:
If in the left child and the right child nodes only including a data point, stop the data cutting.
4. abnormal deviation data examination method as claimed in claim 2, which is characterized in that preset cut point to described wait locate in basis
It is described raw according to the pending data before managing the left child and right child that data carry out the determining isolated tree of data cutting
At isolated woods further include:
Determine the dimension distribution of the pending data；
Default cut point corresponding to each dimension is determined according to dimension distribution.
5. abnormal deviation data examination method as claimed in claim 4, which is characterized in that described to be determined often according to dimension distribution
Default cut point corresponding to a dimension, comprising:
Generate initial cut point at random in default dimension, the data value of the cut point be greater than described in the default dimension to
The minimum value of data is handled, and is less than the maximum value of pending data described in the default dimension；
Analogue data cutting is carried out to the pending data based on the initial cut point；
If the analogue data cutting result meets default cutting result, it is determined that the initial cut point is default cut point.
6. the abnormal deviation data examination method as described in claim 3 or 4 or 5, which is characterized in that the data cutting or the mould
Intending the cutting method that data are cut includes:
If the data point in the pending data is less than the default cut point or initial cut point, it is determined that the number
Strong point is the left child of present node；
If the data point in the pending data is not less than the default cut point or initial cut point, it is determined that described
Data point is the right child of present node.
7. a kind of anomaly data detection device characterized by comprising
Data acquisition module, for obtaining pending data；
Generation module, for generating isolated woods according to the pending data；
Spider module, for determining the depth-averaged value of each data point in the pending data based on the isolated woods；
Detection module, if the depth-averaged value for data point is greater than predetermined depth value, it is determined that the data point is abnormal number
According to.
8. anomaly data detection device as claimed in claim 7, which is characterized in that the generation module includes:
Root node determination unit, for determining the root node of the isolated tree according to the pending data；
Child node determination unit cuts to obtain described isolate for carrying out data to the pending data according to default cut point
The left child and right child of tree.
9. a kind of terminal device, including memory, processor and storage are in the memory and can be on the processor
The computer program of operation, which is characterized in that the processor realizes such as claim 1 to 6 when executing the computer program
The step of any one the method.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In when the computer program is executed by processor the step of any one of such as claim 1 to 6 of realization the method.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110399935A (en) * | 2019-08-02 | 2019-11-01 | 哈工大机器人（合肥）国际创新研究院 | The real-time method for monitoring abnormality of robot and system based on isolated forest machine learning |
| CN110750536A (en) * | 2019-10-11 | 2020-02-04 | 清华大学 | Vibration noise smoothing method and system for attitude time series data |
| CN112990330A (en) * | 2021-03-26 | 2021-06-18 | 国网河北省电力有限公司营销服务中心 | User energy abnormal data detection method and device |
| CN113220796A (en) * | 2020-01-21 | 2021-08-06 | 北京达佳互联信息技术有限公司 | Abnormal business index analysis method and device |
| CN114611616A (en) * | 2022-03-16 | 2022-06-10 | 吕少岚 | Unmanned aerial vehicle intelligent fault detection method and system based on integrated isolated forest |
| TWI780433B (en) * | 2019-12-12 | 2022-10-11 | 大陸商支付寶（杭州）信息技術有限公司 | A method and device for constructing and predicting an isolated forest model based on federated learning |
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| CN108776683A (en) * | 2018-06-01 | 2018-11-09 | 广东电网有限责任公司 | A kind of electric power operation/maintenance data cleaning method based on isolated forest algorithm and neural network |
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108776683A (en) * | 2018-06-01 | 2018-11-09 | 广东电网有限责任公司 | A kind of electric power operation/maintenance data cleaning method based on isolated forest algorithm and neural network |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110399935A (en) * | 2019-08-02 | 2019-11-01 | 哈工大机器人（合肥）国际创新研究院 | The real-time method for monitoring abnormality of robot and system based on isolated forest machine learning |
| CN110750536A (en) * | 2019-10-11 | 2020-02-04 | 清华大学 | Vibration noise smoothing method and system for attitude time series data |
| CN110750536B (en) * | 2019-10-11 | 2020-06-23 | 清华大学 | Vibration noise smoothing method and system for attitude time series data |
| TWI780433B (en) * | 2019-12-12 | 2022-10-11 | 大陸商支付寶（杭州）信息技術有限公司 | A method and device for constructing and predicting an isolated forest model based on federated learning |
| CN113220796A (en) * | 2020-01-21 | 2021-08-06 | 北京达佳互联信息技术有限公司 | Abnormal business index analysis method and device |
| CN112990330A (en) * | 2021-03-26 | 2021-06-18 | 国网河北省电力有限公司营销服务中心 | User energy abnormal data detection method and device |
| CN112990330B (en) * | 2021-03-26 | 2022-09-20 | 国网河北省电力有限公司营销服务中心 | User energy abnormal data detection method and device |
| CN114611616A (en) * | 2022-03-16 | 2022-06-10 | 吕少岚 | Unmanned aerial vehicle intelligent fault detection method and system based on integrated isolated forest |
| CN114611616B (en) * | 2022-03-16 | 2023-02-07 | 吕少岚 | Unmanned aerial vehicle intelligent fault detection method and system based on integrated isolated forest |
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