CN109597745B - Abnormal data processing method and device - Google Patents
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Abstract
The invention provides an abnormal data processing method and device, the method is applied to the technical field of data processing, and the method comprises the following steps: acquiring data to be processed; sorting the data points in the data to be processed according to a preset sorting method; if the sorting sequence number of the data points is smaller than a preset threshold value, determining the data points as abnormal data; and correcting the abnormal data according to a preset correction method. The abnormal data processing method and device provided by the invention can accurately detect and correct the abnormal data in the data set.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an abnormal data processing method and device.
Background
In reality, due to human errors or natural errors, abnormal data values are generated, and the probability of occurrence of abnormal data and occurrence of data conflicts is greatly increased in a multi-data source environment. How to deal with these outliers is an important issue for data cleansing.
In data processing, particularly when function fitting is performed, the occurrence of abnormal points can not only greatly change the effect of function fitting, but also sometimes cause the gradient of the function to generate singular gradient, so that the algorithm is easily stopped, and the function relation among the study variables is influenced. In order to effectively avoid the loss caused by the abnormal points, a certain method is needed to be adopted for processing the abnormal points. However, in many cases, the detection of abnormal data is too dependent on the distribution of the data set itself, and it is difficult to accurately detect and correct the abnormal data in the data set.
Disclosure of Invention
The invention aims to provide an abnormal data processing method and device, which are used for solving the technical problem that the abnormal data processing cannot be accurately performed in the prior art.
In a first aspect of an embodiment of the present invention, there is provided an abnormal data processing method, including:
acquiring data to be processed;
sorting the data points in the data to be processed according to a preset sorting method;
if the sorting sequence number of the data points is smaller than a preset threshold value, determining the data points as abnormal data;
and correcting the abnormal data according to a preset correction method.
In a second aspect of an embodiment of the present invention, there is provided an abnormal data processing apparatus, the apparatus including:
the data acquisition module is used for acquiring data to be processed;
the sorting module is used for sorting the data points in the data to be processed according to a preset sorting method;
the detection module is used for determining the data point as abnormal data if the sequence number of the data point is smaller than a preset threshold value;
and the correction module is used for correcting the abnormal data according to a preset correction method.
In a third aspect of the embodiment of the present invention, there is provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the above-mentioned abnormal data processing method when executing the computer program.
In a fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above-described abnormal data processing method.
The method and the device for processing the abnormal data have the beneficial effects that: according to the method and the device for processing the abnormal data, the data to be processed is ordered through the preset ordering method, and the preset threshold value is determined according to the abnormal data detection requirement, so that the abnormal data in the data to be processed is determined according to the preset threshold value, and the abnormal data is corrected. Because the preset sorting method does not depend on the distribution of the data to be processed, the abnormal data in the data to be processed can be accurately detected according to the sorting result of the preset sorting method, and the abnormal data can be corrected according to the detection result.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart illustrating an abnormal data processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an abnormal data processing method according to another embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for processing abnormal data according to still another embodiment of the present invention;
FIG. 4 is a flowchart illustrating an abnormal data processing method according to another embodiment of the present invention;
FIG. 5 is a flowchart illustrating an abnormal data processing method according to another embodiment of the present invention;
FIG. 6 is a block diagram illustrating an abnormal data processing apparatus according to an embodiment of the present invention;
fig. 7 is a schematic block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a flow chart of an abnormal data processing method according to an embodiment of the invention is shown. The method comprises the following steps:
s101: and obtaining data to be processed.
In this embodiment, if the data to be processed is semi-structured data, the data to be processed is obtained based on the content and the structure frame. If the data to be processed is unstructured data, relevant fields in the data to be processed are identified by adopting a fuzzy matching method, so that the data to be processed of each field is obtained.
S102: and ordering the data points in the data to be processed according to a preset ordering method.
In this embodiment, the preset sorting method sorts the data points according to the average value of the neighbor distances of the preset values of the data points in the data to be processed.
S103: if the sorting sequence number of the data points is smaller than the preset threshold value, determining the data points as abnormal data.
In this embodiment, after the data points in the data to be processed are sorted in descending order, a preset threshold is determined according to the actual data cleaning requirement. For example, the data to be processed includes 100 data points, and only 95% of the data is needed after the data is cleaned, the preset threshold may be determined to be 5, that is, after the data points are sorted in a descending order, the first five points are determined to be outliers, and the outliers are corrected as abnormal data.
S104: and correcting the abnormal data according to a preset correction method.
In this embodiment, the abnormal data is corrected according to the data type of the abnormal data, for example, if the abnormal data is numerical data, the abnormal data is corrected according to the average value of the same attribute data corresponding to the abnormal data. If the abnormal data is non-numerical data, correcting the abnormal data according to the attribute value with the highest occurrence frequency in the same attribute data corresponding to the abnormal data.
As can be seen from the above description, the method for processing abnormal data according to the embodiment of the present invention sorts the data to be processed by a preset sorting method, and then determines a preset threshold according to the detection requirement of the abnormal data, so as to determine the abnormal data in the data to be processed according to the preset threshold and correct the abnormal data. Because the preset sorting method does not depend on the distribution of the data to be processed, the abnormal data in the data to be processed can be accurately detected according to the sorting result of the preset sorting method, and the abnormal data can be corrected according to the detection result.
Referring to fig. 1 and fig. 2 together, fig. 2 is a flow chart of an abnormal data processing method according to another embodiment of the present application. Based on the above embodiment, step S102 is described in detail as follows:
s201: and determining the average value of the neighbor distances of the preset value of each data point in the data to be processed.
In this embodiment, a neighbor distance average value of a preset value of each data point in the data to be processed is determined, that is, a distance of the first five data points closest to each data point in the data to be processed is calculated, and an average value of the five closest distances, that is, a neighbor distance average value, is calculated. For example, when the preset value is 5, the first five nearest distances from the data point are A, B, C, D and E, respectively, where a < B < C < D < E < γ, γ is other distances than the first five nearest distances, and then the neighbor distance average is (a+b+c+d+e)/(5).
In this embodiment, the nearest neighbor distance may be calculated using the euclidean distance.
S202: and ordering the data points in the data to be processed in a descending order according to the average value of the neighbor distances.
In this embodiment, if the detected data point in the data to be processed is abnormal data, the distance between the detected data point and other data points is far, so that the data points in the data to be processed can be sorted in descending order according to the average value of the neighbor distances, and the data points with the front sorting positions are outliers, that is, abnormal data.
Referring to fig. 1 and fig. 3 together, fig. 3 is a flowchart illustrating a method for processing abnormal data according to another embodiment of the present invention, where the method for determining a preset value may include:
s301: training sample data is obtained, the training sample data including normal data points and abnormal data points.
In this embodiment, the normal data points are used to establish a standard neighbor distance average, i.e., the neighbor distance average between the normal data points is used as a standard for determining other neighbor distance averages. The entire sample data set is used to make the determination of the preset value.
S302: an initial preset value is set.
In this embodiment, the initial preset value is set first, and this embodiment can determine the initial preset value as 90% or 95% or 98% of the number of data points in the sample data.
S303: and updating the initial preset value according to the training sample data to obtain a preset value.
In this embodiment, the initial preset value is continuously updated to the preset value by setting the initial preset value and stopping the update of the initial preset value.
Referring to fig. 1 and fig. 4 together, fig. 4 is a flow chart of an abnormal data processing method according to another embodiment of the present application. Based on the above embodiment, step S303 is described in detail as follows:
s401: and determining a first neighbor distance average value of all the normal data points according to the normal data points.
In this embodiment, the first neighbor distance average value is a standard neighbor distance average value, and whether to stop updating the initial preset value can be determined according to the first neighbor distance average value.
S402: and determining a second neighbor distance average value of initial preset values of all data points in the sample data.
In this embodiment, the second neighbor distance average value of all the data points may be calculated according to the initial preset value, and used for comparing with the standard neighbor distance average value to determine whether the initial preset value needs to be updated.
S403: if the difference value between the first neighbor distance average value and the second neighbor distance average value meets the preset error range, the initial preset value is determined to be the preset value.
In this embodiment, the condition for stopping the update of the initial preset value is that the difference between the first neighboring distance average value and the second neighboring distance average value satisfies the preset error range, and at this time, the current initial preset value may be determined as the preset value.
Please refer to fig. 1 and fig. 4 together, which are a specific implementation of the method for processing abnormal data according to an embodiment of the present invention. On the basis of the above embodiment, step S303 may further include:
s404: if the difference value between the first neighbor distance average value and the second neighbor distance average value does not meet the preset error range, the initial preset value is reduced, and the step of determining the second neighbor distance average value of the initial preset value of all data points in the sample data is carried out in a returning mode.
In this embodiment, if the difference between the first neighbor distance average value and the second neighbor distance average value does not satisfy the preset error range, it indicates that the initial preset value does not reach the preset value standard, at this time, the initial preset value is reduced, the second neighbor distance average value of all the data points of the initial preset value is continuously calculated, and then compared with the first neighbor distance average value until the difference between the first neighbor distance average value and the second neighbor distance average value satisfies the preset error range, at this time, it indicates that the initial preset value reaches the preset value standard, and the current initial preset value is used as the preset value.
From the above description, the method for determining the preset value provided by the embodiment of the invention is based on data training and verification, and can effectively improve the accuracy of abnormal point detection.
Referring to fig. 1 to fig. 5 together, fig. 5 is a flowchart illustrating an abnormal data processing method according to another embodiment of the present application. Based on the above embodiment, step S104 is described in detail as follows:
s501: if the abnormal data is numerical data, the abnormal data is corrected according to the average value of the same attribute data corresponding to the abnormal data.
S502: if the abnormal data is non-numerical data, correcting the abnormal data according to the attribute value with the highest occurrence frequency in the same attribute data corresponding to the abnormal data.
In the present embodiment, the attribute of the abnormal data is divided into a numerical attribute and a non-numerical attribute to be processed respectively. If the outlier is numerical, replacing the outlier according to the average value of the values of the attribute in all other objects; if the outlier is non-numeric, the outlier is replaced with the value of the attribute that has the greatest number of values among all other objects according to the statistical mode principle
Corresponding to the method for processing abnormal data in the above embodiment, fig. 6 is a block diagram of an apparatus for processing abnormal data according to an embodiment of the present invention. For convenience of explanation, only portions relevant to the embodiments of the present invention are shown. Referring to fig. 6, the apparatus may include: a data acquisition module 10, a sorting module 20, a detection module 30 and a correction module 40.
The data acquisition module 10 is configured to acquire data to be processed.
The sorting module 20 is configured to sort the data points in the data to be processed according to a preset sorting method.
The detection module 30 is configured to determine that the data point is abnormal data if the ordering number of the data point is smaller than a preset threshold.
The correction module 40 is configured to correct the abnormal data according to a preset correction method.
Referring to fig. 6, in another embodiment of the present invention, the generating module 20 may include:
the average value determining unit 21 is configured to determine an average value of neighbor distances of preset values of each data point in the data to be processed.
The sorting unit 22 is configured to sort the data points in the data to be processed in descending order according to the average value of the neighbor distances.
Referring to fig. 6, in still another embodiment of the present invention, the abnormal data processing apparatus may further include:
the sample acquisition module 50 is configured to acquire training sample data, where the training sample data includes normal data points and abnormal data points.
A determining module 60 for setting an initial preset value.
The updating module 70 is configured to update the initial preset value according to the training sample data.
Referring to fig. 6, in yet another embodiment of the present invention, the update module 70 may include:
a first mean unit 71, configured to determine a first neighbor distance mean of all normal data points according to the normal data points.
A second average unit 72, configured to determine a second neighbor distance average value of initial preset values of all data points in the sample data.
The first determining unit 73 is configured to determine an initial preset value as a preset value if a difference between the first neighboring distance average and the second neighboring distance average meets a preset error range.
Referring to fig. 6, in yet another embodiment of the present invention, the update module 70 may further include:
the second judging unit 74 is configured to reduce the initial preset value if the difference between the first neighbor distance average value and the second neighbor distance average value does not satisfy the preset error range, and return to performing the step of determining the second neighbor distance average value of the initial preset value of all data points in the sample data.
Referring to fig. 6, in yet another embodiment of the present invention, the correction module 40 may include:
the first correction unit 41 is configured to correct the abnormal data according to an average value of the same attribute data corresponding to the abnormal data if the abnormal data is numerical data.
The second correction unit 42 is configured to correct the abnormal data according to the attribute value with the highest occurrence frequency in the same attribute data corresponding to the abnormal data if the abnormal data is non-numeric data.
Referring to fig. 7, fig. 7 is a schematic block diagram of a terminal device according to an embodiment of the present invention. The terminal 600 in the present embodiment as shown in fig. 7 may include: one or more processors 601, one or more input devices 602, one or more output devices 603, and one or more memories 604. The processor 601, the input device 602, the output device 603, and the memory 604 communicate with each other via a communication bus 605. The memory 604 is used to store a computer program comprising program instructions. The processor 601 is operative to execute program instructions stored in the memory 604. Wherein the processor 601 is configured to invoke program instructions to perform the following functions of the modules/units in the above-described device embodiments, such as the functions of the modules 10 to 70 shown in fig. 6.
It should be appreciated that in embodiments of the present invention, the processor 601 may be a central processing unit (Central Processing Unit, CPU), which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 602 may include a touch pad, a fingerprint sensor (for collecting fingerprint information of a user and direction information of a fingerprint), a microphone, etc., and the output device 603 may include a display (LCD, etc.), a speaker, etc.
The memory 604 may include read only memory and random access memory and provides instructions and data to the processor 601. A portion of memory 604 may also include non-volatile random access memory. For example, the memory 604 may also store information of device type.
In a specific implementation, the processor 601, the input device 602, and the output device 603 described in the embodiments of the present invention may execute the implementation described in the first embodiment and the second embodiment of the method for processing abnormal data provided in the embodiments of the present invention, and may also execute the implementation of the terminal described in the embodiments of the present invention, which is not described herein again.
In another embodiment of the present invention, a computer readable storage medium is provided, where the computer readable storage medium stores a computer program, where the computer program includes program instructions, where the program instructions, when executed by a processor, implement all or part of the procedures in the method embodiments described above, or may be implemented by instructing related hardware by the computer program, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by the processor, implements the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
The computer readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, such as a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store a computer program and other programs and data required for the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working procedures of the terminal and the unit described above may refer to the corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In several embodiments provided in the present application, it should be understood that the disclosed terminal and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (1)
1. A terminal device comprising a memory, a processor and a computer program stored in the memory and operable on the processor, characterized in that the processor, input device, output device and the memory communicate via a communication bus;
the memory is used for providing instructions and data to the processor;
the processor, when executing the computer program, performs the steps of the method of:
under the environment of multiple data sources, obtaining data to be processed; if the data to be processed is semi-structured data, acquiring the data to be processed based on the content of the data to be processed and the structure frame; if the data to be processed is unstructured data, identifying relevant fields in the data to be processed by adopting a fuzzy matching method, so as to obtain the data to be processed of each field;
sorting the data points in the data to be processed according to a preset sorting method;
if the sorting sequence number of the data points is smaller than a preset threshold value, determining the data points as abnormal data;
correcting the abnormal data according to a preset correction method;
the correcting the abnormal data according to a preset correcting method comprises the following steps:
correcting the abnormal data according to the data type of the abnormal data, and if the abnormal data is numerical data, correcting the abnormal data according to the average value of the same attribute data corresponding to the abnormal data; if the abnormal data are non-numerical data, correcting the abnormal data according to the attribute value with the highest occurrence frequency in the same attribute data corresponding to the abnormal data;
the preset sorting method is to sort the data points according to the average value of the neighbor distances of preset values of the data points in the data to be processed;
the method for determining the preset value comprises the following steps:
acquiring training sample data, wherein the training sample data comprises normal data points and abnormal data points;
setting an initial preset value;
updating an initial preset value according to the training sample data to obtain a preset value;
determining a first neighbor distance average value of all normal data points according to the normal data points;
determining a second neighbor distance mean value of initial preset values of all data points in the sample data;
if the difference value between the first neighbor distance average value and the second neighbor distance average value meets a preset error range, determining an initial preset value as a preset value;
if the difference value between the first neighbor distance average value and the second neighbor distance average value does not meet the preset error range, reducing the initial preset value, taking the reduced initial preset value as the initial preset value, and returning to the step of determining the second neighbor distance average value of the initial preset values of all data points in the sample data to continue to execute.
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| CN113828529B (en) * | 2021-11-26 | 2022-02-11 | 南京派格测控科技有限公司 | Processing method and system based on concatenation abnormity of serial measurement data |
| CN117272216B (en) * | 2023-11-22 | 2024-02-09 | 中国建材检验认证集团湖南有限公司 | Data analysis method for automatic flow monitoring station and manual water gauge observation station |
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