CN109272059B - Dynamic data classification method and device - Google Patents
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Abstract
The embodiment of the invention provides a dynamic data classification method and device, and relates to the technical field of data classification. The dynamic data classification method comprises the following steps: sequentially acquiring data information; respectively matching a first judgment standard and a second judgment standard according to the sequence of the data information; and adding the data information meeting the first judgment standard or the second judgment standard into the data group. The data set obtained by the method and the device has followability and can improve the accuracy of the judgment standard.
Description
Technical Field
The invention relates to the technical field of data classification, in particular to a dynamic data classification method and device.
Background
In the existing judging method, a data set is generated according to historical data, a judging standard is obtained according to the data set, then current data is matched with the judging standard, and whether the current data is abnormal data or not is judged. In actual operation, the obtained current data can be reasonably changed along with the change of the working condition, and because the data in the data set is not dynamically changed data when the determination standard is determined, the reasonable influence caused by the change of the working condition can not be eliminated, the data which is reasonably changed along with the change of the working condition can be mistakenly determined as abnormal data, and further the accuracy of the existing determination method is low.
Disclosure of Invention
The invention aims to provide a dynamic data classification method and a dynamic data classification device, and a data set obtained by the method and the device has followability and can improve the accuracy of a judgment standard.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides a dynamic data classification method, where the method includes: sequentially acquiring data information; respectively matching a first judgment standard and a second judgment standard according to the sequence of the data information; and adding the data information meeting the first judgment standard or the second judgment standard into the data group.
In a second aspect, an embodiment of the present invention further provides a dynamic data classification apparatus, where the apparatus includes: the acquisition module is used for sequentially acquiring data information; the matching module is used for respectively matching the first judgment standard and the second judgment standard according to the sequence of the data information; and the data group determining module is used for adding the data information meeting the first judgment standard or the second judgment standard into the data group.
According to the dynamic data classification method and device provided by the embodiment of the invention, data information is acquired in sequence, then the first judgment standard and the second judgment standard are respectively matched according to the sequence of the data information, and finally the data information meeting the first judgment standard or the second judgment standard is added into a data group. Therefore, the data information in the data group obtained by the method is dynamically changed and has follow-up property, so that the data group can eliminate the influence caused by the change of working conditions, and the accuracy of the judgment standard can be improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram illustrating an application environment of a dynamic data classification method and apparatus provided in an embodiment of the present invention;
FIG. 2 is a block diagram of an electronic device according to an embodiment of the invention;
FIG. 3 is a flow chart of a dynamic data classification method according to an embodiment of the present invention;
FIG. 4 is a sub-flowchart of step S2 according to an embodiment of the present invention;
FIG. 5 is a sub-flowchart of step S3 according to an embodiment of the present invention;
FIG. 6 is a second flowchart of a dynamic data classification method according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram illustrating a dynamic data classification apparatus according to an embodiment of the present invention.
Icon: 1-an electronic device; 10-dynamic data classification means; 11-an acquisition module; 12-a matching module; 13-a data set determination module; 14-a steady state processing module; 20-a memory; 30-a processor; 40-a communication unit; 2-a client; 3-a data acquisition device; 4-network.
Detailed Description
The dynamic data classification method and device provided by the embodiment of the invention can be applied to the application environment shown in fig. 1. As shown in fig. 1, the electronic device 1, the client 2 and the data acquisition device 3 are located in a wireless network 4 or a wired network 4, and the electronic device 1 performs data interaction with the client 2 and the data acquisition device 3 through the wireless network 4 or the wired network 4.
The dynamic data classification method and device provided by the embodiment of the invention are applicable to the electronic equipment 1. The electronic device 1 may be, but is not limited to, a Host (Host), a smart camera, etc. The client 2 may be, but is not limited to, a smart phone, a Personal Computer (PC), a tablet computer, a Personal Digital Assistant (PDA), a Mobile Internet Device (MID), and the like. The data acquisition device 3 may be, but is not limited to, an image sensor, a temperature sensor, a vibration sensor, and the like.
In this embodiment, the client 2 may set relevant parameters of the dynamic data classification method and apparatus and send the parameters to the electronic device 1 through the network 4, the data acquisition apparatus 3 may be used to acquire data information and send the data information to the electronic device 1, and the electronic device 1 executes the dynamic data classification method and apparatus on the received data information according to the set relevant parameters and sends the relevant results to the client 2 for display; the client 2 may also control the start and stop of the dynamic data classification method and apparatus and send the dynamic data classification method and apparatus to the electronic device 1 through the network 4.
As shown in fig. 2, is a block schematic diagram of the electronic device 1 shown in fig. 1. The electronic device 1 comprises a memory 20, a processor 30 and a communication unit 40.
The memory 20, the processor 30 and the communication unit 40 are electrically connected to each other directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The processor 30 is for executing executable modules, such as computer programs, stored in the memory 20.
The Memory 20 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The Processor 30 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor 30 may be any conventional processor or the like.
Wherein the memory 20 is used for storing programs, such as dynamic data sorting devices. The dynamic data classification means includes at least one software functional module which can be stored in the memory 20 in the form of software or firmware (firmware) or is fixed in an Operating System (OS) of the electronic device 1. The processor 30, upon receiving the execution instruction, executes the program to implement the dynamic data classification method. The communication unit 40 is configured to establish a communication connection between the electronic device 1 and the client 2 through the network 4, and to transceive data through the network 4.
It will be appreciated that the configuration shown in fig. 2 is merely illustrative and that the electronic device 1 may also include more or fewer components than shown in fig. 2 or have a different configuration than shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Fig. 3 is a schematic flow chart of a dynamic data classification method according to an embodiment of the present invention, and it should be noted that the dynamic data classification method according to the present invention is not limited by the specific sequence shown in fig. 3 and described below. It should be understood that in other embodiments, the order of some steps in the dynamic data classification method according to the present invention may be interchanged according to actual needs, or some steps may be omitted or deleted. The specific flow shown in fig. 3 will be described in detail below. Referring to fig. 3, the present embodiment describes a processing flow of the electronic device 1, and the method includes:
step S1, data information is sequentially acquired.
In the present embodiment, the data information is acquired by the data acquisition device 3 and transmitted to the electronic device 1. The data information may be, but is not limited to, temperature information, brightness information, frequency information, image information, and the like.
In this embodiment, if the electronic device 1 does not acquire data information within a predetermined time, and if the number of data information acquired by the electronic device 1 within a previous time is not 1, one data information is deleted from the previously acquired data information, and the above steps are repeated until the number of data information acquired by the electronic device 1 is 1, and then the last data information is saved as an initial value. This allows a new round of data statistics to be started and compared with the data statistics of the previous round.
If the electronic apparatus 1 acquires the data information within the prescribed time, the electronic apparatus 1 will execute step S2 described below.
In step S2, the first and second criteria are matched according to the sequence of the data information.
In the present embodiment, as shown in fig. 4, step S2 includes the following sub-steps:
step S21, when the data information is the first data information, matching the first data information with a first judgment standard; the first judgment standard is to judge whether the difference value between the first data information and a preset initial value is within a first preset range.
It can be understood that, when the data information is the first data information received by the electronic device 1, the first data information and the preset initial value are subtracted to obtain a difference value between the first data information and the preset initial value, and the difference value between the first data information and the preset initial value is matched with the first preset range. The initial value can be preset by a worker, and can also be the last data information stored in the previous round of data statistics; the first preset range can be set by a worker according to actual conditions, or can be set in advance in the electronic device 1.
Step S22, when the data information is non-first data information, matching a second judgment criterion for the non-first data information; and the second judgment criterion is to judge whether the difference value between the non-first data information and the historical data reference value is within a second preset range and the current data volume in the data group is less than a preset value.
In this embodiment, the historical data reference value is determined from data information obtained from history according to a maximum inter-class variance method, a mean variance method, or a principal component analysis method.
It can be understood that the data information acquired in the history may be data information acquired before the currently acquired non-first data information by the electronic device 1, and when the next non-first data information is acquired, the last non-first data information is also updated to the data information acquired in the history.
The historical data reference value can be obtained by calculating the maximum inter-class variance method, the mean variance method or the principal component analysis method from data information acquired from history. The data information acquired in history comprises discarded data information and accepted data information, namely normal data information and abnormal data information. Of course, in this embodiment, other methods may be used to calculate the historical data reference value from the data information obtained from the history, which is not limited herein.
For example, if the historical data reference value is obtained by using a mean square error method, the specific working principle of the mean square error method is to calculate a standard deviation according to the data information obtained by the history, and if the data information obtained by the history is normally distributed, 2 times of the standard deviation or 3 times of the standard deviation can be used as the historical data reference value. If the historical data reference value is obtained by adopting a principal component analysis method, the specific working principle of the principal component analysis method is to obtain a data average value by calculating according to data information obtained by history, then obtain the difference value from each data information obtained by history to the data average value by taking the data average value as the center, and obtain the historical data reference value according to the difference value.
In this embodiment, the second preset range may be set by a worker according to an actual situation, or may be preset in the electronic device 1.
In step S3, data information meeting the first criterion or the second criterion is added to the data group.
In this embodiment, the data information may be understood as an element, and the data group may be understood as a set, where an element obtained each time meets the first criterion or the second criterion, the element belongs to the set, and where an element obtained does not meet the first criterion or the second criterion, the element does not belong to the set, that is, is outside the set. It can also be considered that the elements in the set have extremely high similarity and small difference, the elements outside the set have obvious difference with the elements inside the set, and whether the elements can be added into the set can be distinguished through the first judgment standard or the second judgment standard. It is also understood that elements within the set are normal elements (i.e., normal data information) and elements outside the set are abnormal elements (i.e., abnormal data information).
In the present embodiment, as shown in fig. 5, step S3 includes the following sub-steps:
step S31, if the difference between the first data information and the preset initial value is within a first preset range, adding the first data information into the data set.
It can be understood that, if the data information acquired by the electronic device 1 is the first data information, the first data information is matched according to the first judgment criterion, and if the first data information meets the first judgment criterion (that is, the difference between the first data information and the preset initial value is within the first preset range), the first data information is added to the data group (that is, the first data information is the normal data information).
Further, in this embodiment, if the difference between the first data information and the preset initial value is not within the first preset range, it indicates that the first data information is abnormal data information, and the staff may determine whether the first data information is reasonably abnormal according to the actual situation, and if the first data information is reasonably abnormal, the staff sends an instruction for receiving the first data information to the electronic device 1 through the client 2, and the electronic device 1 adds the first data information into the data group according to the instruction. If the first data message is not reasonably abnormal, the electronic device 1 discards the first data message, i.e. the first data message is not added to the data set.
Step S32, if the difference between the non-first data information and the historical data reference value is within a second preset range and the current data size in the data group is less than a preset value, adding the non-first data information into the data group.
It can be understood that, if the data information acquired by the electronic device 1 is non-first data information, the non-first data information is matched according to the second determination criterion, and if the non-first data information meets the second determination criterion (that is, the difference between the non-first data information and the reference value of the historical data is within the second preset range, and the current data size in the data group is less than the preset value), the non-first data information is added to the data group (that is, the non-first data information is normal data information).
Further, in this embodiment, if the difference between the non-first data information and the reference value of the historical data is within a second preset range and the current data amount in the data group is not less than a preset value, the non-first data information is added to the data group, and redundant data information is deleted from the data group according to a preset rule, so as to update the data group.
In this embodiment, the preset rule may be, but is not limited to, the earliest time for adding the data set, the largest difference with the historical data reference value, or randomly selected from the data set, and the specific preset rule may be set by the staff according to the actual requirement.
It can be understood that, when the data information acquired by the electronic device 1 is non-first data information, if the difference between the non-first data information and the reference value of the historical data is within the second preset range but the current data amount in the data group is not less than the preset value, the non-first data information is added into the data group, and the data information with the earliest time of adding the data group, the data information with the largest difference from the reference value of the historical data, or one data information randomly selected from the data group is determined as redundant data information, and the redundant data information is deleted from the data group to update the data group. Therefore, the data set has certain stability on the whole and can slowly change, so that the historical data reference value adapts to the difference change generated along with the change of the working condition, namely, the data set has followability.
The data set can only receive 10 pieces of data information, and if the difference between the 11 th data information acquired by the electronic device 1 and the historical data reference value is within a second preset range after the 10 pieces of data information are received in the data set, one piece of data information is selected from the received 10 pieces of data information in the data set and the 11 th data information acquired by the electronic device 1 according to a preset rule to be deleted, so that the data volume in the data set is kept at 10 all the time, and the data information in the data set is dynamically changed.
Further, in this embodiment, if the difference between the non-first data information and the reference value of the historical data is not within the second preset range, the non-first data information will not be added to the data set even if the current data amount in the data set is smaller than the preset value.
Referring to fig. 6, a detailed description will be given of an embodiment of screening for appearance defects of a product, in which before a product leaves a factory, the appearance of a package of the product is generally inspected to check whether an outline and a color of a graphic on the package of the product meet requirements, and the following steps are performed:
step S101, judging whether image information of a product is input; if yes, executing step S104; if no input is made, step S102 is executed.
Step S102, judging whether no image information is input within a specified time; if the image information is input within the predetermined time, executing step S101; if no image information is input for a predetermined time, step S103 is executed.
Step S103, judging whether the number of the image information acquired in the previous time is 1; if the number of the image information acquired in the previous time is 1, storing the image information as an initial value; if the number of pieces of image information acquired in the previous period is more than 1, one piece of image information is deleted from the image information acquired in the previous period, the operation is continued by returning to step S101, and if no image information is input subsequently or after a predetermined period of time has elapsed, the image information acquired in the previous period is continuously deleted until the number of pieces of image information acquired in the previous period is 1, and the last piece of data information is stored as an initial value.
Step S104, judging whether the image information is the first image information; if the image information is the first image information, step S105 is executed, and if the image information is not the first image information, step S107 is executed.
Step S105, judging whether the difference value between the first image information and a preset initial value is within a first preset range, and if so, adding the first image information into a data set; if not, go to step S106.
Step S106, determining whether the first data information is reasonably abnormal or not by a worker according to the actual situation; if the first image information is reasonably abnormal, adding the first data information into the data group; if the first image information is not reasonably abnormal, the first image information is discarded.
Step S107, judging whether the difference value between the non-first image information and the historical data reference value is within a second preset range; if the difference value between the non-first image information and the historical data reference value is not within a second preset range, discarding the non-first image information; if the difference between the non-first image information and the historical data reference value is within the second preset range, step S108 is executed.
Step S108, judging whether the current data volume in the data group is smaller than a preset value; if the current data volume in the data group is smaller than a preset value, adding the non-first image information into the data group; and if the current data volume in the data group is not less than the preset value, adding the non-first image information into the data group, and deleting the image information with the earliest time of adding the data group from the data group so as to update the data group.
The detailed operation steps can change the brightness value of the image information in the data set along with the change of time, and the product is prevented from being judged as a defective product by mistake due to the brightness change generated by the change of the image information along with the change of time.
As shown in fig. 7, a schematic structural diagram of a dynamic data classification device 10 according to an embodiment of the present invention is provided, where the dynamic data classification device 10 is applied to an electronic device 1, it should be noted that the basic principle and the generated technical effects of the dynamic data classification device 10 according to the embodiment are the same as those of the foregoing method embodiment, and for brief description, reference may be made to corresponding contents in the foregoing method embodiment for a part not mentioned in the embodiment. The dynamic data classification device 10 includes an obtaining module 11, a matching module 12, and a data set determining module 13.
The obtaining module 11 is configured to sequentially obtain data information.
It is understood that the obtaining module 11 may execute the step S1.
The matching module 12 is configured to match the first determination criterion and the second determination criterion according to the sequence of the data information.
In this embodiment, the matching module 12 is configured to match a first judgment criterion for a first data message when the data message is the first data message; the first judgment standard is to judge whether the difference value between the first data information and a preset initial value is within a first preset range.
The matching module 12 is configured to match a second criterion for the non-first data information when the data information is the non-first data information; and the second judgment criterion is to judge whether the difference value between the non-first data information and the historical data reference value is within a second preset range and the current data volume in the data group is less than a preset value.
It is understood that the matching module 12 may perform the above step S2.
The data group determining module 13 is configured to add data information meeting the first criterion or the second criterion to the data group.
It is to be understood that the data set determining module 13 may execute the above step S3.
Further, in this embodiment, the dynamic data classification apparatus 10 further includes a steady-state processing module 14, configured to add the non-first data information to the data group and delete redundant data information from the data group according to a preset rule to update the data group when a difference between the non-first data information and a reference value of historical data is within a second preset range and a current data amount in the data group is not less than a preset value.
In summary, in the dynamic data classification method and apparatus provided in the embodiments of the present invention, the data information is sequentially obtained, the first determination criterion and the second determination criterion are respectively matched according to the sequence of the data information, and finally, the data information meeting the first determination criterion or the second determination criterion is added to the data group. Therefore, the data information in the data group obtained by the method is dynamically changed and has follow-up property, so that the data group can eliminate the influence caused by the change of working conditions, and the accuracy of the judgment standard can be improved.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Claims (8)
1. A method for dynamic data classification, the method comprising:
sequentially acquiring data information;
respectively matching a first judgment standard and a second judgment standard according to the sequence of the data information, wherein the first judgment standard is used for judging whether the difference value between the first data information and a preset initial value is within a first preset range, the second judgment standard is used for judging whether the difference value between the non-first data information and a historical data reference value is within a second preset range, and the current data volume in the data group is less than a preset value;
adding data information meeting the first judgment standard or the second judgment standard into the data group;
and if the difference value between the non-first data information and the historical data reference value is within the second preset range and the current data volume in the data group is not less than the preset value, adding the non-first data information into the data group, and deleting redundant data information from the data group according to a preset rule so as to update the data group.
2. The dynamic data classification method of claim 1, wherein the step of matching the first criterion and the second criterion, respectively, according to the order of the data information comprises:
when the data information is the first data information, matching the first judgment standard for the first data information;
and if the difference value between the first data information and the preset initial value is within the first preset range, adding the data information meeting the first judgment standard into the data group.
3. The dynamic data classification method of claim 1, wherein the step of matching the first criterion and the second criterion, respectively, according to the order of the data information comprises:
when the data information is the non-first data information, matching the non-first data information with the second judgment standard; and if the difference value between the non-first data information and the historical data reference value is within the second preset range and the current data volume in the data group is less than the preset value, the step of adding the data information meeting the second judgment standard into the data group is executed.
4. The dynamic data classification method of claim 1, wherein the step of deleting redundant data information from the data set according to the predetermined rule comprises:
and judging the data information with the earliest time of adding the data group or the data information with the largest difference value with the historical data reference value as redundant data information, and deleting the redundant data information from the data group.
5. The dynamic data classification method of claim 3, characterized in that the historical data reference values are determined from historically acquired data information according to a maximum inter-class variance method, a mean variance method or a principal component analysis method.
6. An apparatus for dynamic data classification, the apparatus comprising:
the acquisition module is used for sequentially acquiring data information;
the matching module is used for respectively matching a first judgment standard and a second judgment standard according to the sequence of the data information, wherein the first judgment standard is used for judging whether the difference value between the first data information and a preset initial value is within a first preset range, the second judgment standard is used for judging whether the difference value between the non-first data information and a historical data reference value is within a second preset range, and the current data volume in the data group is less than a preset value;
a data group determining module, configured to add data information meeting the first criterion or the second criterion to the data group;
and the steady-state processing module is used for adding the non-first data information into the data group and deleting redundant data information from the data group according to a preset rule so as to update the data group when the difference value between the non-first data information and the historical data reference value is within the second preset range and the current data amount in the data group is not less than the preset value.
7. The dynamic data classification apparatus of claim 6, wherein the matching module is configured to match the first criterion for the first data message when the data message is the first data message; and if the difference value between the first data information and the preset initial value is within the first preset range, the data group determining module is used for adding the data information meeting the first judgment standard into the data group.
8. The dynamic data classification device of claim 6, wherein the matching module is configured to match the second criterion for the non-first data message when the data message is the non-first data message;
and if the difference value between the non-first data information and the historical data reference value is within the second preset range and the current data volume in the data group is smaller than the preset value, the data group determining module is used for adding the data information meeting the second judgment standard into the data group.
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| CN101325004A (en) * | 2008-08-01 | 2008-12-17 | 北京航空航天大学 | Method for compensating real time traffic information data |
| CN105072122A (en) * | 2015-08-19 | 2015-11-18 | 山东超越数控电子有限公司 | Rapid matching classification method for data packets |
| CN105759608A (en) * | 2016-02-26 | 2016-07-13 | 北京邮电大学 | Energy-saving method and system |
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| CN101325004A (en) * | 2008-08-01 | 2008-12-17 | 北京航空航天大学 | Method for compensating real time traffic information data |
| CN105072122A (en) * | 2015-08-19 | 2015-11-18 | 山东超越数控电子有限公司 | Rapid matching classification method for data packets |
| CN105759608A (en) * | 2016-02-26 | 2016-07-13 | 北京邮电大学 | Energy-saving method and system |
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