CN117061170B - Intelligent manufacturing industry big data analysis method based on feature selection - Google Patents
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Abstract
The invention discloses an intelligent manufacturing industry big data analysis method based on feature selection, which relates to the technical field of data analysis and comprises the following steps: performing network attack monitoring on the cloud storage module, and calculating to obtain threat indexes of the cloud storage module; determining a data acquisition period of the cloud storage module according to the threat index; responding to the data acquisition instruction, acquiring industrial manufacturing data stored by the cloud storage module in a corresponding data acquisition period and marking the industrial manufacturing data as target data; by setting a plurality of data acquisition periods, acquiring and analyzing the data stored in the data acquisition periods, and improving the data processing efficiency; extracting characteristic information of target data and potential operation related information of corresponding intelligent equipment for fusion analysis, classifying the target data according to an operation management value GR, and if the target data is core data, distributing the core data to an advanced operation terminal for analysis; if the data is the common data, the data is distributed to the intermediate-level operation terminal, the operation resources are reasonably distributed, and the data analysis efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an intelligent manufacturing industry big data analysis method based on feature selection.
Background
The industrial Internet is a result of integration of a global industrial system with advanced computing, analyzing and sensing technologies and Internet connection, and is essentially characterized in that equipment, production lines, factories, suppliers, products and clients are tightly connected and integrated through an open and global industrial network platform, and various element resources in the industrial economy are efficiently shared, so that the cost is reduced, the efficiency is increased and the manufacturing industry is helped to prolong an industrial chain and promote the transformation development of the manufacturing industry through an automatic and intelligent production mode.
Because of cost or history reasons, a large number of non-digital production equipment exists in general industrial enterprises at present, cannot be integrated with an enterprise information system, becomes an island of enterprise production and processing information, and has poor accuracy and timeliness because the equipment state and the processing information can only be reported in a manual mode; the existing industrial big data analysis system has the problems that production data cannot be classified and analysis terminals with different operation grades are distributed for analysis, so that the data analysis efficiency is low.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides an intelligent manufacturing industry big data analysis method based on feature selection.
To achieve the above object, an embodiment according to a first aspect of the present invention provides an intelligent manufacturing industry big data analysis method based on feature selection, including the steps of:
step one: collecting various data during operation of the intelligent equipment, and marking the collected various data as industrial manufacturing data; transmitting the industrial manufacturing data to a cloud storage module in the form of a message;
Step two: performing network attack monitoring on the cloud storage module, and calculating to obtain threat indexes Ws of the cloud storage module; determining a data acquisition period of the cloud storage module according to the threat index Ws; the method specifically comprises the following steps: the database stores a mapping relation table of threat index ranges and data acquisition periods;
step three: responding to the data acquisition instruction, acquiring industrial manufacturing data stored by the cloud storage module in a corresponding data acquisition period and marking the industrial manufacturing data as target data;
Step four: extracting characteristic information of target data and potential operation related information of industrial manufacturing data generated during operation of corresponding intelligent equipment, performing fusion analysis, calculating a management value GR of the target data, and classifying the target data according to the management value GR, wherein the method specifically comprises the following steps:
Comparing the pipe transporting value GR with a set value; if the transportation value GR is greater than the set value, marking the target data as core data; otherwise, marking the target data as common data;
If the core data is the core data, distributing the core data to an advanced operation terminal for analysis; if the data is the common data, the common data is distributed to a medium-level operation terminal for analysis.
Further, in the second step, network attack monitoring is performed on the cloud storage module, which specifically includes:
When the cloud storage module is monitored to suffer from network attack, starting timing; stopping timing when no network attack is detected again; counting the time period between starting timing and stopping timing as attack duration time period; marking the duration of the attack duration period as an attack duration period CT;
Counting the number of network attacks P1 in the duration time of the attack, and counting the number of types of the network attacks L1; network attacks include virus attacks, email attacks, IP attacks, and redundant data attacks; calculating an attack value Gz by using a formula gz=CT×g1+P1×g2+L1×g3, wherein g1, g2 and g3 are all preset coefficient factors;
The attack value Gz is subjected to grade judgment to obtain an evaluation signal, which specifically comprises the following steps: comparing the attack value Gz with a preset communication threshold value; the preset communication threshold comprises X1 and X2; and X2 is less than X1;
When Gz is more than or equal to X1, the evaluation signal is a high-risk signal; when X2 is less than or equal to Gz and less than X1, the evaluation signal is a medium risk signal; when Gz is less than X2, the evaluation signal is a light danger signal;
In a preset time period, counting the total times of the evaluation signals to be P2; counting the duty ratio of each of the high-risk signal, the medium-risk signal and the light-risk signal compared with the number of evaluation signals and marking the duty ratio as Zb1, zb2 and Zb3 in sequence; and calculating to obtain threat index Ws of the cloud storage module by using a formula Ws= ƒ ×P2× (Zb1×3+Zb2×2+Zb3), wherein ƒ is a preset compensation factor.
Further, determining a data acquisition period of the cloud storage module according to the threat index Ws; the method comprises the following steps: determining a threat index range interval in which the threat index Ws is positioned in the corresponding mapping relation table; acquiring a corresponding data acquisition period Zi according to the threat index range interval; issuing a data acquisition instruction according to the data acquisition period Zi; the map table is preset by an administrator.
Further, in the fourth step, feature information of the target data and potential operation related information of the industrial manufacturing data generated during operation of the corresponding intelligent equipment are extracted for fusion analysis, and the specific steps are as follows:
collecting characteristic information of target data; the characteristic information comprises data capacity, a storage time stamp of data and a data type; marking the data capacity of the target data as Lz;
calculating according to the storage time stamp of the target data to obtain a storage time length Ct; acquiring the data type of the target data, and marking the corresponding type value as LX;
Acquiring intelligent equipment corresponding to target data, and acquiring potential operation related information of industrial manufacturing data generated during operation of the intelligent equipment; the potential operation association information comprises a data type, a data volume, a data transmission distance and a data transmission bandwidth;
evaluating an operation optimization index Yz of the intelligent equipment according to the potential operation association information; and calculating a pipe transporting value GR of the target data by using a formula GR= (Lz×b1+Ct×b2+LX×b3) ×Yz, wherein b1, b2 and b3 are all preset coefficient factors.
Further, the specific evaluation method of the operational optimization index Yz is as follows:
counting the total operation times of the intelligent equipment as C1 in a preset time period;
The data volume, the data transmission distance and the data transmission bandwidth in each piece of potential operation related information are marked as WLi, WDi and WKi in sequence; acquiring the data type of the potential operation association information, and marking the corresponding type value as Wxi;
Calculating to obtain an operation value YSi required by the intelligent device by using a formula YSi= (WLi×a1+ WDi × a2+Wxi×a3)/(WKi ×a4); wherein a1, a2, a3 and a4 are all preset coefficient factors;
Comparing the operation value YSi with a preset operation threshold value; counting the number of times that YSi is larger than a preset operation threshold as Lb1, and when YSi is larger than the preset operation threshold, obtaining the difference between YSi and the preset operation threshold and summing to obtain an supercomputed total value CZ; and calculating the operation optimization index Yz of the intelligent device by using a formula Yz=C1× (Lb1×a5+CZ×a6), wherein a5 and a6 are preset coefficient factors.
Compared with the prior art, the invention has the beneficial effects that:
The method comprises the steps of collecting various data during operation of intelligent equipment, and marking the collected various data as industrial manufacturing data; transmitting the industrial manufacturing data to a cloud storage module in the form of a message; performing network attack monitoring on the cloud storage module, and calculating to obtain threat indexes Ws of the cloud storage module; determining a data acquisition period of the cloud storage module according to the threat index Ws and issuing a corresponding data acquisition instruction; responding to the data acquisition instruction, acquiring industrial manufacturing data stored by the cloud storage module in a corresponding data acquisition period and marking the industrial manufacturing data as target data; according to the invention, a plurality of data acquisition periods are set, and data stored in the data acquisition periods are acquired and analyzed, so that the data processing efficiency is improved;
According to the invention, characteristic information of target data and potential operation related information of industrial manufacturing data generated during operation of corresponding intelligent equipment are extracted, fusion analysis is carried out, and a management value GR of the target data is obtained through calculation; if the transportation value GR is greater than the set value, marking the target data as core data; otherwise, marking the target data as common data; if the core data is the core data, distributing the core data to an advanced operation terminal for analysis; if the data is the common data, the common data is distributed to a medium-level operation terminal for analysis, operation resources are reasonably distributed, and the data analysis efficiency is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic block diagram of an intelligent manufacturing industry big data analysis method based on feature selection according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, an intelligent manufacturing industry big data analysis method based on feature selection includes the following steps:
step one: collecting various data during operation of the intelligent equipment, and marking the collected various data as industrial manufacturing data; transmitting the industrial manufacturing data to a cloud storage module in the form of a message;
step two: performing network attack monitoring on the cloud storage module, and calculating to obtain threat indexes Ws of the cloud storage module; determining a data acquisition period of the cloud storage module according to the threat index Ws and issuing a corresponding data acquisition instruction; the method comprises the following specific steps:
When the cloud storage module is monitored to suffer from network attack, starting timing; stopping timing when no network attack is detected again;
Counting the time period between starting timing and stopping timing as attack duration time period; marking the duration of the attack duration period as an attack duration period CT;
counting the number of network attacks in the duration time of the attack, wherein the network attacks comprise virus attacks, email attacks, IP attacks, redundant data attacks and the like, and the number of the network attacks is P1;
counting the number of types of network attacks as L1; calculating an attack value Gz by using a formula gz=CT×g1+P1×g2+L1×g3, wherein g1, g2 and g3 are all preset coefficient factors;
The attack value Gz is subjected to grade judgment to obtain an evaluation signal, which specifically comprises the following steps: comparing the attack value Gz with a preset communication threshold value; the preset communication threshold comprises X1 and X2; and X2 is less than X1;
When Gz is more than or equal to X1, the evaluation signal is a high-risk signal; when X2 is less than or equal to Gz and less than X1, the evaluation signal is a medium risk signal; when Gz is less than X2, the evaluation signal is a light danger signal;
In a preset time period, counting the total times of the evaluation signals to be P2; counting the duty ratio of each of the high-risk signal, the medium-risk signal and the light-risk signal compared with the number of evaluation signals and marking the duty ratio as Zb1, zb2 and Zb3 in sequence;
calculating to obtain threat index Ws of the cloud storage module by using a formula Ws= ƒ ×P2× (Zb1×3+Zb2×2+Zb3), wherein ƒ is a preset compensation factor;
Determining a data acquisition period of the cloud storage module according to the threat index Ws; the method comprises the following steps:
The database stores a mapping relation table of threat index ranges and data acquisition periods; determining a threat index range interval in which the threat index Ws is positioned in the corresponding mapping relation table;
Acquiring a corresponding data acquisition period Zi according to the threat index range interval; issuing a data acquisition instruction according to the data acquisition period Zi; the greater the threat index Ws is, the shorter the corresponding data acquisition period is; the mapping relation table is preset by an administrator; according to the invention, a plurality of data acquisition periods are set, and data stored in the data acquisition periods are acquired and analyzed, so that the data processing efficiency is improved;
step three: responding to the data acquisition instruction, acquiring industrial manufacturing data stored by the cloud storage module in a corresponding data acquisition period and marking the industrial manufacturing data as target data;
step four: extracting characteristic information of target data and classifying; if the core data is the core data, distributing the core data to an advanced operation terminal for analysis; if the data is the common data, the common data is distributed to a medium-level operation terminal for analysis, operation resources are reasonably distributed, and the data analysis efficiency is improved;
In this embodiment, feature information of the target data is extracted and classified, specifically:
collecting characteristic information of target data; the characteristic information comprises data capacity, a storage time stamp of the data, a data type and the like; marking the data capacity of the target data as Lz;
calculating according to the storage time stamp of the target data to obtain a storage time length Ct; acquiring the data type of the target data, and marking the corresponding type value as LX;
Acquiring intelligent equipment corresponding to target data, and acquiring potential operation related information of industrial manufacturing data generated during operation of the intelligent equipment; the potential operation association information comprises data types, data amounts, data transmission distances and data transmission bandwidths;
Counting the total operation times of the intelligent equipment as C1 in a preset time period; the data volume, the data transmission distance and the data transmission bandwidth in each piece of potential operation related information are marked as WLi, WDi and WKi in sequence; acquiring the data type of the potential operation association information, and marking the corresponding type value as Wxi;
calculating to obtain an operation value YSi required by the intelligent device by using a formula YSi= (WLi×a1+ WDi × a2+Wxi×a3)/(WKi ×a4); wherein a1, a2, a3 and a4 are all preset coefficient factors;
Comparing the operation value YSi with a preset operation threshold value; counting the number of times that YSi is larger than a preset operation threshold as Lb1, and when YSi is larger than the preset operation threshold, obtaining the difference between YSi and the preset operation threshold and summing to obtain an supercomputed total value CZ; calculating to obtain an operation optimization index Yz of the intelligent device by using a formula Yz=C1× (Lb1×a5+CZ×a6), wherein a5 and a6 are preset coefficient factors;
Calculating a pipe transporting value GR of the target data by using a formula GR= (Lz×b1+Ct×b2+LX×b3) ×Yz, wherein b1, b2 and b3 are all preset coefficient factors;
Comparing the pipe transporting value GR with a set value; if the transport pipe value GR is greater than the set value, marking the target data as core data; otherwise, the target data is marked as normal data.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
The intelligent manufacturing industrial big data analysis method based on feature selection is characterized in that various data are collected during operation of intelligent equipment and marked as industrial manufacturing data; transmitting the industrial manufacturing data to a cloud storage module in the form of a message; performing network attack monitoring on the cloud storage module, and calculating to obtain threat indexes Ws of the cloud storage module; determining a data acquisition period of the cloud storage module according to the threat index Ws and issuing a corresponding data acquisition instruction; responding to the data acquisition instruction, acquiring industrial manufacturing data stored by the cloud storage module in a corresponding data acquisition period and marking the industrial manufacturing data as target data; according to the invention, a plurality of data acquisition periods are set, and data stored in the data acquisition periods are acquired and analyzed, so that the data processing efficiency is improved;
extracting characteristic information of target data and potential operation related information of industrial manufacturing data generated during operation of corresponding intelligent equipment, performing fusion analysis, and calculating to obtain a management value GR of the target data; if the transport pipe value GR is greater than the set value, marking the target data as core data; otherwise, marking the target data as common data; if the core data is the core data, distributing the core data to an advanced operation terminal for analysis; if the data is the common data, the common data is distributed to the intermediate-level operation terminal for analysis, the operation resources are reasonably distributed, and the data analysis efficiency is improved.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (1)
1. The intelligent manufacturing industry big data analysis method based on feature selection is characterized by comprising the following steps:
step one: collecting various data during operation of the intelligent equipment, and marking the collected various data as industrial manufacturing data; transmitting the industrial manufacturing data to a cloud storage module in the form of a message;
step two: performing network attack monitoring on the cloud storage module, and calculating to obtain threat indexes Ws of the cloud storage module; the method specifically comprises the following steps:
When the cloud storage module is monitored to suffer from network attack, starting timing; stopping timing when no network attack is detected again; counting the time period between starting timing and stopping timing as attack duration time period; marking the duration of the attack duration period as an attack duration period CT;
counting the number of network attacks P1 in the duration time of the attack, and counting the number of types of the network attacks L1; network attacks include virus attacks, email attacks, IP attacks, and redundant data attacks;
calculating an attack value Gz by using a formula gz=CT×g1+P1×g2+L1×g3, wherein g1, g2 and g3 are all preset coefficient factors;
The attack value Gz is subjected to grade judgment to obtain an evaluation signal, which specifically comprises the following steps: comparing the attack value Gz with a preset communication threshold value; the preset communication threshold comprises X1 and X2; and X2 is less than X1;
When Gz is more than or equal to X1, the evaluation signal is a high-risk signal;
When X2 is less than or equal to Gz and less than X1, the evaluation signal is a medium risk signal;
When Gz is less than X2, the evaluation signal is a light danger signal;
In a preset time period, counting the total times of the evaluation signals to be P2; counting the duty ratio of each of the high-risk signal, the medium-risk signal and the light-risk signal compared with the number of evaluation signals and marking the duty ratio as Zb1, zb2 and Zb3 in sequence; calculating to obtain threat index Ws of the cloud storage module by using a formula Ws= ƒ ×P2× (Zb1×3+Zb2×2+Zb3), wherein ƒ is a preset compensation factor;
Determining a data acquisition period of the cloud storage module according to the threat index Ws; the method specifically comprises the following steps: the database stores a mapping relation table of threat index ranges and data acquisition periods;
determining a threat index range interval in which the threat index Ws is positioned in the corresponding mapping relation table;
acquiring a corresponding data acquisition period Zi according to the threat index range interval; issuing a data acquisition instruction according to the data acquisition period Zi; the mapping relation table is preset by an administrator;
step three: responding to the data acquisition instruction, acquiring industrial manufacturing data stored by the cloud storage module in a corresponding data acquisition period and marking the industrial manufacturing data as target data;
Step four: extracting characteristic information of target data and potential operation related information of industrial manufacturing data generated during operation of corresponding intelligent equipment, performing fusion analysis, calculating a management value GR of the target data, and classifying the target data according to the management value GR, wherein the method specifically comprises the following steps:
collecting characteristic information of target data; the characteristic information comprises data capacity, a storage time stamp of data and a data type; marking the data capacity of the target data as Lz;
calculating according to the storage time stamp of the target data to obtain a storage time length Ct; acquiring the data type of the target data, and marking the corresponding type value as LX;
Acquiring intelligent equipment corresponding to target data, and acquiring potential operation related information of industrial manufacturing data generated during operation of the intelligent equipment; the potential operation association information comprises a data type, a data volume, a data transmission distance and a data transmission bandwidth;
Evaluating an operation optimization index Yz of the intelligent equipment according to the potential operation association information; the specific evaluation steps are as follows:
counting the total operation times of the intelligent equipment as C1 in a preset time period;
The data volume, the data transmission distance and the data transmission bandwidth in each piece of potential operation related information are marked as WLi, WDi and WKi in sequence; acquiring the data type of the potential operation association information, and marking the corresponding type value as Wxi;
Calculating to obtain an operation value YSi required by the intelligent device by using a formula YSi= (WLi×a1+ WDi × a2+Wxi×a3)/(WKi ×a4); wherein a1, a2, a3 and a4 are all preset coefficient factors;
Comparing the operation value YSi with a preset operation threshold value; counting the number of times that YSi is larger than a preset operation threshold as Lb1, and when YSi is larger than the preset operation threshold, obtaining the difference between YSi and the preset operation threshold and summing to obtain an supercomputed total value CZ; calculating an operation optimization index Yz of the intelligent equipment by using a formula Yz=C1× (Lb1×a5+CZ×a6), wherein a5 and a6 are preset coefficient factors;
Calculating a pipe transporting value GR of the target data by using a formula GR= (Lz×b1+Ct×b2+LX×b3) ×Yz, wherein b1, b2 and b3 are all preset coefficient factors;
Comparing the pipe transporting value GR with a set value; if the transportation value GR is greater than the set value, marking the target data as core data; otherwise, marking the target data as common data;
If the core data is the core data, distributing the core data to an advanced operation terminal for analysis; if the data is the common data, the common data is distributed to a medium-level operation terminal for analysis.
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| CN111092946A (en) * | 2019-12-18 | 2020-05-01 | 博依特(广州)工业互联网有限公司 | Data processing method and system applied to edge computing gateway |
| CN113596404A (en) * | 2021-07-29 | 2021-11-02 | 中大检测(湖南)股份有限公司 | Multi-communication mode multifunctional monitoring method and equipment |
| CN113946294A (en) * | 2021-10-29 | 2022-01-18 | 蜂巢科技(南通)有限公司 | Distributed storage system and data processing method thereof |
| CN114710353A (en) * | 2022-04-11 | 2022-07-05 | 万申科技股份有限公司 | Risk management and control system based on AIoT intelligent edge gateway |
| CN116389130A (en) * | 2023-04-11 | 2023-07-04 | 江苏电子信息职业学院 | Large-scale network security defense system based on knowledge graph |
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