CN117131530B - Intelligent factory sensitive data encryption protection method - Google Patents
Intelligent factory sensitive data encryption protection method Download PDFInfo
- Publication number
- CN117131530B CN117131530B CN202311361731.4A CN202311361731A CN117131530B CN 117131530 B CN117131530 B CN 117131530B CN 202311361731 A CN202311361731 A CN 202311361731A CN 117131530 B CN117131530 B CN 117131530B
- Authority
- CN
- China
- Prior art keywords
- data
- sequence
- time
- sensitivity
- difference
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000004512 die casting Methods 0.000 claims abstract description 66
- 230000035945 sensitivity Effects 0.000 claims abstract description 36
- 238000004364 calculation method Methods 0.000 claims abstract description 17
- 230000008569 process Effects 0.000 claims abstract description 16
- 238000005266 casting Methods 0.000 claims description 47
- 230000011218 segmentation Effects 0.000 claims description 23
- 230000005856 abnormality Effects 0.000 claims description 19
- 238000002347 injection Methods 0.000 claims description 19
- 239000007924 injection Substances 0.000 claims description 19
- 230000007547 defect Effects 0.000 claims description 13
- 238000001816 cooling Methods 0.000 claims description 12
- 230000006835 compression Effects 0.000 claims description 6
- 238000007906 compression Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 5
- 238000004519 manufacturing process Methods 0.000 description 13
- 238000003825 pressing Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000001627 detrimental effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007306 functionalization reaction Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000007769 metal material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Bioethics (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Storage Device Security (AREA)
Abstract
The invention relates to the technical field of data processing, in particular to an intelligent factory sensitive data encryption protection method, which comprises the following steps: the method comprises the steps of acquiring pressure data in the operation process of die casting equipment by using a pressure sensor, obtaining a time sequence data sequence, dividing the time sequence data into single die casting sequence segments, obtaining a difference sequence, obtaining product inadequacy corresponding to the single die casting sequence segments according to the data quantity, the corresponding time of data and the data value in all target data sequence segments in the difference sequence, obtaining high-sensitivity data sequence segments, carrying out encryption processing on low-sensitivity data, and obtaining the data sensitivity degree of the high-sensitivity data sequence segments according to the product inadequacy corresponding to all the single die casting sequence segments, the quantity of the high-sensitivity data sequence segments and the equipment problem severity corresponding to bad products, thereby carrying out encryption processing on all the data in the high-sensitivity data sequence segments. The invention reduces the calculation cost and ensures the data safety by adaptively selecting the encryption algorithm and the key length.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent factory sensitive data encryption protection method.
Background
The intelligent factory is a modern manufacturing mode based on information technology and the Internet of things, and has wide application fields. In the intelligent factory applied to die casting, the method can improve the production efficiency and quality of die casting products through the advantages of automatic production, data driving decision, supply chain integration, flexible production, resource optimization, safety, reliability and the like. The protection of the operation data of the die casting equipment is one of the keys for ensuring the stability and reliability of the production environment, so that the sensitive data in the operation of the die casting equipment needs to be encrypted and protected, and only legal users with corresponding keys can decrypt and access the data in the data transmission, storage and processing processes, thereby effectively protecting the privacy of the data, reducing the risk of data leakage, preventing the data from being tampered in the transmission and storage processes and increasing the safety of the data. And encrypting sensitive data is one of the necessary measures required by industry standards and regulations.
The existing problems are as follows: along with the production operation of die casting equipment in an intelligent factory, a large amount of time sequence data is generated, and important high-sensitivity data and unimportant low-sensitivity data exist in the time sequence data, so that different data often need different encryption standards for encryption processing because the low-level encryption standard is simple and easy to use and low in calculation cost and has lower security, while the high-level encryption standard has higher security and higher calculation cost.
Disclosure of Invention
The invention provides an intelligent factory sensitive data encryption protection method for solving the existing problems.
The intelligent factory sensitive data encryption protection method adopts the following technical scheme:
one embodiment of the invention provides a smart factory sensitive data encryption protection method, which comprises the following steps:
acquiring pressure data in the operation process of the die casting equipment in real time by using a pressure sensor to obtain a time sequence data sequence; dividing single-shot casting sequence segments in the time sequence data sequence, and marking any single-shot casting sequence segment as a reference sequence segment; sequentially calculating absolute values of difference values of two adjacent data in the reference sequence section to obtain a difference sequence;
dividing data in the difference sequence into three types of data; in the difference sequence, a data sequence segment formed by continuously adjacent data in each type of data is recorded as a target data sequence segment; obtaining the product inadequacy corresponding to the reference sequence section according to the data quantity, the data corresponding time and the data value in all the target data sequence sections in the difference sequence;
obtaining a high-sensitivity data sequence segment in the time sequence data sequence according to product inadequacy corresponding to all single casting sequence segments in the time sequence data sequence, and encrypting all low-sensitivity data in the time sequence data sequence;
dividing the data quantity of each same data value by the data quantity in all the high-sensitivity data sequence segments in the time sequence data sequence, and recording the data quantity as the occurrence probability of each data value; obtaining the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence according to the product inadequacy, the quantity of the high-sensitivity data sequence segments and the occurrence probability and importance of all data values in all the high-sensitivity data sequence segments corresponding to all the single die casting sequence segments in the time sequence data sequence;
and according to the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence, encrypting all data in all the high-sensitivity data sequence segments in the time sequence data sequence.
Further, the dividing the single casting sequence segment in the time sequence data sequence comprises the following specific steps:
according to the operation log of the die casting equipment, obtaining the die casting starting time and the die casting ending time of each die casting product in the operation process of the die casting equipment;
and in the time sequence data, recording a time sequence data sequence section between the die casting starting time and the die casting ending time of each die casting product as a single die casting sequence section.
Further, the data in the difference sequence is divided into three types of data, and the specific steps are as follows:
obtaining an injection segmentation threshold value of a difference sequence by using an Ojin algorithm, and recording all data which are larger than the injection segmentation threshold value in the difference sequence as first-class data; all data which are smaller than or equal to the injection segmentation threshold value in the difference sequence are recorded as suspected class data;
obtaining cold pressing segmentation threshold values of suspected class data by using an Ojin algorithm, and recording all data which are larger than the cold pressing segmentation threshold values in the suspected class data as second class data; all data which are less than or equal to the cold pressing segmentation threshold value in the suspected data are recorded as third data; the first class data, the second class data and the third class data form a difference sequence.
Further, the method for obtaining the product inadequacy corresponding to the reference sequence segment according to the data quantity, the data corresponding time and the data value in all the target data sequence segments in the difference sequence comprises the following specific steps:
obtaining the normality of each target data sequence segment in each type of data in the difference sequence according to the corresponding time of all data in each target data sequence segment in each type of data in the difference sequence and the data quantity in each target data sequence segment in each type of data in the difference sequence;
obtaining the continuity of each type of data in the difference sequence according to the normality of all target data sequence segments in each type of data in the difference sequence and the data quantity in all target data sequence segments in each type of data in the difference sequence;
obtaining the data abnormality of each stage in the single casting process according to the continuity of all the class data in the difference sequence and the data quantity in all the class data in the difference sequence;
and obtaining the product inadequacy corresponding to the reference sequence section according to the data abnormality of each stage in the single casting process and all data values in the third type of data in the difference sequence.
Further, according to the time corresponding to all the data in each target data sequence segment in each type of data in the difference sequence and the data quantity in each target data sequence segment in each type of data in the difference sequence, a specific calculation formula corresponding to the normality of each target data sequence segment in each type of data in the difference sequence is obtained, wherein the specific calculation formula is as follows:
wherein the method comprises the steps ofFor the normality of the x-th target data sequence segment in the i-th class of data in the difference sequence,for the average value of all data corresponding to the moments in the x target data sequence segment in the ith data in the difference sequence,for the average value of all data corresponding time points in the ith data in the difference sequence, n is the data quantity in the reference sequence segment,is the data quantity in the x target data sequence segment in the i-th data in the difference sequence.
Further, according to the data abnormality of each stage in the single casting process and all the data values in the third class of data in the difference sequence, a specific calculation formula corresponding to the product unevenness corresponding to the reference sequence segment is obtained as follows:
wherein C is the product defect corresponding to the reference sequence segment, n is the data quantity in the reference sequence segment,for the amount of data in the i-th class of data in the difference sequence,for the amount of data in the x-th target data sequence segment in the i-th class of data in the difference sequence,for the normality of the x-th target data sequence segment in the i-th class of data in the difference sequence,for the number of target data sequence segments in the i-th class of data in the difference sequence,for the continuity of the i-th class of data in the difference sequence,for the mean of all data in the third class of data in the difference sequence,is the data abnormality of each stage in the single casting process, and I is an absolute value function.
Further, according to the product failure corresponding to all single casting sequence segments in the time sequence data sequence, obtaining a high sensitive data sequence segment in the time sequence data sequence, and encrypting all low sensitive data in the time sequence data sequence, comprising the following specific steps:
according to the product defects corresponding to all single casting sequence segments in the time sequence data sequence, forming a product defect set;
obtaining an unfavorable segmentation threshold value in a product unfavorable set by using an Ojin algorithm, and recording a single casting sequence segment corresponding to data larger than the unfavorable segmentation threshold value in the product unfavorable set as a high-sensitivity data sequence segment;
recording data which is not in the high sensitive data sequence section in the time sequence data sequence as low sensitive data;
and (3) encrypting all the low-sensitivity data in the sequence of the ordinal data by using a DES encryption algorithm of a low-level encryption standard to obtain ciphertext of the low-sensitivity data.
Further, the step of obtaining the data sensitivity of the high-sensitivity data sequence segment in the time sequence data sequence according to the product inadequacy, the number of the high-sensitivity data sequence segments corresponding to all single compression casting sequence segments in the time sequence data sequence, the occurrence probability and the importance of all data values in all the high-sensitivity data sequence segments comprises the following specific steps:
obtaining the product abnormality degree corresponding to the time sequence data according to the number of the high sensitive data sequence segments in the time sequence data sequence and the variance of the product abnormality corresponding to all the single casting sequence segments in the time sequence data sequence;
obtaining the equipment problem severity corresponding to the bad product according to the importance of all data values in all the high-sensitivity data sequence segments in the time sequence data sequence and the occurrence probability of all the data values in all the high-sensitivity data sequence segments in the time sequence data sequence;
and obtaining the data sensitivity degree of the high-sensitivity data sequence section in the time sequence data sequence according to the product abnormality degree corresponding to the time sequence data sequence and the equipment problem severity degree corresponding to the bad product.
Further, according to the anomaly degree of the product corresponding to the time sequence data sequence and the severity degree of the equipment problem corresponding to the bad product, a specific calculation formula corresponding to the data sensitivity degree of the high-sensitivity data sequence section in the time sequence data sequence is obtained as follows:
wherein Q is the data sensitivity degree of the high sensitive data sequence section in the time sequence data sequence, z is the variety number of different data values in all the high sensitive data sequence sections in the time sequence data sequence,for the probability of occurrence of the j-th data value in all highly sensitive data sequence segments in the time series data sequence,the j-th data value in all the high sensitive data sequence segments in the time sequence data is important, g is the number of the high sensitive data sequence segments in the time sequence data, m is the number of the single casting sequence segments in the time sequence data, V is the variance of product inadequacy corresponding to all the single casting sequence segments in the time sequence data,for the number of highly sensitive data sequence segments for which the j-th data value is present in the time-series data sequence,for the degree of product anomaly corresponding to the time series data sequence,for the severity of the equipment problem corresponding to the poor product,as an exponential function with a base of natural constant,is a linear normalization function.
Further, according to the data sensitivity degree of the high sensitive data sequence segment in the time sequence data sequence, encrypting all data in all the high sensitive data sequence segments in the time sequence data sequence, including the following specific steps:
if the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence is smaller than or equal to a preset first judgment threshold value, encrypting all data in all the high-sensitivity data sequence segments in the time sequence data sequence by using an AES encryption algorithm with a key length of 128 bits to obtain ciphertext of the high-sensitivity data;
if the data sensitivity degree of the high-sensitivity data sequence section in the time sequence data sequence is larger than a preset first judgment threshold value and smaller than a preset second judgment threshold value, encrypting all data in all the high-sensitivity data sequence section in the time sequence data sequence by using an AES encryption algorithm with the key length of 192 bits to obtain ciphertext of the high-sensitivity data;
and if the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence is greater than or equal to a preset second judgment threshold value, encrypting all data in all the high-sensitivity data sequence segments in the time sequence data sequence by using an AES encryption algorithm with a key length of 256 bits to obtain ciphertext of the high-sensitivity data.
The technical scheme of the invention has the beneficial effects that:
in the embodiment of the invention, the pressure sensor is used for collecting pressure data in the operation process of the die casting equipment in real time to obtain a time sequence data sequence, the time sequence data sequence is divided into single die casting sequence segments, and absolute values of difference values of two adjacent data in the single die casting sequence segments are calculated in sequence to obtain a difference sequence. Dividing data in a difference sequence into three types of data, marking a data sequence section formed by continuous adjacent data in each type of data as a target data sequence section, obtaining product inadequacies corresponding to single compression casting sequence sections according to the data quantity, the corresponding time and the data value in all the target data sequence sections in the difference sequence, obtaining high sensitive data sequence sections in a time sequence data sequence, carrying out encryption processing on all low sensitive data in the time sequence data sequence, and obtaining the data sensitivity degree of the high sensitive data sequence sections in the time sequence data sequence according to the product inadequacies, the quantity of the high sensitive data sequence sections and the equipment problem severity corresponding to poor products corresponding to all the single compression casting sequence sections in the time sequence data sequence, so as to carry out encryption processing on all data in all the high sensitive data sequence sections in the time sequence data sequence, thereby completing encryption processing of the time sequence data sequence. The method comprises the steps of carrying out encryption processing on normal data of a die-casting product by using a low-level encryption standard, reducing encryption calculation cost, carrying out encryption processing on data of a die-casting bad product which is a reaction equipment problem by using a high-level encryption standard, and obtaining the sensitivity degree of the data of the die-casting bad product through the equipment problem severity degree of the die-casting bad product reaction, so that the key length is selected in a self-adaptive mode, the sensitivity degree is larger, the key length is larger, and the safety of the data is improved.
Drawings
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 flowchart illustrating a smart factory sensitive data encryption protection method according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to specific implementation, structure, characteristics and effects of an intelligent factory sensitive data encryption protection method according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the intelligent factory sensitive data encryption protection method provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a smart factory sensitive data encryption protection method according to an embodiment of the invention is shown, and the method includes the following steps.
Step S001: acquiring pressure data in the operation process of the die casting equipment in real time by using a pressure sensor to obtain a time sequence data sequence; dividing single-shot casting sequence segments in the time sequence data sequence, and marking any single-shot casting sequence segment as a reference sequence segment; and sequentially calculating absolute values of difference values of two adjacent data in the reference sequence segment to obtain a difference sequence.
Device operational data is known to be critical data in intelligent plants that reveals the performance, health and reliability of the device, as well as information about critical device components that an attacker can use to discover vulnerabilities and problems with the device, thereby invading or destroying the plant system. And the equipment operation data contains information of unique production process, technical details, design improvement and the like of the company, and production plan and efficiency. Therefore, in this embodiment, the operation data of the die casting equipment in the intelligent factory is taken as an example, the high-sensitivity data and the low-sensitivity data in the operation data of the die casting equipment are distinguished, and the data with different sensitivity degrees are encrypted by using a proper encryption algorithm, so that the data security is ensured, and meanwhile, the calculation cost is reduced.
In general, pressure data is the most critical information in the die casting process, which affects product quality and production efficiency. Therefore, the pressure sensor is used for collecting pressure data in the operation process of the die casting equipment in real time to obtain the time sequence data sequence A.
According to the operation log of the die casting equipment, the die casting starting time and the die casting ending time of each die casting product in the operation process of the die casting equipment can be obtained. In the time sequence data A, the time sequence data sequence section between the die casting starting time and the die casting ending time of each die casting product is recorded as a single die casting sequence section. It should be noted that, in the die casting process, there is usually a certain standby time from one product to the next, but the data in the standby time is not important, so the data in the standby time is not analyzed in this embodiment.
Since setting up and setting up a die casting apparatus requires a certain time and resources and maintaining stability and consistency of the apparatus during production, continuous operations are generally arranged on a single die casting apparatus to produce the same type of die casting products in order to improve production efficiency and reduce costs. The data between the individual die-cast sequence segments should be similar.
And taking any single casting sequence segment in the time sequence data sequence A, and recording the single casting sequence segment as a reference sequence segment. Sequentially calculating absolute values of differences between two adjacent data in the reference sequence segment to obtain a difference sequence corresponding to the reference sequence segmentWhere n is the number of data in the reference sequence segment,is the absolute value of the difference between the n-1 th and n-th data in the reference sequence segment.
Step S002: dividing data in the difference sequence into three types of data; in the difference sequence, a data sequence segment formed by continuously adjacent data in each type of data is recorded as a target data sequence segment; and obtaining the product inadequacy corresponding to the reference sequence segment according to the data quantity, the data corresponding time and the data value in all the target data sequence segments in the difference sequence.
Since the die casting process of a single workpiece can be divided into: injection stage, pressure maintaining stage and cooling stage. Wherein, the injection stage: the injection machine applies high pressure to inject the metal material into the mold cavity, and the pressure can rapidly rise to reach a peak value; pressure maintaining stage: the pressure may drop slightly but remain relatively stable; and (3) a cooling stage: the pressure will slowly decrease until a lower steady state is reached.
The difference between adjacent pressure data in the injection phase is large, the difference between adjacent pressure data in the cooling phase is moderate, the difference between adjacent pressure data in the dwell phase is small, and is close to 0. Thus using the Ojin algorithm to obtain the difference sequenceIs divided into threshold values by injection, and the difference sequences are divided into two different sequencesAll data which are larger than the injection segmentation threshold value are recorded as first-class data; sequence of differencesAll data less than or equal to the injection segmentation threshold value are recorded as suspected class data; obtaining a difference sequence by using the Ojin algorithmCold-pressing segmentation threshold value of suspected class data, and differential sequenceAll data which are larger than the cold pressing segmentation threshold value in the suspected type data are recorded as second type data; sequence of differencesAll data which are less than or equal to the cold pressing segmentation threshold value in the suspected data are recorded as third data, and the difference sequence is recorded up toThe data in (a) is divided into three types of data, wherein the first type of data, the second type of data and the third type of data form a difference sequence B. It is noted that the difference sequenceThe three types of data divided by the data in the step (a) are sequentially data in the injection, cooling and pressure maintaining stages. The method of the Sedrin algorithm is a well-known technique, and the specific method is not described here.
In the difference sequenceAnd (3) recording a data sequence segment formed by continuously adjacent data in each type of data as a target data sequence segment. It should be noted that if one data in one class of data is not the same class as its neighboring data, the single data is also a target data sequence segment.
The data change is larger in the transition time periods among the injection stage, the pressure maintaining stage and the cooling stage, and the data change in the middle period of each stage is more stable. Therefore, the calculation formula of the product poor C corresponding to the reference sequence segment is shown as follows:
wherein C is the product defect corresponding to the reference sequence segment, n is the data quantity in the reference sequence segment,for a difference sequenceThe amount of data in the i-th class of data,for a difference sequenceThe number of data in the x-th target data sequence segment in the i-th class of data,for a difference sequenceThe normality of the x-th target data sequence segment in the i-th class of data,for a difference sequenceThe number of target data sequence segments in the i-th class of data,for a difference sequenceThe average value of all data corresponding to the moments in the x target data sequence segment in the i-th data,for a difference sequenceThe average value of the corresponding moments of all data in the ith class of data,for a difference sequenceThe average value of all data in the third class of data. I is an absolute function.
What needs to be described is: due to the differential sequenceThe data in the system is divided into three types of data, namely data of injection, cooling and pressure maintaining stages in sequence, soAnd each type of data should only correspond to one target data sequence segment, if one type of data corresponds to a plurality of target data sequence segments, and the number of data in the target data sequence segments is small, the greater the possibility that the type of data is abnormal is indicated, and when the number of data in the target data sequence segment is close to the number of data in the type where the target data sequence segment is located, the more normal the target data sequence segment is. Because there may be false division of data in the time period of transition of injection, cooling and pressure maintaining stages, when the corresponding time of the target data sequence segment is closer to the time of the class in which the target data sequence segment is located, the target data sequence segment is more reliable, soFor a difference sequenceThe time reliability of the (x) th target data sequence segment in the (i) th class data is calculated by usingAndrepresenting the difference sequenceNormalcy of the xth target data sequence segment in the ith class of data. And then to normalize the valueIs thatWeight, weighted average of (2)The continuity of the data is represented, and the smaller the value is, the greater the possibility of abnormality of the data is. The more the data quantity of each type of data is, the longer the time consumption of the data is in the single casting process, the more important the data is, so the normalized value is usedFor normalized inverse proportional valueThe larger the weight of the data abnormality of each stage in the single casting process is, the worse the quality of the casting product is. Since it is necessary that the pressure in the pressure maintaining stage is kept constant, when the pressure data in the die casting pressure maintaining stage is kept unchanged, the density uniformity, surface quality and dimensional stability of the product can be improved,the data mean value of the corresponding data in the pressure maintaining stage is shown, the larger the value is, the more the data is abnormal, and the quality of the die-casting product is poorer. For this purposeAndand indicates that the product corresponding to the reference sequence segment is not good.
Step S003: and obtaining a high-sensitivity data sequence segment in the time sequence data sequence according to the product inadequacy corresponding to all the single casting sequence segments in the time sequence data sequence, and encrypting all the low-sensitivity data in the time sequence data sequence.
In the above way, the product defects corresponding to each single casting sequence segment in the time sequence data sequence A are obtained, and thus a product defect set is obtainedWhere m is the number of single shot sequence segments in the time series data sequence A,is the product defect corresponding to the mth single casting sequence section in the time sequence data sequence A. And obtaining an unfavorable segmentation threshold value in the product unfavorable set by using an Ojin algorithm, and recording a single casting sequence segment corresponding to data greater than the unfavorable segmentation threshold value in the product unfavorable set as a high-sensitivity data sequence segment. The high-sensitivity data sequence section corresponds to the die casting data of the poor die casting product.
It is known that when a poor die cast product is produced on a die cast device, the data at the time of production of the poor die cast product reveals the performance, health and reliability of the device, which has a vulnerability, problem of the device, and an attacker can find out the security vulnerability of the device by analyzing these data. And when a large number of poor die casting products are present, which may affect production planning and efficiency, leakage of such data can be detrimental to enterprise competitiveness and even provide commercial advantages to competitors.
Therefore, the data in the high sensitive data sequence section in the time sequence data sequence A needs to be protected with emphasis, and the data in the non-high sensitive data sequence section in the time sequence data sequence A contains less important information, so the data which is not in the high sensitive data sequence section in the time sequence data sequence A is marked as low sensitive data. Therefore, the embodiment uses the DES encryption algorithm of the low-level encryption standard to encrypt all the low-sensitivity data in the sequence A of the ordinal data to obtain the ciphertext of the low-sensitivity data, which can well reduce the calculation cost.
Step S004: dividing the data quantity of each same data value by the data quantity in all the high-sensitivity data sequence segments in the time sequence data sequence, and recording the data quantity as the occurrence probability of each data value; and obtaining the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence according to the product inadequacy, the number of the high-sensitivity data sequence segments and the occurrence probability and the importance of all data values in all the high-sensitivity data sequence segments corresponding to all the single die casting sequence segments in the time sequence data sequence.
And for the data in all the high-sensitivity data sequence segments in the time sequence data sequence A, the sensitivity degree of the data needs to be further analyzed, and when the sensitivity degree is higher, encryption protection with higher safety is carried out, so that the safety of the high-sensitivity data is ensured.
The number of categories of different data values in all the highly sensitive data sequence segments in the statistical time sequence A is z. In all the highly sensitive data sequence segments in the time series data sequence a, the number of data of each identical data value divided by the number of data in all the highly sensitive data sequence segments is recorded as the occurrence probability of each data value.
The calculation formula of the data sensitivity degree Q of the high-sensitivity data sequence segment in the time sequence data sequence A can be known:
wherein Q is the data sensitivity degree of the high sensitive data sequence section in the time sequence data sequence A, z is the variety number of different data values in all the high sensitive data sequence sections in the time sequence data sequence A,for the probability of occurrence of the j-th data value in all the highly sensitive data sequence segments in the time-series data sequence a,for the importance of the j-th data value in all the highly sensitive data sequence segments in the time sequence data A, g is the number of highly sensitive data sequence segments in the time sequence data sequence A, m is the number of single casting sequence segments in the time sequence data sequence A, V is the variance of product defects corresponding to all the single casting sequence segments in the time sequence data sequence,for the number of highly sensitive data sequence segments for which the j-th data value is present in the time-series data sequence a,the present embodiment uses an exponential function based on natural constantsTo present inverse proportion relation and normalization processing, and the implementer can set inverse proportion function and normalization function according to actual situation.Normalizing the data values to [0,1 ] as a linear normalization function]Within the interval.
It should be noted that the larger the variance V, the larger the difference between the die-cast product imperfections, the more important the highly sensitive data, but the larger V may be due to the larger imperfections of a small number of die-cast products, so that the V needs to be corrected according to the number of bad die-cast products, thereby normalizing the valuesAnd the product of the V represents the abnormality degree of the product corresponding to the time sequence data sequence A, and the larger the value is, the more important the data is, and the more important protection is needed. The defective products caused by further analysis are defects of the same defects caused by the same problem of the equipmentThe more kinds of problems occur in the equipment, the more important the data is, and the more important the important protection is. When the bad product is caused by the same equipment problem, the same problem data in all the high-sensitivity data sequence sections in the real-time sequence data sequence A can be used for multiple times of data, and the problem data in all the high-sensitivity data sequence sections in the real-time sequence data sequence A should be different when the bad product is caused by different equipment problems. Thus (2)The smaller andthe smaller the size, the more important the data value, thereby normalizing the value with inverse proportionRepresenting the importance of such data values, thereby normalizing the valuesIs thatWith the inverse of the weighted average thereofThe greater the value of the severity of the equipment problem corresponding to the bad product, the more important the data, and the more important the important protection is needed. For this purposeAndand the normalized value of the product of (a) represents the data sensitivity of the highly sensitive data sequence segment in the time series data sequence a.
Step S005: and according to the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence, encrypting all data in all the high-sensitivity data sequence segments in the time sequence data sequence.
In this embodiment, the AES encryption algorithm of the advanced encryption standard is used to encrypt the highly sensitive data, and known AES encryption algorithms support key lengths of 128 bits, 192 bits and 256 bits, and increasing the key length can increase the computational complexity required by an attacker to crack the ciphertext, that is, enhance the security of the data, but increase the computational cost.
The first judgment threshold value set in the embodimentA second judgment threshold valueIn the description of this example, other values may be set in other embodiments, and the present example is not limited thereto. If the data sensitivity degree Q of the high-sensitivity data sequence segment in the time sequence data sequence A is smaller than or equal to the first judgment threshold valueWhen the encryption method is used, an AES encryption algorithm with the key length of 128 bits is used for encrypting all data in all high-sensitivity data sequence segments in the time sequence data sequence A; if the data sensitivity degree Q of the high-sensitivity data sequence segment in the time sequence data sequence A is greater than the first judgment threshold valueAnd is smaller than the second judgment threshold valueWhen the encryption method is used, an AES encryption algorithm with the key length of 192 bits is used for encrypting all data in all high-sensitivity data sequence segments in the time sequence data sequence A; if the data sensitivity degree Q of the high-sensitivity data sequence section in the time sequence data sequence A is more than or equal to the second judgment threshold valueWhen the encryption processing is carried out on all data in all high sensitive data sequence segments in the time sequence data sequence A by using an AES encryption algorithm with a key length of 256 bits, therebyAnd obtaining the ciphertext of the high-sensitivity data.
Thus, the encryption processing of the time sequence data sequence A is completed, and the encryption protection of the intelligent factory sensitive data is completed.
The present invention has been completed.
In summary, in the embodiment of the present invention, the pressure sensor is used to collect the pressure data in the operation process of the die casting device in real time, so as to obtain the time sequence data sequence, and divide the time sequence data sequence into single die casting sequence segments, and sequentially calculate the absolute value of the difference value between two adjacent data in the single die casting sequence segments, so as to obtain the difference sequence. Dividing data in a difference sequence into three types of data, marking a data sequence section formed by continuous adjacent data in each type of data as a target data sequence section, obtaining product inadequacies corresponding to single compression casting sequence sections according to the data quantity, the corresponding time and the data value in all the target data sequence sections in the difference sequence, obtaining high sensitive data sequence sections in a time sequence data sequence, carrying out encryption processing on all low sensitive data in the time sequence data sequence, and obtaining the data sensitivity degree of the high sensitive data sequence sections in the time sequence data sequence according to the product inadequacies, the quantity of the high sensitive data sequence sections and the equipment problem severity corresponding to poor products corresponding to all the single compression casting sequence sections in the time sequence data sequence, so as to carry out encryption processing on all data in all the high sensitive data sequence sections in the time sequence data sequence, thereby completing encryption processing of the time sequence data sequence. According to the invention, the time sequence data is classified, different encryption algorithms and self-adaptive key lengths are used for different data, so that the calculation cost is reduced, and the safety of the data is ensured.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (2)
1. An intelligent factory sensitive data encryption protection method is characterized by comprising the following steps:
acquiring pressure data in the operation process of the die casting equipment in real time by using a pressure sensor to obtain a time sequence data sequence; dividing single-shot casting sequence segments in the time sequence data sequence, and marking any single-shot casting sequence segment as a reference sequence segment; sequentially calculating absolute values of difference values of two adjacent data in the reference sequence section to obtain a difference sequence;
dividing data in the difference sequence into three types of data; in the difference sequence, a data sequence segment formed by continuously adjacent data in each type of data is recorded as a target data sequence segment; obtaining the product inadequacy corresponding to the reference sequence section according to the data quantity, the data corresponding time and the data value in all the target data sequence sections in the difference sequence;
obtaining a high-sensitivity data sequence segment in the time sequence data sequence according to product inadequacy corresponding to all single casting sequence segments in the time sequence data sequence, and encrypting all low-sensitivity data in the time sequence data sequence;
dividing the data quantity of each same data value by the data quantity in all the high-sensitivity data sequence segments in the time sequence data sequence, and recording the data quantity as the occurrence probability of each data value; obtaining the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence according to the product inadequacy, the quantity of the high-sensitivity data sequence segments and the occurrence probability and importance of all data values in all the high-sensitivity data sequence segments corresponding to all the single die casting sequence segments in the time sequence data sequence;
encrypting all data in all the high-sensitivity data sequence segments in the time sequence data sequence according to the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence;
the three types of data are sequentially data in the injection, cooling and pressure maintaining stages;
the data in the difference sequence is divided into three types of data, and the specific steps are as follows:
obtaining an injection segmentation threshold value of a difference sequence by using an Ojin algorithm, and recording all data which are larger than the injection segmentation threshold value in the difference sequence as first-class data; all data which are smaller than or equal to the injection segmentation threshold value in the difference sequence are recorded as suspected class data;
obtaining a cooling segmentation threshold value of suspected class data by using an Ojin algorithm, and marking all data which are larger than the cooling segmentation threshold value in the suspected class data as second class data; all data which are less than or equal to the cooling segmentation threshold value in the suspected class data are recorded as third class data; the first class data, the second class data and the third class data form a difference sequence; the first type of data is data in an injection stage, the second type of data is data in a cooling stage, and the third type of data is data in a pressure maintaining stage;
according to the data quantity, the data corresponding time and the data value in all the target data sequence segments in the difference sequence, the product inadequacy corresponding to the reference sequence segment is obtained, and the method comprises the following specific steps:
obtaining the normality of each target data sequence segment in each type of data in the difference sequence according to the corresponding time of all data in each target data sequence segment in each type of data in the difference sequence and the data quantity in each target data sequence segment in each type of data in the difference sequence;
obtaining the continuity of each type of data in the difference sequence according to the normality of all target data sequence segments in each type of data in the difference sequence and the data quantity in all target data sequence segments in each type of data in the difference sequence;
obtaining the data abnormality of each stage in the single casting process according to the continuity of all the class data in the difference sequence and the data quantity in all the class data in the difference sequence;
obtaining product inadequacy corresponding to the reference sequence section according to data abnormality of each stage in the single casting process and all data values in the third class of data in the difference sequence;
the specific calculation formula corresponding to the normality of each target data sequence segment in each type of data in the difference sequence is obtained according to the corresponding time of all data in each target data sequence segment in each type of data in the difference sequence and the data quantity in each target data sequence segment in each type of data in the difference sequence, wherein the specific calculation formula is as follows:
；
wherein the method comprises the steps ofFor the normality of the x target data sequence segment in the i-th class of data in the difference sequence,/for the x target data sequence segment in the i-th class of data in the difference sequence>For the mean value of all data corresponding to the moment in the x target data sequence section in the ith data in the difference sequence,/for the data>For the mean value of all data corresponding time points in the ith data in the difference sequence, n is the data quantity in the reference sequence section,/for the data in the ith data in the difference sequence>The data quantity in the x target data sequence section in the i-th data in the difference sequence is obtained;
the specific calculation formula corresponding to the product inadequacy corresponding to the reference sequence section is obtained according to the data abnormality of each stage in the single casting process and all the data values in the third class data in the difference sequence:
；
wherein C is the product defect corresponding to the reference sequence segment, n is the data quantity in the reference sequence segment,for the number of data in the i-th class of data in the difference sequence,/for the number of data in the i-th class of data in the difference sequence>For the number of data in the x target data sequence segment in the i-th data in the difference sequence,/th data sequence segment>For the normality of the x target data sequence segment in the i-th class of data in the difference sequence,/for the x target data sequence segment in the i-th class of data in the difference sequence>For the number of target data sequence segments in the i-th class of data in the difference sequence,/for the number of target data sequence segments in the i-th class of data in the difference sequence>For the continuity of the i-th class of data in the difference sequence, -/-, is provided>For the mean value of all data in the third class of data in the difference sequence, +.>For data abnormality at each stage in the single casting process, +.>As a function of absolute value;
obtaining a high-sensitivity data sequence segment in the time sequence data sequence according to product inadequacy corresponding to all single casting sequence segments in the time sequence data sequence, and encrypting all low-sensitivity data in the time sequence data, wherein the method comprises the following specific steps of:
according to the product defects corresponding to all single casting sequence segments in the time sequence data sequence, forming a product defect set;
obtaining an unfavorable segmentation threshold value in a product unfavorable set by using an Ojin algorithm, and recording a single casting sequence segment corresponding to data larger than the unfavorable segmentation threshold value in the product unfavorable set as a high-sensitivity data sequence segment;
recording data which is not in the high sensitive data sequence section in the time sequence data sequence as low sensitive data;
encrypting all low sensitive data in the sequence of the ordinal data by using a DES encryption algorithm of a low-level encryption standard to obtain ciphertext of the low sensitive data;
the method for obtaining the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence according to the product inadequacy, the quantity of the high-sensitivity data sequence segments corresponding to all single-time compression casting sequence segments in the time sequence data sequence, the occurrence probability and the importance of all data values in all the high-sensitivity data sequence segments comprises the following specific steps:
obtaining the product abnormality degree corresponding to the time sequence data according to the number of the high sensitive data sequence segments in the time sequence data sequence and the variance of the product abnormality corresponding to all the single casting sequence segments in the time sequence data sequence;
obtaining the equipment problem severity corresponding to the bad product according to the importance of all data values in all the high-sensitivity data sequence segments in the time sequence data sequence and the occurrence probability of all the data values in all the high-sensitivity data sequence segments in the time sequence data sequence;
obtaining the data sensitivity degree of the high-sensitivity data sequence section in the time sequence data sequence according to the product abnormality degree corresponding to the time sequence data sequence and the equipment problem severity degree corresponding to the bad product;
the specific calculation formula corresponding to the data sensitivity degree of the high-sensitivity data sequence section in the time sequence data sequence is obtained according to the product abnormality degree corresponding to the time sequence data sequence and the equipment problem severity degree corresponding to the bad product:
；
wherein Q is the data sensitivity degree of the high sensitive data sequence section in the time sequence data sequence, z is the variety number of different data values in all the high sensitive data sequence sections in the time sequence data sequence,for the j-th data value in all the highly sensitive data sequence segments in the time sequence data sequenceProbability of occurrence of->The j-th data value in all the high-sensitivity data sequence segments in the time sequence data is of importance, g is the number of the high-sensitivity data sequence segments in the time sequence data, m is the number of the single casting sequence segments in the time sequence data, V is the variance of product failure corresponding to all the single casting sequence segments in the time sequence data, and the j-th data value is of importance>For the number of highly sensitive data sequence segments in the time-series data sequence for which the j-th data value is present,/->Product abnormality degree corresponding to time sequence data, < ->Severity of equipment problem for bad products, +.>As an exponential function based on natural constants, < +.>Is a linear normalization function;
the encrypting process is carried out on all data in all the high sensitive data sequence segments in the time sequence data sequence according to the data sensitivity degree of the high sensitive data sequence segments in the time sequence data sequence, and the method comprises the following specific steps:
if the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence is smaller than or equal to a preset first judgment threshold value, encrypting all data in all the high-sensitivity data sequence segments in the time sequence data sequence by using an AES encryption algorithm with a key length of 128 bits to obtain ciphertext of the high-sensitivity data;
if the data sensitivity degree of the high-sensitivity data sequence section in the time sequence data sequence is larger than a preset first judgment threshold value and smaller than a preset second judgment threshold value, encrypting all data in all the high-sensitivity data sequence section in the time sequence data sequence by using an AES encryption algorithm with the key length of 192 bits to obtain ciphertext of the high-sensitivity data;
and if the data sensitivity degree of the high-sensitivity data sequence segments in the time sequence data sequence is greater than or equal to a preset second judgment threshold value, encrypting all data in all the high-sensitivity data sequence segments in the time sequence data sequence by using an AES encryption algorithm with a key length of 256 bits to obtain ciphertext of the high-sensitivity data.
2. The smart factory sensitive data encryption protection method according to claim 1, wherein the dividing the single casting sequence segment in the time sequence data sequence comprises the following specific steps:
according to the operation log of the die casting equipment, obtaining the die casting starting time and the die casting ending time of each die casting product in the operation process of the die casting equipment;
and in the time sequence data, recording a time sequence data sequence section between the die casting starting time and the die casting ending time of each die casting product as a single die casting sequence section.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311361731.4A CN117131530B (en) | 2023-10-20 | 2023-10-20 | Intelligent factory sensitive data encryption protection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311361731.4A CN117131530B (en) | 2023-10-20 | 2023-10-20 | Intelligent factory sensitive data encryption protection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117131530A CN117131530A (en) | 2023-11-28 |
| CN117131530B true CN117131530B (en) | 2024-01-30 |
Family
ID=88863053
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311361731.4A Active CN117131530B (en) | 2023-10-20 | 2023-10-20 | Intelligent factory sensitive data encryption protection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117131530B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117874556B (en) * | 2024-03-12 | 2024-06-04 | 国网河南省电力公司经济技术研究院 | Block chain-based power big data secure sharing method |
| CN117975742B (en) * | 2024-03-29 | 2024-06-18 | 大连禾圣科技有限公司 | Smart city traffic management system and method based on big data |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102982619A (en) * | 2011-09-02 | 2013-03-20 | 北京国网盛源智能终端科技有限公司 | Wall mounting type fee paying terminal platform |
| EP2757736A1 (en) * | 2013-01-19 | 2014-07-23 | Fabulonia OÜ | Secure streaming method in a numerically controlled manufacturing system, and a secure numerically controlled manufacturing system |
| CN107292183A (en) * | 2017-06-29 | 2017-10-24 | 国信优易数据有限公司 | A kind of data processing method and equipment |
| KR20220061360A (en) * | 2020-11-06 | 2022-05-13 | 한국생산기술연구원 | Die-casting product defect detection method and system based on deep learning anomaly detection |
| CN115510302A (en) * | 2022-11-16 | 2022-12-23 | 西北工业大学 | Intelligent factory data classification method based on big data statistics |
| CN115618397A (en) * | 2022-12-19 | 2023-01-17 | 深圳市研强物联技术有限公司 | Voice encryption method for recording pen |
| CN116168349A (en) * | 2023-04-25 | 2023-05-26 | 江苏中科云控智能工业装备有限公司 | Big data-based die-casting visual monitoring system and method |
| WO2023142813A1 (en) * | 2022-01-26 | 2023-08-03 | 中国第一汽车股份有限公司 | Data fusion method and apparatus based on multi-sensor, device, and medium |
| CN116566585A (en) * | 2023-07-10 | 2023-08-08 | 中国科学院东北地理与农业生态研究所 | Informationized intelligent storage method |
| CN116720211A (en) * | 2023-08-07 | 2023-09-08 | 知心健(南京)科技有限公司 | Dynamic monitoring data security encryption method for heart rehabilitation |
| CN116756493A (en) * | 2023-08-15 | 2023-09-15 | 湖南湘江智慧科技股份有限公司 | Data management method for security and fire control finger collecting platform |
| CN116821833A (en) * | 2023-08-29 | 2023-09-29 | 好林(威海)新材料有限公司 | Data anomaly detection method for machine filter cloth adhesion |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100446336B1 (en) * | 2003-05-20 | 2004-09-01 | 엔에이치엔(주) | Method and Device of Data Encryption |
| US9842215B2 (en) * | 2015-11-03 | 2017-12-12 | Palo Alto Research Center Incorporated | Computer-implemented system and method for anonymizing encrypted data |
| US11356248B2 (en) * | 2017-12-21 | 2022-06-07 | Telefonaktiebolaget Lm Ericsson (Publ) | Stream ciphering |
-
2023
- 2023-10-20 CN CN202311361731.4A patent/CN117131530B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102982619A (en) * | 2011-09-02 | 2013-03-20 | 北京国网盛源智能终端科技有限公司 | Wall mounting type fee paying terminal platform |
| EP2757736A1 (en) * | 2013-01-19 | 2014-07-23 | Fabulonia OÜ | Secure streaming method in a numerically controlled manufacturing system, and a secure numerically controlled manufacturing system |
| CN107292183A (en) * | 2017-06-29 | 2017-10-24 | 国信优易数据有限公司 | A kind of data processing method and equipment |
| KR20220061360A (en) * | 2020-11-06 | 2022-05-13 | 한국생산기술연구원 | Die-casting product defect detection method and system based on deep learning anomaly detection |
| WO2023142813A1 (en) * | 2022-01-26 | 2023-08-03 | 中国第一汽车股份有限公司 | Data fusion method and apparatus based on multi-sensor, device, and medium |
| CN115510302A (en) * | 2022-11-16 | 2022-12-23 | 西北工业大学 | Intelligent factory data classification method based on big data statistics |
| CN115618397A (en) * | 2022-12-19 | 2023-01-17 | 深圳市研强物联技术有限公司 | Voice encryption method for recording pen |
| CN116168349A (en) * | 2023-04-25 | 2023-05-26 | 江苏中科云控智能工业装备有限公司 | Big data-based die-casting visual monitoring system and method |
| CN116566585A (en) * | 2023-07-10 | 2023-08-08 | 中国科学院东北地理与农业生态研究所 | Informationized intelligent storage method |
| CN116720211A (en) * | 2023-08-07 | 2023-09-08 | 知心健(南京)科技有限公司 | Dynamic monitoring data security encryption method for heart rehabilitation |
| CN116756493A (en) * | 2023-08-15 | 2023-09-15 | 湖南湘江智慧科技股份有限公司 | Data management method for security and fire control finger collecting platform |
| CN116821833A (en) * | 2023-08-29 | 2023-09-29 | 好林(威海)新材料有限公司 | Data anomaly detection method for machine filter cloth adhesion |
Non-Patent Citations (1)
| Title |
|---|
| 铝合金缸体压铸工艺及品质改进;石海;;特种铸造及有色合金(第12期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117131530A (en) | 2023-11-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117131530B (en) | Intelligent factory sensitive data encryption protection method | |
| Zhang et al. | Fault detection strategy based on weighted distance of $ k $ nearest neighbors for semiconductor manufacturing processes | |
| CN113064932B (en) | Network situation assessment method based on data mining | |
| Shu et al. | Autocorrelated process monitoring using triggered cuscore charts | |
| CN107886009B (en) | Big data generation method and system for preventing privacy disclosure | |
| CN107808100B (en) | Steganalysis method for specific test sample | |
| Yue | An integrated anomaly detection method for load forecasting data under cyberattacks | |
| CN110363033A (en) | A kind of chip security appraisal procedure and device | |
| CN117828649B (en) | Micro data center system | |
| CN115348080B (en) | Comprehensive analysis system and method for vulnerability of network equipment based on big data | |
| Chen et al. | Cumulative conformance count chart with variable sampling intervals and control limits | |
| Nieves Avendano et al. | Anomaly detection and event mining in cold forming manufacturing processes | |
| Zhang et al. | Privacy-preserving and sensor-fused framework for prognostic & health management in leased manufacturing system | |
| CN116996628B (en) | Network data transmission protection method | |
| CN117692257A (en) | High-speed encryption method and device for service data of electric power Internet of things | |
| CN113282876B (en) | Method, device and equipment for generating one-dimensional time sequence data in anomaly detection | |
| CN112926079B (en) | Crypto algorithm timing analysis evaluation method based on clear and ciphertext correlation analysis | |
| CN117459322B (en) | Computer software data encryption method based on Internet of things | |
| CN117909989B (en) | Data security acquisition method and system based on big data | |
| CN118573479B (en) | Data security exchange system based on business passwords | |
| CN110569654B (en) | Block chain credible data processing method and device for supply chain quick response mode | |
| Schreyer et al. | Data Analysis of Production Data for Continuous Casting of Aluminum Rolling Ingots | |
| CN117114506B (en) | Intelligent factory quality detection and analysis method | |
| CN111260818B (en) | Mold state diagnosis method for fastener forming machine | |
| US10277392B2 (en) | Cracking devices and methods thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |