CN115215023B - File access path intelligent security analysis system and method based on big data - Google Patents
File access path intelligent security analysis system and method based on big data Download PDFInfo
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- CN115215023B CN115215023B CN202210545346.4A CN202210545346A CN115215023B CN 115215023 B CN115215023 B CN 115215023B CN 202210545346 A CN202210545346 A CN 202210545346A CN 115215023 B CN115215023 B CN 115215023B
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- 238000004458 analytical method Methods 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000005540 biological transmission Effects 0.000 claims abstract description 131
- 230000007246 mechanism Effects 0.000 claims abstract description 105
- 238000012360 testing method Methods 0.000 claims abstract description 79
- 230000001050 lubricating effect Effects 0.000 claims description 22
- 239000010687 lubricating oil Substances 0.000 claims description 22
- 239000003921 oil Substances 0.000 claims description 14
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47B—TABLES; DESKS; OFFICE FURNITURE; CABINETS; DRAWERS; GENERAL DETAILS OF FURNITURE
- A47B63/00—Cabinets, racks or shelf units, specially adapted for storing books, documents, forms, or the like
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
- G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
- G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
- G06K7/1404—Methods for optical code recognition
- G06K7/1408—Methods for optical code recognition the method being specifically adapted for the type of code
- G06K7/1417—2D bar codes
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Abstract
The invention discloses a folder access path intelligent security analysis system and method based on big data, and belongs to the technical field of big data analysis. The system comprises a folder intelligent storage module, a testing module, a fault analysis module and a control module; the intelligent storage module of the file folder is used for realizing intelligent storage of the file folder of a user, and a file folder storage bin loaded with the file folder of the user is driven to a file taking opening at the front panel of the file cabinet by a file taking two-dimensional code through a driving mechanism; the test module is used for acquiring historical test data and building a transmission mechanism fault prediction model; the fault analysis module is used for predicting the fault probability of the transmission mechanism; the control module is used for calculating the security degree value of the access path of the file folder and preventing the transmission mechanism from being failed. The invention can analyze the fault degree of the file cabinet transmission mechanism and reduce the risks of blocking and downtime in the operation process.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to an intelligent security analysis system and method for a folder access path based on big data.
Background
In the prior art, an intelligent filing cabinet is mainly used for exchange office work such as enterprise files, documents and receipts, a user places the files in the cabinet and reminds a worker to take the files, after the management worker completes the files, the manager returns the files to the cabinet again, the cabinet can send information to the user, the user is reminded that the files are completed, and the files can be taken through verification codes. The operation mode can effectively adjust the office time of users and office staff, and is more flexible and convenient. The office filing cabinet occupies little space, and the bearing capacity is big and single cell volume rate is high, can be safe convenient deposit and deposit important office file, archives, financial document and staff's personal article, effectively reduces enterprise management cost expenditure.
However, in the actual use process, the long-term repeated access process can lead to the abrasion of the transmission mechanism in the file cabinet, the phenomenon of blocking easily occurs, and further the file cabinet is down and cannot be serviced, if the access of important files is involved, the use experience and time of a user are seriously affected.
Disclosure of Invention
The invention aims to provide a folder access path intelligent security analysis system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
the intelligent security analysis method for the folder access path based on the big data comprises the following steps:
s1, a user stores a folder into a file cabinet, generates a piece taking two-dimensional code and sends the two-dimensional code to a user port;
s2, a user initiates a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and a file storage bin carrying the user file is transmitted to a file taking port at the front panel of the file cabinet through a transmission mechanism;
s3, acquiring historical test data, building a transmission mechanism fault prediction model, acquiring the weight of each layer of folder storage bin in real time, acquiring the data stored and fetched in a file cabinet by a user, and predicting the transmission mechanism fault probability;
s4, setting a transmission mechanism fault probability threshold value, obtaining predicted transmission mechanism fault probability, calculating a folder access path safety degree value, and controlling a lubricating device according to the folder access path safety degree value to conduct advanced treatment on transmission mechanism faults.
According to the technical scheme, the historical test data comprise the monitored operation times of the transmission mechanism, and each layer of the folder storage bin is recorded as one operation time of the transmission mechanism in the transmission process; and the weight of the folder storage bin set in each transmission;
the historical test data also includes transmission failure data.
In the running process of the file cabinet, the weight of the file cabinet is different due to different quantity of carried file folders, and the power systems required by different bearing are also different; simultaneously, the folder storage bins for taking the parts by the user are different each time, so that the transmission times of the transmission mechanism are different, analysis and consideration are carried out on the two aspects in the application, the accurate degree of the result is ensured to be high, and the operation efficiency is effectively improved.
According to the technical scheme, the transmission mechanism fault prediction model comprises:
carrying out normalization processing and outlier filtering on the historical test data;
KMeans is essentially a data partitioning method based on euclidean distance metric, and the dimension with large mean and variance will have a decisive influence on the clustering result of data. It is important to normalize and unify the data (specifically, the features of each dimension) before clustering. In addition, outliers have a large impact on the mean calculation, resulting in center shifts, and these noise points are also filtered in advance;
selecting N test data from the processed historical test data, and marking the N test data as { x } 1 ，x 2 ，…，x N Each test data has m attribute characteristics, wherein the attribute characteristics refer to the running times of the transmission mechanism and the weight of a folder storage bin arranged in each transmission;
randomly selecting K centroids from N sample data to serve as initial clustering centers, and marking as:
{C 1 ，C 2 ，…，C K }，1<K≤N
defining an optimization target:
wherein L (α, β) represents an optimization function; x is x i Representing the ith test data;
α i represents x i A cluster to which the cluster belongs; beta αi Representing cluster alpha i A corresponding center point; m represents the total number of test data;
setting the iteration times t=0, 1,2, … …;
s3-1, calculating Euclidean distance from each test data to each cluster center, distributing the test data to the cluster center with the smallest Euclidean distance, and generating S clusters:
wherein,represents x at the t th iteration i A cluster to which the cluster belongs; />Representing the center point corresponding to the cluster S under the t-th iteration; s is S h Represents any one of the generated S clusters;
s3-2, recalculating the cluster center for each cluster center of the generated S clusters;
wherein F is t+1 ， h Cluster S representing the t-th iteration h New cluster centers generated in the t+1st iteration; s h I represents cluster S h The number of test data;
s3-3, repeating the steps S3-1 and S3-2 until L (alpha, beta) converges, i.e. all clusters are not changed any more;
s3-4, acquiring the number of test data with faults of the transmission mechanism in all the test data in the clusters, and recording the number as h 0 Calculating the failure rate:
wherein G is r Representing the failure rate of the cluster r generated after convergence of L (alpha, beta); h is a r Representing the number of test data for cluster r generated after convergence of L (α, β).
According to the technical scheme, the transmission mechanism is connected with a lubricating device;
an oil storage tank, a pressurizing nozzle and a controller are arranged in the lubricating device;
the oil storage tank is used for storing lubricating oil; the pressurizing nozzle is used for setting a pressurizing value, spraying out lubricating oil in the oil storage tank and enabling the lubricating oil to cover the transmission mechanism; the controller is used for controlling the on-off of the pressurized spray head;
acquiring the weight of each layer of folder storage bin in real time, acquiring the data stored and acquired by a user in a file cabinet, and acquiring a classification cluster of a current file cabinet transmission mechanism according to a transmission mechanism fault prediction model;
according to the classification clusters, obtaining failure rate as predicted failure probability of the transmission mechanism;
setting a fault probability threshold G of a transmission mechanism min When the predicted failure probability of the transmission mechanism is larger than the failure probability threshold G of the transmission mechanism min When the controller opens the pressurizing nozzle, the safety degree value T of the file access path is calculated 0 ：
T 0 ＝G r -G min
According to the file access path safety degree value, outputting a pressurizing numerical value of the pressurizing nozzle, and transmitting the pressurizing numerical value to the lubricating device:
u 0 ＝T 0 *w 1
wherein u is 0 A pressurization value representing the pressurization spray head; w (w) 1 Representing the impact coefficient value;
after the pressurizing nozzle obtains the pressurizing value, the lubricating oil in the oil storage tank is sprayed onto the transmission mechanism, and after the lubricating oil is sprayed, the controller closes the pressurizing nozzle.
When the predicted transmission failure probability is lower than the transmission failure probability threshold G min When the controller does not change.
The intelligent safety analysis system for the folder access path based on the big data comprises a folder intelligent storage module, a test module, a fault analysis module and a control module;
the intelligent storage module of the file folder is used for storing the file folder into a file cabinet by a user, generating a two-dimension code for taking a file, and sending the two-dimension code to a user port; the user initiates a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and a file storage bin carrying the file of the user is transmitted to a file taking port at the front panel of the file cabinet through a transmission mechanism; the test module is used for acquiring historical test data and building a transmission mechanism fault prediction model; the fault analysis module is used for acquiring the weight of each layer of folder storage bin in real time, acquiring the data stored and fetched by a user in the file cabinet, and predicting the fault probability of the transmission mechanism; the control module is used for calculating a folder access path safety degree value, controlling the lubricating device to spray lubricating oil according to the folder access path safety degree value, and preventing the transmission mechanism from failure;
the output end of the test module is connected with the input end of the fault analysis module; the output end of the fault analysis module is connected with an administrator computer system; the administrator computer system is connected with the input end of the control module; the output end of the control module is connected with the lubricating device;
the intelligent storage module of the folder is connected with an administrator computer system.
According to the technical scheme, the intelligent storage module for the folder comprises: the file cabinet, the transmission mechanism, the folder storage bin, the front panel of the file cabinet and the data transmission sub-module;
the folder storage bin is used for storing folders; the transmission mechanism is used for driving the folder storage bin to transmit; the front panel of the file cabinet is used for a user to take a piece; the data transmission submodule is used for storing the file folder into the file cabinet by a user, generating a piece taking two-dimensional code and sending the piece taking two-dimensional code to the user port; the user initiates a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and a file storage bin carrying the file of the user is transmitted to a file taking port at the front panel of the file cabinet through a transmission mechanism;
the data transmission sub-module is connected with the transmission mechanism.
According to the technical scheme, the test module comprises a test data acquisition sub-module and a model construction sub-module;
the test data acquisition sub-module is used for acquiring historical test data; the model construction submodule is used for constructing a transmission mechanism fault prediction model;
the output end of the test data acquisition sub-module is connected with the input end of the model construction sub-module; the output end of the model construction submodule is connected with the input end of the fault analysis module.
According to the technical scheme, the fault analysis module comprises a real-time data acquisition sub-module and a fault probability analysis sub-module;
the real-time data acquisition sub-module is used for acquiring the weight of each layer of folder storage bin in real time and acquiring the data stored and acquired by a user in the file cabinet; the fault probability analysis submodule is used for predicting the fault probability of the transmission mechanism according to the transmission mechanism fault prediction model;
the output end of the real-time data acquisition sub-module is connected with the input end of the fault probability analysis sub-module; and the output end of the fault probability analysis sub-module is connected with the administrator computer system.
According to the technical scheme, the control module comprises a folder access path safety degree calculation sub-module and an intelligent control sub-module;
the folder access path safety degree computing sub-module is used for computing a folder access path safety degree value; the intelligent control submodule is used for controlling the lubricating device to spray lubricating oil according to the folder access path safety degree value so as to prevent the transmission mechanism from malfunctioning.
Compared with the prior art, the invention has the following beneficial effects:
1. the intelligent file cabinet access process has the advantages that files are stored by taking the two-dimension codes, the operation is simple, consumables are not needed, a special person is not required to watch, daily use and management cost is saved, safety and rapidness are realized, equipment investment cost is low, and enterprise handling efficiency is improved;
2. the invention can analyze the fault degree of the file cabinet transmission mechanism, predicts the fault rate of the transmission mechanism through the processing of test data, remedies in advance, and reduces the risks of blocking and downtime in the operation process;
3. the invention is also provided with a folder access path safety degree value, and outputs the pressurizing value of the pressurizing nozzle according to the folder access path safety degree value, so that the pressure of the pressurizing nozzle can be controlled, the oil outlet quantity is further controlled, and the drip pollution and the resource loss caused by excessive injection of lubricating oil can be prevented.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a file cabinet of the intelligent security analysis system and method for a folder access path based on big data of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but 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.
Referring to fig. 1, in the present embodiment:
an intelligent file cabinet is arranged;
storing the file folder into an intelligent file cabinet by a user, generating a piece taking two-dimensional code, and sending the two-dimensional code to a user port;
the user can initiate a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and the file storage bin carrying the user file is transmitted to a file taking port at the front panel of the file cabinet through the transmission mechanism;
acquiring historical test data and building a transmission mechanism fault prediction model;
the historical test data comprise the monitored running times of the transmission mechanism, and each layer of the folder storage bin is replaced to be recorded as one running time of the transmission mechanism in the transmission process; and the weight of the folder storage bin set in each transmission;
the historical test data also includes transmission failure data.
The transmission mechanism fault prediction model comprises:
carrying out normalization processing and outlier filtering on the historical test data;
selecting N test data from the processed historical test data, and marking the N test data as { x } 1 ，x 2 ，…，x N Each test data has m attribute characteristics, wherein the attribute characteristics refer to the running times of the transmission mechanism and the weight of a folder storage bin arranged in each transmission;
randomly selecting K centroids from N sample data to serve as initial clustering centers, and marking as:
{C 1 ，C 2 ，…，C K }，1<K≤N
defining an optimization target:
wherein L (α, β) represents an optimization function; x is x i Representing the ith test data;
α i represents x i A cluster to which the cluster belongs; beta αi Representing cluster alpha i A corresponding center point; m represents the total number of test data;
setting the iteration times t=0, 1,2, … …;
s3-1, calculating Euclidean distance from each test data to each cluster center, distributing the test data to the cluster center with the smallest Euclidean distance, and generating S clusters:
wherein,represents x at the t th iteration i A cluster to which the cluster belongs; />Representing the center point corresponding to the cluster S under the t-th iteration; s is S h Represents any one of the generated S clusters;
s3-2, recalculating the cluster center for each cluster center of the generated S clusters;
wherein F is t+1 ， h Cluster S representing the t-th iteration h New cluster centers generated in the t+1st iteration; s h I represents cluster S h The number of test data;
s3-3, repeating the steps S3-1 and S3-2 until L (alpha, beta) converges, i.e. all clusters are not changed any more;
s3-4, acquiring the number of test data with faults of the transmission mechanism in all the test data in the clusters, and recording the number as h 0 Calculating the failure rate:
wherein G is r Representing the failure rate of the cluster r generated after convergence of L (alpha, beta); h is a r Representing the number of test data for cluster r generated after convergence of L (α, β).
The transmission mechanism is connected with a lubricating device; the lubricating device is a common oil spraying device and is connected with the controller;
an oil storage tank, a pressurizing nozzle and a controller are arranged in the lubricating device;
the oil storage tank is used for storing lubricating oil; the pressurizing nozzle is used for setting a pressurizing value, spraying out lubricating oil in the oil storage tank and enabling the lubricating oil to cover the transmission mechanism; the controller is used for controlling the on-off of the pressurized spray head;
acquiring the weight of each layer of folder storage bin in real time, acquiring the data stored and acquired by a user in a file cabinet, and acquiring a classification cluster of a current file cabinet transmission mechanism according to a transmission mechanism fault prediction model;
according to the classification clusters, obtaining failure rate as predicted failure probability of the transmission mechanism;
setting a transmission mechanism fault probability threshold, acquiring predicted transmission mechanism fault probability, calculating a folder access path safety degree value, and controlling a lubricating device according to the folder access path safety degree value to perform advanced treatment on transmission mechanism faults;
setting a fault probability threshold G of a transmission mechanism min When the predicted failure probability of the transmission mechanism is larger than the failure probability threshold G of the transmission mechanism min When the controller opens the pressurizing nozzle, the safety degree value T of the file access path is calculated 0 ：
T 0 ＝G r -G min
According to the file access path safety degree value, outputting a pressurizing numerical value of the pressurizing nozzle, and transmitting the pressurizing numerical value to the lubricating device:
u 0 ＝T 0 *w 1
wherein u is 0 A pressurization value representing the pressurization spray head; w (w) 1 Representing the impact coefficient value;
after the pressurizing nozzle obtains the pressurizing value, the lubricating oil in the oil storage tank is sprayed onto the transmission mechanism, and after the lubricating oil is sprayed, the controller closes the pressurizing nozzle.
When the predicted transmission failure probability is lower than the transmission failure probability threshold G min When the controller does not change.
In the second embodiment, a folder access path intelligent security analysis system based on big data is provided, and the system comprises a folder intelligent storage module, a test module, a fault analysis module and a control module;
the intelligent storage module of the file folder is used for storing the file folder into a file cabinet by a user, generating a two-dimension code for taking a file, and sending the two-dimension code to a user port; the user initiates a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and a file storage bin carrying the file of the user is transmitted to a file taking port at the front panel of the file cabinet through a transmission mechanism; the test module is used for acquiring historical test data and building a transmission mechanism fault prediction model; the fault analysis module is used for acquiring the weight of each layer of folder storage bin in real time, acquiring the data stored and fetched by a user in the file cabinet, and predicting the fault probability of the transmission mechanism; the control module is used for calculating a folder access path safety degree value, controlling the lubricating device to spray lubricating oil according to the folder access path safety degree value, and preventing the transmission mechanism from failure;
the output end of the test module is connected with the input end of the fault analysis module; the output end of the fault analysis module is connected with an administrator computer system; the administrator computer system is connected with the input end of the control module; the output end of the control module is connected with the lubricating device;
the intelligent storage module of the folder is connected with an administrator computer system.
The intelligent storage module of the folder comprises: the file cabinet, the transmission mechanism, the folder storage bin, the front panel of the file cabinet and the data transmission sub-module;
the folder storage bin is used for storing folders; the transmission mechanism is used for driving the folder storage bin to transmit; the front panel of the file cabinet is used for a user to take a piece; the data transmission submodule is used for storing the file folder into the file cabinet by a user, generating a piece taking two-dimensional code and sending the piece taking two-dimensional code to the user port; the user initiates a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and a file storage bin carrying the file of the user is transmitted to a file taking port at the front panel of the file cabinet through a transmission mechanism;
the data transmission sub-module is connected with the transmission mechanism.
The test module comprises a test data acquisition sub-module and a model construction sub-module;
the test data acquisition sub-module is used for acquiring historical test data; the model construction submodule is used for constructing a transmission mechanism fault prediction model;
the output end of the test data acquisition sub-module is connected with the input end of the model construction sub-module; the output end of the model construction submodule is connected with the input end of the fault analysis module.
The fault analysis module comprises a real-time data acquisition sub-module and a fault probability analysis sub-module;
the real-time data acquisition sub-module is used for acquiring the weight of each layer of folder storage bin in real time and acquiring the data stored and acquired by a user in the file cabinet; the fault probability analysis submodule is used for predicting the fault probability of the transmission mechanism according to the transmission mechanism fault prediction model;
the output end of the real-time data acquisition sub-module is connected with the input end of the fault probability analysis sub-module; and the output end of the fault probability analysis sub-module is connected with the administrator computer system.
The control module comprises a folder access path safety degree calculation sub-module and an intelligent control sub-module;
the folder access path safety degree computing sub-module is used for computing a folder access path safety degree value; the intelligent control submodule is used for controlling the lubricating device to spray lubricating oil according to the folder access path safety degree value so as to prevent the transmission mechanism from malfunctioning.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The intelligent security analysis method for the folder access path based on big data is characterized by comprising the following steps of: the method comprises the following steps:
s1, a user stores a folder into a file cabinet, generates a piece taking two-dimensional code and sends the two-dimensional code to a user port;
s2, a user initiates a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and a file storage bin carrying the user file is transmitted to a file taking port at the front panel of the file cabinet through a transmission mechanism;
s3, acquiring historical test data, building a transmission mechanism fault prediction model, acquiring the weight of each layer of folder storage bin in real time, acquiring the data stored and fetched in a file cabinet by a user, and predicting the transmission mechanism fault probability;
s4, setting a transmission mechanism fault probability threshold value, obtaining predicted transmission mechanism fault probability, calculating a folder access path safety degree value, and controlling a lubricating device according to the folder access path safety degree value to perform advanced treatment on transmission mechanism faults;
the historical test data comprise the monitored running times of the transmission mechanism, and each layer of the folder storage bin is replaced to be recorded as one running time of the transmission mechanism in the transmission process; and the weight of the folder storage bin set in each transmission;
the historical test data also comprises transmission mechanism fault data;
the transmission mechanism fault prediction model comprises:
carrying out normalization processing and outlier filtering on the historical test data;
selecting N test data from the processed historical test data, and marking the N test data as { x } 1 ，x 2 ，···，x N Each test data has m attribute characteristics, wherein the attribute characteristics refer to the running times of the transmission mechanism and the weight of a folder storage bin arranged in each transmission;
randomly selecting K centroids from N sample data to serve as initial clustering centers, and marking as:
{C 1 ，C 2 ，···，C K }，1<K≤N
defining an optimization target:
wherein L (α, β) represents an optimization function; x is x i Representing the ith test data; alpha i Represents x i A cluster to which the cluster belongs;representing cluster alpha i A corresponding center point; m represents the total number of test data;
setting the iteration times t=0, 1,2, … …;
s3-1, calculating Euclidean distance from each test data to each cluster center, distributing the test data to the cluster center with the smallest Euclidean distance, and generating S clusters:
wherein,represents x at the t th iteration i A cluster to which the cluster belongs; />Representing the center point corresponding to the cluster S under the t-th iteration; s is S h Represents any one of the generated S clusters;
s3-2, recalculating the cluster center for each cluster center of the generated S clusters;
wherein F is t+1，h Cluster S representing the t-th iteration h New cluster centers generated in the t+1st iteration; s h I represents cluster S h The number of test data;
s3-3, repeating the steps S3-1 and S3-2 until L (alpha, beta) converges, i.e. all clusters are not changed any more;
s3-4, acquiring the number of test data with faults of the transmission mechanism in all the test data in the clusters, and recording the number as h 0 Calculating the failure rate:
wherein G is r Representing the failure rate of the cluster r generated after convergence of L (alpha, beta); h is a r Representing the number of test data of the cluster r generated after convergence of L (alpha, beta);
the transmission mechanism is connected with a lubricating device;
an oil storage tank, a pressurizing nozzle and a controller are arranged in the lubricating device;
the oil storage tank is used for storing lubricating oil; the pressurizing nozzle is used for setting a pressurizing value, spraying out lubricating oil in the oil storage tank and enabling the lubricating oil to cover the transmission mechanism; the controller is used for controlling the on-off of the pressurized spray head;
acquiring the weight of each layer of folder storage bin in real time, acquiring the data stored and acquired by a user in a file cabinet, and acquiring a classification cluster of a current file cabinet transmission mechanism according to a transmission mechanism fault prediction model;
according to the classification clusters, obtaining failure rate as predicted failure probability of the transmission mechanism;
setting a fault probability threshold G of a transmission mechanism min When the predicted failure probability of the transmission mechanism is larger than the failure probability threshold G of the transmission mechanism min When the controller opens the pressurizing nozzle, the safety degree value T of the file access path is calculated 0 ：
T 0 ＝G r -G min
According to the file access path safety degree value, outputting a pressurizing numerical value of the pressurizing nozzle, and transmitting the pressurizing numerical value to the lubricating device:
u 0 ＝T 0 *w 1
wherein u is 0 A pressurization value representing the pressurization spray head; w (w) 1 Representing the impact coefficient value;
after the pressurizing spray head obtains the pressurizing value, the lubricating oil in the oil storage tank is sprayed onto the transmission mechanism, and after the lubricating oil is sprayed, the controller closes the pressurizing spray head;
when the predicted transmission failure probability is lower than the transmission failure probability threshold G min When the controller does not change.
2. A big data based folder access path intelligent security analysis system employing the big data based folder access path intelligent security analysis method of claim 1, wherein: the system comprises a folder intelligent storage module, a testing module, a fault analysis module and a control module;
the intelligent storage module of the file folder is used for storing the file folder into a file cabinet by a user, generating a two-dimension code for taking a file, and sending the two-dimension code to a user port; the user initiates a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and a file storage bin carrying the file of the user is transmitted to a file taking port at the front panel of the file cabinet through a transmission mechanism; the test module is used for acquiring historical test data and building a transmission mechanism fault prediction model; the fault analysis module is used for acquiring the weight of each layer of folder storage bin in real time, acquiring the data stored and fetched by a user in the file cabinet, and predicting the fault probability of the transmission mechanism; the control module is used for calculating a folder access path safety degree value, controlling the lubricating device to spray lubricating oil according to the folder access path safety degree value, and preventing the transmission mechanism from failure;
the output end of the test module is connected with the input end of the fault analysis module; the output end of the fault analysis module is connected with an administrator computer system; the administrator computer system is connected with the input end of the control module; the output end of the control module is connected with the lubricating device;
the intelligent storage module of the folder is connected with an administrator computer system.
3. The big data based folder access path intelligent security analysis system of claim 2, wherein: the intelligent storage module of the folder comprises: the file cabinet, the transmission mechanism, the folder storage bin, the front panel of the file cabinet and the data transmission sub-module;
the folder storage bin is used for storing folders; the transmission mechanism is used for driving the folder storage bin to transmit; the front panel of the file cabinet is used for a user to take a piece; the data transmission submodule is used for storing the file folder into the file cabinet by a user, generating a piece taking two-dimensional code and sending the piece taking two-dimensional code to the user port; the user initiates a file taking application to the file cabinet through the file taking two-dimension code, the file cabinet recognizes the file taking two-dimension code, and a file storage bin carrying the file of the user is transmitted to a file taking port at the front panel of the file cabinet through a transmission mechanism;
the data transmission sub-module is connected with the transmission mechanism.
4. The big data based folder access path intelligent security analysis system of claim 2, wherein: the test module comprises a test data acquisition sub-module and a model construction sub-module;
the test data acquisition sub-module is used for acquiring historical test data; the model construction submodule is used for constructing a transmission mechanism fault prediction model;
the output end of the test data acquisition sub-module is connected with the input end of the model construction sub-module; the output end of the model construction submodule is connected with the input end of the fault analysis module.
5. The big data based folder access path intelligent security analysis system of claim 2, wherein: the fault analysis module comprises a real-time data acquisition sub-module and a fault probability analysis sub-module;
the real-time data acquisition sub-module is used for acquiring the weight of each layer of folder storage bin in real time and acquiring the data stored and acquired by a user in the file cabinet; the fault probability analysis submodule is used for predicting the fault probability of the transmission mechanism according to the transmission mechanism fault prediction model;
the output end of the real-time data acquisition sub-module is connected with the input end of the fault probability analysis sub-module; and the output end of the fault probability analysis sub-module is connected with the administrator computer system.
6. The big data based folder access path intelligent security analysis system of claim 2, wherein: the control module comprises a folder access path safety degree calculation sub-module and an intelligent control sub-module;
the folder access path safety degree computing sub-module is used for computing a folder access path safety degree value; the intelligent control submodule is used for controlling the lubricating device to spray lubricating oil according to the folder access path safety degree value so as to prevent the transmission mechanism from malfunctioning.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1371630A (en) * | 1970-10-02 | 1974-10-23 | Owens Illinois Inc | Lubricating system for endless chain |
| DE202006016309U1 (en) * | 2006-10-23 | 2006-12-21 | Keuro Besitz Gmbh & Co Edv-Dienstleistungs Kg | Shelf unit for storing flat articles has shelves which are raised by rack and pinion system on either side |
| CN107558079A (en) * | 2017-09-19 | 2018-01-09 | 浙江理工大学 | Heat setting machine chain quantitative intelligence oil-supplementing system |
| CN207005255U (en) * | 2017-06-14 | 2018-02-13 | 中国石油化工股份有限公司洛阳分公司工会委员会 | A kind of self-lubricate grease injector for being used to circulate driving chain |
| CN109522926A (en) * | 2018-09-28 | 2019-03-26 | 南京航空航天大学 | Method for detecting abnormality based on comentropy cluster |
| CN209601374U (en) * | 2018-09-21 | 2019-11-08 | 上海祺申工程塑料有限公司 | A kind of Novel hoist reducing job failure |
| CN110436106A (en) * | 2019-09-02 | 2019-11-12 | 江苏迅捷装具科技有限公司 | A kind of intellectual access license machine |
| CN110822071A (en) * | 2019-10-16 | 2020-02-21 | 广东寰球智能科技有限公司 | Chain lubrication control method, device, equipment and storage medium |
-
2022
- 2022-05-19 CN CN202210545346.4A patent/CN115215023B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB1371630A (en) * | 1970-10-02 | 1974-10-23 | Owens Illinois Inc | Lubricating system for endless chain |
| DE202006016309U1 (en) * | 2006-10-23 | 2006-12-21 | Keuro Besitz Gmbh & Co Edv-Dienstleistungs Kg | Shelf unit for storing flat articles has shelves which are raised by rack and pinion system on either side |
| CN207005255U (en) * | 2017-06-14 | 2018-02-13 | 中国石油化工股份有限公司洛阳分公司工会委员会 | A kind of self-lubricate grease injector for being used to circulate driving chain |
| CN107558079A (en) * | 2017-09-19 | 2018-01-09 | 浙江理工大学 | Heat setting machine chain quantitative intelligence oil-supplementing system |
| CN209601374U (en) * | 2018-09-21 | 2019-11-08 | 上海祺申工程塑料有限公司 | A kind of Novel hoist reducing job failure |
| CN109522926A (en) * | 2018-09-28 | 2019-03-26 | 南京航空航天大学 | Method for detecting abnormality based on comentropy cluster |
| CN110436106A (en) * | 2019-09-02 | 2019-11-12 | 江苏迅捷装具科技有限公司 | A kind of intellectual access license machine |
| CN110822071A (en) * | 2019-10-16 | 2020-02-21 | 广东寰球智能科技有限公司 | Chain lubrication control method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 韩力群.人工智能.北京邮电大学出版社,2020,第79-84页. * |
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