CN115269958A - Internet reliability data information acquisition and analysis system - Google Patents
Internet reliability data information acquisition and analysis system Download PDFInfo
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- CN115269958A CN115269958A CN202210844816.7A CN202210844816A CN115269958A CN 115269958 A CN115269958 A CN 115269958A CN 202210844816 A CN202210844816 A CN 202210844816A CN 115269958 A CN115269958 A CN 115269958A
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Abstract
The invention discloses an internet reliability data information acquisition and analysis system, which belongs to the technical field of data acquisition and analysis and comprises an acquisition module, a processing module, an analysis module and a server; the acquisition module is used for acquiring data, acquiring the acquired data and sending the acquired data to the processing module, and the processing module is used for processing the acquired data to acquire reliability data and sending the reliability data to the analysis module; the analysis module analyzes and pushes the received reliable data to obtain reference data, analyzes the reference data to obtain a weight coefficient, performs priority ordering on the reliable data to obtain a first sequence, selects the first D reliable data in the first sequence as target data, and sends the target data to corresponding personnel, wherein D is a positive integer; by acquiring the reliability data based on the Internet, the data sources are greatly increased, the quantity of the acquired and processed reliability data is much larger, and the sample data at the design stage is expanded.
Description
Technical Field
The invention belongs to the technical field of data acquisition and analysis, and particularly relates to an internet reliability data information acquisition and analysis system.
Background
Reliability data is the basis of reliability engineering, the reliability engineering runs through the whole process from product planning, design, test, manufacture to maintenance, and the data of the whole process is collected and analyzed; particularly, fault data tells people to design important information on weak links of the system and how to improve the weak links; the production of reliable data is divided into three stages (design, production, use and maintenance);
based on the above problems, the invention provides an internet reliability data information acquisition and analysis system, and a large amount of reliability data is obtained based on the internet.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides an internet reliability data information acquisition and analysis system.
The purpose of the invention can be realized by the following technical scheme:
the internet reliability data information acquisition and analysis system comprises an acquisition module, a processing module, an analysis module and a server;
the acquisition module is used for acquiring data, acquiring the acquired data and sending the acquired data to the processing module, and the processing module is used for processing the acquired data to acquire reliability data and sending the reliability data to the analysis module;
the analysis module analyzes and pushes the received reliable data to obtain reference data, analyzes the reference data to obtain a weight coefficient, performs priority ranking on the reliable data to obtain a first sequence, selects the first D reliable data in the first sequence as target data, and sends the target data to corresponding personnel, wherein D is a positive integer.
Further, the working method of the acquisition module comprises the following steps:
acquiring a data demand attribute, setting a corresponding retrieval formula according to the acquired data demand attribute, and performing data retrieval in the network through the set retrieval formula to acquire acquired data.
Further, the method for acquiring the data demand attribute comprises the following steps:
setting a standard attribute table, establishing a language processing model, filling data requirement attributes according to the standard attribute table by related personnel, marking attribute items needing language supplementation when parts inconvenient to be directly filled appear, performing language supplementation, inputting the supplemented language and corresponding attribute items into the language processing model, obtaining language supplementation data of corresponding attribute items, supplementing the language supplementation data into corresponding positions in the standard attribute table, and identifying the filled standard attribute table to obtain corresponding data requirement attributes.
Further, the working method of the processing module comprises the following steps:
dividing the acquired data into a plurality of single data, classifying the single data to obtain a classification set, matching a checking scheme corresponding to the classification set, checking the classification set according to the matching corresponding checking scheme, and obtaining reliability data according to a checking result.
Further, the method for classifying the single data comprises the following steps:
matching the detection check items corresponding to the single data, matching the corresponding assignments according to the matched detection check items, and establishing the positioning coordinates of the single data;
and setting a corresponding checking scheme according to the type of the single data, establishing a positioning diagram according to the checking scheme, and inputting the positioning coordinates into the positioning diagram to finish the classification of the corresponding single data.
Further, the method for establishing the positioning diagram according to the checking scheme comprises the following steps:
establishing an analysis model, analyzing the checking scheme through the analysis model to obtain a corresponding coordinate interval, establishing a coordinate system, marking the corresponding coordinate interval, marking a corresponding scheme label, and establishing a coordinate graph according to the current coordinate system.
Further, the method for performing reliability data prioritization comprises the following steps:
labeling reliability data as i, wherein i =1, 2, … …, n is a positive integer; marking the weight coefficients as QZi, acquiring the time span of each reliability data, marking as STi, acquiring the reliability of each reliability data, marking as KDi, acquiring the use times of each reliability data, marking as SYi, setting the weight coefficients among the time span, the reliability and the use times, respectively marking as beta 1, beta 2 and beta 3, calculating the priority value according to a priority formula, and sequencing according to the calculated priority value.
Further, the priority formula is Qi = QZi × (b 1 × β 1 × STi + b2 × β 2 × KDi + b3 × β 3 × SYi), wherein b1, b2, and b3 are all proportional coefficients, and the value ranges are 0 & lt b1 & lt 1 & gt, 0 & lt b2 & lt 1 & gt, 0 & lt b3 & lt 1 & gt and 0 & lt b3 & lt 1 & gt.
Compared with the prior art, the invention has the beneficial effects that:
reliability data are obtained based on the Internet, so that data sources are greatly increased, the quantity of the collected and processed reliability data is much larger, and sample data at a design stage is expanded; and through carrying out priority sequencing on the reliability data and carrying out target sequencing with emphasis, relevant personnel can find the required data from a large amount of reliability data more conveniently, and the problem that the work efficiency is influenced by carrying out disordered searching from a large amount of data is avoided.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the internet reliability data information acquisition and analysis system includes an acquisition module, a processing module, an analysis module and a server;
the acquisition module is used for acquiring data, and the specific method comprises the following steps:
the method comprises the steps of obtaining data demand attributes, setting corresponding search formulas according to the obtained data demand attributes, and achieving establishment of the corresponding search formulas through the existing method, so that data retrieval is conducted in a network through the set search formulas without detailed description, and collected data are obtained. And the acquired data is past weighted.
The data requirement attribute is what kind of data is needed, for example, in the design stage of the product, the reliability of a new product design is predicted by collecting the reliability data of the same kind of product, which is beneficial to the comparison and selection of the scheme, so that the corresponding data requirement attribute, namely what kind of data is needed, needs to be set according to the design requirement.
The method for acquiring the data demand attribute comprises the following steps:
in one embodiment, for the case where the writing is directly available by the designer concerned, the writing is directly available by the designer concerned and thus can be obtained directly.
In another embodiment, in order to facilitate subsequent data processing according to the data requirement attributes and facilitate writing by relevant personnel, the expert group sets a corresponding standard attribute table, sets items of the corresponding standard attribute table mainly according to establishment of a search formula, and the relevant personnel fill the data requirement attributes according to the standard attribute table and identify the filled standard attribute table, so that the corresponding data requirement attributes can be obtained.
In another embodiment, it is inconvenient to fill some data attributes directly, because some data may have written inconvenient statements in many cases, but the contents to be expressed can be well expressed by combining language statements, so based on the above problem, a language processing model is established, the language processing model is established based on a CNN network or a DNN network, and is obtained by setting a corresponding training set for training, relevant personnel fills in data requirement attributes according to a standard attribute table, when a part which is inconvenient to be directly filled occurs, an attribute item which needs to be language supplemented is marked, namely, the item in the standard attribute table needs to be language supplemented, language supplementation is directly carried out by marking of relevant personnel, the supplemented language and the corresponding attribute item are input into the language processing model, language supplementation data of the corresponding attribute item is obtained and supplemented into a corresponding position in the standard attribute table, and the filled standard attribute table is identified, so as to obtain the corresponding data requirement attributes.
The data processed by the language processing model is in the same format as the filled-in data.
The processing module is used for processing the acquired data, and the specific method comprises the following steps:
dividing the acquired data into a plurality of single data, wherein the single data is a data set and comprises parameter information of corresponding data, such as data life cycle and the like, and parameter data which can be used for judging the reliability of related data; and classifying the single data to obtain a classification set, matching a checking scheme corresponding to the classification, checking the classification set according to the matching corresponding checking scheme, and obtaining reliability data according to a checking result.
The method for classifying the single data comprises the following steps:
matching the detection check items of the corresponding single data, matching the corresponding assignments according to the matched detection check items, and establishing the positioning coordinates of the single data;
and setting a corresponding checking scheme according to the type of the single data, establishing a positioning diagram according to the checking scheme, and inputting the positioning coordinates into the positioning diagram to finish the classification of the corresponding single data.
The detection check items matched with the corresponding single data are matched according to the types of the single data, an expert group establishes a corresponding check item matching table according to the types of the single data, corresponding assignment is set for each check item, the check items are subdivided according to large items needing checking, the detected large items are basically the same in type when data reliability checking is carried out, the check items are subdivided into corresponding small items through the types of the corresponding single data, namely the check items, the difference of the check items is that the check items can be carried out according to whether the same item can be checked by adopting the same check method, the check items are set according to the corresponding check methods and are used for carrying out fast checking after classification, the check is carried out by adopting the same check method in the same type, and the check efficiency is improved.
And (4) establishing the positioning coordinates of the single data according to the detection check items and the corresponding assignments.
The checking scheme is set by an expert group according to the possible single data types, a plurality of existing methods for checking the reliability of the data exist, and the expert group determines the corresponding checking method according to the existing method and the corresponding single data types.
The method for establishing the positioning diagram according to the checking scheme comprises the following steps:
establishing an analysis model, wherein the analysis model is established based on a CNN network or a DNN network, training is performed by establishing a corresponding training set, and the training set is set according to a checking scheme and a checking and checking item matching table;
and analyzing the checking scheme through the analysis model to obtain a corresponding coordinate interval, establishing a coordinate system, marking the corresponding coordinate interval, marking a corresponding scheme label, and establishing a coordinate graph according to the current coordinate system.
When the positioning coordinates are input into the positioning diagram, corresponding coordinate areas are identified, the single data in the same coordinate area are classified into one type, and the corresponding checking scheme is matched according to the scheme label of the coordinate area.
The analysis module is used for analyzing and pushing the obtained reliability data, and the specific method comprises the following steps:
obtaining reference data, analyzing the reference data, obtaining a weight coefficient, performing priority sorting on reliability data to obtain a first sequence, selecting front D reliability data in the first sequence as target data, and sending the target data to corresponding personnel, wherein D is a positive integer and can be set by related personnel.
The reference data is described by related design stage personnel, mainly for describing demand purposes, is used for analyzing the point of interest, whether the fault data is the correct reference data, and further can obtain the proportion coefficient of the corresponding reliability data.
The method for performing reliability data prioritization comprises the following steps:
labeling reliability data as i, wherein i =1, 2, … …, n is a positive integer; marking the weight coefficients as QZi, acquiring the time span of each reliability data as STi, acquiring the reliability of each reliability data as KDi, acquiring the use times of each reliability data as SYi, setting the weight coefficients among the time span, the reliability and the use times as beta 1, beta 2 and beta 3, respectively, calculating priority values according to a priority formula Qi = QZi × (b 1 × β 1 × STi + b2 × β 2 × KDi + b3 × β 3 × SYi), wherein b1, b2 and b3 are all proportional coefficients, the range is 0 and b1 is less than or equal to 1,0 and b2 is less than or equal to 1, and 0 and b3 is less than or equal to 1, and sorting is performed according to the calculated priority values.
And obtaining the reliability according to the corresponding checking result, or obtaining the corresponding reliability by the existing checking method.
The number of times of use refers to the number of times of access in the internet.
The weight coefficients among the time span, the reliability and the use times are set by the discussion of the expert group.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: the data acquisition is carried out through the acquisition module, the acquired data are acquired and sent to the processing module, the processing module processes the acquired data to acquire reliability data, and the reliability data are sent to the analysis module; the analysis module analyzes and pushes the received reliable data to obtain reference data, analyzes the reference data to obtain a weight coefficient, performs priority sorting on the reliable data to obtain a first sequence, selects the first D reliable data in the first sequence as target data, and sends the target data to corresponding personnel.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present invention.
Claims (8)
1. The internet reliability data information acquisition and analysis system is characterized by comprising an acquisition module, a processing module, an analysis module and a server;
the acquisition module is used for acquiring data, acquiring the acquired data and sending the acquired data to the processing module, and the processing module is used for processing the acquired data to acquire reliability data and sending the reliability data to the analysis module;
the analysis module analyzes and pushes the received reliable data to obtain reference data, analyzes the reference data to obtain a weight coefficient, performs priority ranking on the reliable data to obtain a first sequence, selects the first D reliable data in the first sequence as target data, and sends the target data to corresponding personnel, wherein D is a positive integer.
2. The internet reliability data information acquisition and analysis system of claim 1, wherein the operation method of the acquisition module comprises:
acquiring a data demand attribute, setting a corresponding retrieval formula according to the acquired data demand attribute, and performing data retrieval in the network through the set retrieval formula to acquire acquired data.
3. The internet reliability data information acquisition and analysis system according to claim 2, wherein the method of obtaining the data requirement attribute is:
setting a standard attribute table, establishing a language processing model, filling data requirement attributes according to the standard attribute table by related personnel, marking attribute items needing language supplementation when parts inconvenient to be directly filled appear, performing language supplementation, inputting the supplemented language and corresponding attribute items into the language processing model, obtaining language supplementation data of corresponding attribute items, supplementing the language supplementation data into corresponding positions in the standard attribute table, and identifying the filled standard attribute table to obtain corresponding data requirement attributes.
4. The internet reliability data information acquisition and analysis system of claim 1, wherein the processing module operates in a method comprising:
dividing the acquired data into a plurality of single data, classifying the single data to obtain a classification set, matching a checking scheme corresponding to the classification set, checking the classification set according to the matching corresponding checking scheme, and obtaining reliability data according to a checking result.
5. The internet reliability data information collection and analysis system of claim 4 wherein the method of classifying a single item of data comprises:
matching the detection check items corresponding to the single data, matching the corresponding assignments according to the matched detection check items, and establishing the positioning coordinates of the single data;
and setting a corresponding checking scheme according to the type of the single data, establishing a positioning diagram according to the checking scheme, and inputting the positioning coordinates into the positioning diagram to finish the classification of the corresponding single data.
6. The internet reliability data information acquisition and analysis system of claim 5 wherein the method of creating a location map based on the verification scheme includes:
establishing an analysis model, analyzing the checking scheme through the analysis model to obtain a corresponding coordinate interval, establishing a coordinate system, marking the corresponding coordinate interval, marking a corresponding scheme label, and establishing a coordinate graph according to the current coordinate system.
7. The internet reliability data information collection and analysis system of claim 1 wherein the method of prioritization of reliability data includes:
labeling reliability data as i, wherein i =1, 2, … …, n is a positive integer; marking the weight coefficients as QZi, acquiring the time span of each reliability data, marking as STi, acquiring the reliability of each reliability data, marking as KDi, acquiring the use times of each reliability data, marking as SYi, setting the weight coefficients among the time span, the reliability and the use times, respectively marking as beta 1, beta 2 and beta 3, calculating the priority value according to a priority formula, and sequencing according to the calculated priority value.
8. The internet reliability data information acquisition and analysis system according to claim 7, wherein the priority formula is Qi = QZi × (b 1 × β 1 × STi + b2 × β 2 × KDi + b3 × β 3 × SYi), wherein b1, b2, b3 are all proportional coefficients, and the value ranges are 0 & lt b1 & lt 1 & gt, 0 & lt b2 & lt 1 & gt, 0 & lt b3 & lt 1 & gt.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115935533A (en) * | 2022-11-02 | 2023-04-07 | 北京能科瑞元数字技术有限公司 | Intelligent product design system based on parameters |
| CN116224879A (en) * | 2023-03-22 | 2023-06-06 | 佛山市众合科技有限公司 | Industrial data processing system based on cloud computing |
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- 2022-07-18 CN CN202210844816.7A patent/CN115269958A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115935533A (en) * | 2022-11-02 | 2023-04-07 | 北京能科瑞元数字技术有限公司 | Intelligent product design system based on parameters |
| CN115935533B (en) * | 2022-11-02 | 2023-10-03 | 北京能科瑞元数字技术有限公司 | Product intelligent design system based on parameters |
| CN116224879A (en) * | 2023-03-22 | 2023-06-06 | 佛山市众合科技有限公司 | Industrial data processing system based on cloud computing |
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