CN115237739B - Analysis method, device and equipment for board card running environment and readable storage medium - Google Patents
Analysis method, device and equipment for board card running environment and readable storage medium Download PDFInfo
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Abstract
The invention provides a method, a device and equipment for analyzing a board card running environment and a readable storage medium, which relate to the technical field of computers and comprise the steps of obtaining first information, wherein the first information comprises environment data information of a board card and running data information of the board card; preprocessing the first information to obtain board card running data information under each preprocessed environment data; processing the board operation data information based on a grey correlation analysis method to obtain an operation score value; processing the operation score value based on a least square linear fitting method to obtain second information, wherein the second information is the operation score fitting value of each time slot board card under each environmental data and the change rate of the operation score fitting value; and determining the optimal operating environment of the board card based on the second information. The method can obtain more accurate and objective scoring and reduce the investment of manpower and material resources.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a device and equipment for analyzing a board running environment and a readable storage medium.
Background
At present, the operating efficiency of the board card is influenced by various environments, the service life of the board card can be shortened in various environments, the calculated amount of manual analysis is large, a lot of manpower and material resources are needed, a method for analyzing the environmental data of the board card is needed to determine the optimal operating environment of the board card, and further the property loss caused by damage of the board card or slow operation and elimination of the board card is reduced.
Disclosure of Invention
The present invention is directed to a method, an apparatus, a device and a readable storage medium for analyzing a board running environment, so as to solve the above problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a method for analyzing a board operating environment, including: acquiring at least one piece of first information, wherein the first information comprises operation data information of a board card and environment data information of the board card when the operation data is acquired; the operation data information comprises at least two; the environment data information comprises magnetic field intensity information, humidity information and temperature information around the board card when in operation, and the operation data information of the board card comprises sampling speed information, using channel number information, resolution ratio information and acquisition precision information of the board card;
preprocessing the operation data information to obtain preprocessed operation data information;
processing the preprocessed running data information and the environmental data information based on a grey correlation analysis method, converting the correlation degree obtained through processing into a weighted value, and then performing product calculation on the weighted value and the environmental data information to obtain a running grade value of each preprocessed running data information;
processing all the operation score values according to a least square linear fitting method to obtain second information, wherein the second information is the operation score fitting value of each time slot board card under each environmental data and the variation rate of the operation score fitting value;
and performing product operation on the change rate of the operation score fitting value under each environmental data and the operation score fitting value according to a preset weight proportion, and taking the environmental data corresponding to the highest score in the operation result as the optimal operation environment of the board card.
In a second aspect, the present application further provides an analysis apparatus for a board operating environment, including:
the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring at least one piece of first information, and the first information comprises operation data information of a board card and environment data information of the board card when the operation data is acquired; the operation data information comprises at least two; the environment data information comprises magnetic field intensity information, humidity information and temperature information around the board card when the board card runs, and the running data information of the board card comprises sampling speed information, using channel number information, resolution ratio information and acquisition precision information of the board card;
the first processing unit is used for preprocessing the running data information to obtain preprocessed running data information;
the second processing unit is used for processing the preprocessed running data information and the environment data information based on a grey correlation analysis method, converting the correlation degree obtained by processing into a weight value, and then performing product calculation on the weight value and the environment data information to obtain a running score value of each preprocessed running data information;
the third processing unit is used for processing all the operation score values according to a least square linear fitting method to obtain second information, wherein the second information is the operation score fitting value of each time slot board under each environmental data and the change rate of the operation score fitting value;
and the fourth processing unit is used for performing product operation on the operation score fitting value under each environmental data and the change rate of the operation score fitting value according to a preset weight proportion, and taking the environmental data corresponding to the highest score in the operation result as the optimal operation environment of the board card.
In a third aspect, the present application further provides an analysis device for a board operating environment, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the analysis method of the board card running environment when executing the computer program.
In a fourth aspect, the present application further provides a readable storage medium, where a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the steps of the analysis method based on the board operating environment are implemented.
The invention has the beneficial effects that:
according to the method, the data of the board card running under different running environments are preprocessed and converted into corresponding score values, then the key scores of the board card are selected for analysis according to the key factor screening, and then the running score change rate of the board card in different time periods is determined and judged, so that the possibility that the score is reduced quickly due to the fact that the running score is highest in one environment but the fluctuation is large is prevented, the accuracy of determining the best board card running environment is improved, the scoring is more accurate, and the investment of manpower and material resources is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of an analysis method of a board card operating environment according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an analysis apparatus for a board card operating environment according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an analysis device of a board card operating environment according to an embodiment of the present invention.
The labels in the figure are: 701. a first acquisition unit; 702. a first processing unit; 703. a second processing unit; 704. a third processing unit; 705. a fourth processing unit; 706. a second acquisition unit; 707. a first clustering unit; 708. a first analysis unit; 709. a third acquisition unit; 710. a fifth processing unit; 711. a sixth processing unit; 712. a seventh processing unit; 7021. a first clustering subunit; 7022. a first processing subunit; 7031. a second processing subunit; 7032. a first calculation subunit; 7033. a third processing subunit; 7041. a fourth processing subunit; 7042. a fifth processing subunit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a variety of configurations for each. Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
As shown in fig. 1, the present embodiment provides a method for analyzing a board operating environment, where the method includes step S1, step S2, step S3, step S4, and step S5.
S1, acquiring at least one piece of first information, wherein the first information comprises operation data information of a board card and environment data information of the board card when the operation data is acquired; the operation data information comprises at least two; the environment data information comprises magnetic field intensity information, humidity information and temperature information around the board card when in operation, and the operation data information of the board card comprises sampling speed information, using channel number information, resolution ratio information and acquisition precision information of the board card;
it can be understood that in this step, the environmental information around the board card is collected through a magnetic field intensity collecting device, wherein the magnetic field intensity around the board card is generally collected through a magnetic field intensity collecting device, the environmental information around the board card is collected through a temperature collector and a humidity collector, the temperature and humidity information around the board card are collected through a temperature collector and a humidity collector, and the sampling speed information, the using channel number information, the resolution ratio information and the collecting precision information of the board card are tested and data are collected through a board card testing device.
S2, preprocessing the operation data information to obtain preprocessed operation data information;
s3, processing the preprocessed running data information and the environment data information based on a grey correlation analysis method, converting the correlation degree obtained through processing into a weight value, and then performing product calculation on the weight value and the environment data information to obtain a running score value of each preprocessed running data information;
s4, processing all the operation score values according to a least square linear fitting method to obtain second information, wherein the second information is the operation score fitting value of each time slot board under each environmental data and the change rate of the operation score fitting value;
it can be understood that in the step, all scores are segmented according to a certain time sequence, and the operation score values and the change rates of the boards which operate in different time periods under different environments are determined, so that the possibility that the scores are rapidly reduced due to large fluctuation although the operation score is highest under one environment is prevented, and the accuracy of determining the optimal board operation environment is further improved.
And S5, performing product operation on the change rate of the operation score fitting value and the operation score fitting value under each environmental data according to a preset weight proportion, and taking the environmental data corresponding to the highest score in the operation result as the optimal operation environment of the board card.
The method and the device can judge the optimal operation environment of the board card by analyzing and checking the environment data of the board card according to the operation data of the board card in each environment and based on the rating state of the board card in each environment and the rating change rate of the board card operating in each time period under the same environment data information, wherein the optimal operation environment refers to the condition that the rating state of the board card in the environment is the best, and the rating state of the board card operating in the environment for a period of time is the slowest to descend or the fastest to ascend, so that the specific placement position of the equipment provided with the board card is determined, unnecessary loss is reduced, the service time of the board card is prolonged, and the data loss caused by frequent board card replacement is prevented while manpower and material resources are reduced.
In a specific embodiment of the present disclosure, the step S2 includes a step S21 and a step S22.
Step S21, performing data analysis on all the operation data information, wherein the operation data information is divided into incomplete information data and complete information data according to the integrity of the information, and clustering the incomplete information data and the complete information data respectively by adopting an EM (effective electromagnetic tomography) algorithm to obtain a clustering result of the incomplete information data and a clustering result of the complete information data;
and S22, calculating an average value based on the clustering result of the data with complete information, and carrying out interpolation filling on the clustering result of the incomplete information by using the average value to obtain the preprocessed running data information.
The method and the device have the advantages that the environment data information of the board card corresponds to the operation data information of the board card, the operation data information of the board card is determined to be performed in a certain environment, the operation data information of the board card is classified, clustering is performed by using an EM algorithm to obtain a clustering result, and then the clustering result of incomplete information is filled by using a KNN algorithm, so that the preprocessed data are all original data, the original form of the data is reserved, the influence of missing data is weakened, and the reliability of the data is improved.
It will be appreciated that the maximum expectation algorithm is an algorithm that finds the parameter maximum likelihood estimate or maximum a posteriori estimate in a probabilistic model that relies on hidden variables that cannot be observed. The algorithm is mainly calculated by two steps alternately, wherein the first step is to calculate expectation (E) and calculate the maximum likelihood estimated value of the hidden variable by using the existing estimated value of the hidden variable; the second step is to maximize (M), the maximum likelihood found at step E, to calculate the value of the parameter. The parameter estimates found in step M will be used for the next E step calculation, alternating until convergence. The most direct application of the EM algorithm is to find parameter estimation, but if we regard the potential category as hidden variable and the sample as observed value, the clustering problem can be converted into parameter estimation problem, which is the principle of clustering using the EM algorithm.
The KNN algorithm can be understood that the incomplete data subsets Di are sorted from small to large according to the number of the missing attribute values, then the distance from a point in each cluster in the sorted incomplete information clustering result to each cluster center formed by EM clustering is calculated, and sorting is carried out from small to large; classifying each incomplete record into a class with a cluster center with the minimum distance from the record; calculating the distance dis between the incomplete record and other training data in the class to which the incomplete record belongs by using an Euclidean distance formula;
in a specific embodiment of the present disclosure, the step S3 includes a step S31, a step S32, and a step S33.
Step S31, carrying out non-dimensionalization treatment on each piece of the preprocessed running data information and each piece of the environment data information to obtain the non-dimensionalized running data information and the non-dimensionalized environment data information;
step S32, calculating a correlation coefficient of the non-dimensionalized running data information and the environment data information based on the non-dimensionalized running data information and the environment data information, and calculating a correlation degree of each running data information and each environment data information based on the correlation coefficient;
step S33, converting the relevance into a scoring weight of the running data information under the environment data corresponding to the relevance, performing product calculation on the scoring weight and the board running data under the environment data corresponding to the relevance, and taking the result of the product calculation as the running scoring value of each running data information.
The correlation coefficient between the board operation data and the environment data of the board is calculated by performing grey correlation analysis on the board operation data information under each preprocessed environment data, the correlation degree is calculated, the correlation degree is used as the weight proportion of the board operation data information under each environment data, and the product obtained by multiplying the board operation data information under each environment data by the weight proportion is the operation score value under each environment data.
The board card environment data analysis method and the board card environment data analysis system can be used for objectively and accurately analyzing the board card environment data by determining the relevance between the board card operation data and the board card environment data to calculate the operation score value.
In a specific embodiment of the present disclosure, the step S4 includes a step S41 and a step S42.
S41, sending all the operation score values to a preset space rectangular coordinate system to be converted into a first curve graph, fitting each operation score value in the first curve graph based on a least square linear fitting method to obtain a first fitting curve graph, and determining the board card operation score fitting value under each environmental data;
and S42, performing segmentation processing on the board operation score fitting values under all the environmental data according to the same time sequence, and performing statistical processing on the board operation score fitting values of each time period under each environmental data obtained through the segmentation processing to obtain the operation score fitting values of the board in each time period under each environmental data and the change rate of the operation score fitting values.
The method comprises the steps of fitting a first curve graph by adopting a least square linear fitting method, determining board operation score fitting values under each environmental data, then carrying out time period division on all board operation score fitting values under each environmental data, dividing all board operation score fitting values under each environmental data of each time period into all board operation score fitting values under each environmental data of each time period, classifying board operation score fitting values under the same environmental data of each time period, further obtaining operation score fitting values under each time period under the same environmental data, then comparing operation score fitting values under each time period under the same environmental data, dividing a reduced score fitting value by a reduced score fitting value, further obtaining a board score change rate of each time period, further determining performance change and score of board operation in a certain time period, further preventing the possibility that the board operation score is highest but fluctuates greatly and the score is reduced rapidly under one environment, and further improving the accuracy of determining the best operation environment.
It can be understood that the invention can greatly reduce the data amount to be stored on the premise of keeping the characteristics of the data curve by the least square linear fitting method, has high practical application value, reduces the cost and ensures the accuracy.
In a specific embodiment of the present disclosure, step S5 is followed by step S6, step S7, and step S8.
S6, acquiring the optimal operation environment information of at least one type of board card;
s7, clustering all the optimal operation environment information of the board card based on a distance clustering algorithm to obtain the range information of a cluster set of all the clusters;
and S8, calculating to obtain a threshold range corresponding to each cluster according to the range information of the cluster set of all the clusters, analyzing the threshold ranges corresponding to all the clusters, and taking the threshold ranges corresponding to the clusters as the optimal operating environment range of the board card.
It can be understood that the optimal operation environment information of each board card is analyzed by clustering the optimal operation environment information of at least one type of board card, so that an optimal operation range is obtained, the practicability of the invention is increased, the investment of manpower and material resources is reduced, and the cost is saved.
In a specific embodiment of the present disclosure, the step S5 is followed by a step S9, a step S10, a step S11, and a step S12,
S9, acquiring the optimal operation environment information of at least one type of board card;
s10, establishing a hierarchical structure model based on an analytic hierarchy process, calling all keywords of the optimal operation environment information of the various board cards, and classifying the operation data information of all the board cards according to each keyword to obtain the optimal operation environment information of the classified board cards;
s11, constructing the classified optimal operation environment information of the board cards into a pair comparison matrix based on a pair comparison method and a comparison scale of 1-9, and calculating to obtain a weight coefficient of each category of the optimal operation environment information of the board cards in the total category;
and S12, classifying each category of the optimal operation environment information of the board card based on a weight coefficient of each category of the optimal operation environment information of the board card in the total category, and determining the grade of each category of the optimal operation environment information of the board card.
The invention can be understood that the invention classifies the optimal operation environment information of at least one type of board card by constructing a hierarchical structure model, determines the optimal operation environment information of each board card, and then calculates the grade of the optimal operation environment information of each board card according to the weight of the optimal operation environment information.
Example 2
As shown in fig. 2, the present embodiment provides an apparatus for analyzing a board operating environment, where the apparatus includes a first obtaining unit 701, a first processing unit 702, a second processing unit 703, a third processing unit 704, and a fourth processing unit 705.
A first obtaining unit 701, configured to obtain at least one piece of first information, where the first information includes operation data information of a board card and environment data information of the board card when the operation data is collected; the operation data information comprises at least two; the environment data information comprises magnetic field intensity information, humidity information and temperature information around the board card when in operation, and the operation data information of the board card comprises sampling speed information, using channel number information, resolution ratio information and acquisition precision information of the board card;
a first processing unit 702, configured to perform preprocessing on the operation data information to obtain preprocessed operation data information;
the second processing unit 703 is configured to process the preprocessed running data information and the environment data information based on a gray correlation analysis method, convert the processed correlation degree into a weight value, and then perform product calculation on the weight value and the environment data information to obtain a running score value of each piece of preprocessed running data information;
the third processing unit 704 processes all the running score values according to a least square linear fitting method to obtain second information, wherein the second information is a running score fitting value of each time slot board under each environmental data and a variation rate of the running score fitting value;
a fourth processing unit 705, configured to perform product operation on the operation score fitting value under each environmental data and the change rate of the operation score fitting value according to a preset weight ratio, and use the environmental data corresponding to the highest score in the operation result as the optimal operation environment of the board card.
In a specific embodiment of the present disclosure, the first processing unit 702 includes a first clustering subunit 7021 and a first processing subunit 7022.
The first clustering subunit 7021 is configured to perform data analysis on all the operation data information, where the operation data information is divided into incomplete information data and complete information data according to the integrity of the information, and an EM algorithm is used to perform clustering processing on the incomplete information data and the complete information data respectively to obtain a clustering result of the incomplete information data and a clustering result of the complete information data;
the first processing subunit 7022 is configured to calculate an average value based on the clustering result of the complete information data, and perform interpolation filling on the clustering result of the incomplete information by using the average value to obtain the preprocessed running data information.
In an embodiment of the present disclosure, the second processing unit 703 includes a second processing subunit 7031, a first calculating subunit 7032, and a third processing subunit 7033.
A second processing subunit 7031, configured to perform non-dimensionalization processing on each piece of the preprocessed running data information and each piece of the environment data information to obtain non-dimensionalized running data information and non-dimensionalized environment data information;
a first calculating subunit 7032, configured to calculate a correlation coefficient between the non-dimensionalized running data information and the environment data information based on the non-dimensionalized running data information and the environment data information, and calculate a correlation degree between each piece of running data information and each piece of environment data information based on the correlation coefficient;
a third processing subunit 7033, configured to convert the association degree into a scoring weight of the operation data information under the environment data corresponding to the association degree, perform product calculation on the scoring weight and the board operation data under the environment data corresponding to the association degree, and use a result of the product calculation as an operation scoring value of each operation data information.
In a specific embodiment of the present disclosure, the third processing unit 704 includes a fourth processing subunit 7041 and a fifth processing subunit 7042.
A fourth processing subunit 7041, configured to send all the operation score values to a preset space rectangular coordinate system and convert the operation score values into a first curve graph, fit each operation score value in the first curve graph based on a least square linear fitting method to obtain a first fit curve graph, and determine a board operation score fit value under each environmental data;
a fifth processing subunit 7042, configured to perform segmentation processing on the board operation score fitted values in all the environment data according to the same time sequence, and perform statistical processing on the board operation score fitted value in each time period in each environment data obtained through the segmentation processing, to obtain an operation score fitted value of the board in each time period in each environment data and a change rate of the operation score fitted value.
In a specific embodiment of the present disclosure, the fourth processing unit 705 further includes a second obtaining unit 706, a first clustering unit 707, and a first analyzing unit 708.
A second obtaining unit 706, configured to obtain optimal operating environment information of at least one type of board card;
the first clustering unit 707 is configured to cluster all the optimal operating environment information of the board card based on a distance clustering algorithm to obtain range information of a cluster set of all cluster clusters;
the first analysis unit 708 is configured to calculate a threshold range corresponding to each cluster according to the range information of the cluster set of all the clusters, analyze the threshold range corresponding to all the clusters, and use the threshold range corresponding to the cluster as the optimal operating environment range of the board card.
In a specific embodiment of the present disclosure, the fourth processing unit 705 further includes a third obtaining unit 709, a fifth processing unit 710, a sixth processing unit 711, and a seventh processing unit 712.
A third obtaining unit 709, configured to obtain optimal operating environment information of at least one type of board card;
a fifth processing unit 710, configured to establish a hierarchical structure model based on an analytic hierarchy process, call all the keywords of the optimal operating environment information of the multiple boards, and classify the operating data information of all the boards according to each keyword to obtain the optimal operating environment information of the classified boards;
a sixth processing unit 711, configured to construct the classified optimal operating environment information of the board cards into a pair-wise comparison matrix based on a pair-wise comparison method and a 1-9 comparison scale, and calculate a weight coefficient of each category of the classified optimal operating environment information of the board cards in the total category;
a seventh processing unit 712, configured to grade each category of the optimal operating environment information of the board based on a weight coefficient of each category of the optimal operating environment information of the board occupying the total category, and determine a grade of each category of the optimal operating environment information of the board.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3
Corresponding to the above method embodiment, this embodiment further provides an analysis device for a board operating environment, and the analysis device for a board operating environment described below and the analysis method for a board operating environment described above may be referred to correspondingly.
FIG. 3 is a block diagram illustrating an analysis device 800 of a board operating environment in accordance with an exemplary embodiment. As shown in fig. 3, the analysis device 800 of the board operating environment may include: a processor 801, a memory 802. The analysis device 800 of the board operating environment may further include one or more of a multimedia component 803, an i/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the analysis apparatus 800 of the board operating environment, so as to complete all or part of the steps in the analysis method of the board operating environment. Memory 802 is used to store various types of data to support the operation of analytics device 800 in the board runtime environment, which may include, for example, instructions for any application or method operating on analytics device 800 in the board runtime environment, as well as application-related data such as contact data, messages sent or received, pictures, audio, video, and so forth. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication module 805 is used for wired or wireless communication between the analysis device 800 of the board operating environment and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (NFC for short), 2G, 3G, or 4G, or a combination of one or more of them, so the corresponding communication component 805 may include: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the analysis Device 800 of the board operating environment may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to perform the analysis method of the board operating environment.
In another exemplary embodiment, a computer readable storage medium including program instructions is further provided, and the program instructions, when executed by a processor, implement the steps of the analysis method of the board execution environment described above. For example, the computer readable storage medium may be the memory 802 including the program instructions, which are executable by the processor 801 of the board execution environment analysis apparatus 800 to perform the board execution environment analysis method.
Example 4
Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment, and a readable storage medium described below and an analysis method of a board operating environment described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the analysis method for the board card operating environment of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The present invention has been described in terms of the preferred embodiment, and it is not intended to be limited to the embodiment. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for analyzing a board running environment is characterized by comprising the following steps:
acquiring at least one piece of first information, wherein the first information comprises operation data information of a board card and environment data information of the board card when the operation data is acquired; the operation data information comprises at least two; the environment data information comprises magnetic field intensity information, humidity information and temperature information around the board card when in operation, and the operation data information of the board card comprises sampling speed information, using channel number information, resolution ratio information and acquisition precision information of the board card;
preprocessing the operation data information to obtain preprocessed operation data information;
processing the preprocessed running data information and the environment data information based on a grey correlation analysis method, converting the correlation degree obtained by processing into a weight value, and then performing product calculation on the weight value and the environment data information to obtain a running score value of each preprocessed running data information;
processing all the operation score values according to a least square linear fitting method to obtain second information, wherein the second information is an operation score fitting value of each time slot board card under each environmental data and a change rate of the operation score fitting value;
and performing product operation on the operation score fitting value under each environmental data and the change rate of the operation score fitting value according to a preset weight proportion, and taking the environmental data corresponding to the highest score in the operation result as the optimal operation environment of the board card.
2. The method for analyzing the board card operating environment according to claim 1, wherein the step of preprocessing the operating data information to obtain the preprocessed operating data information comprises:
performing data analysis on all the running data information to obtain incomplete information data and complete information data in the running data information, and performing clustering processing on the incomplete information data and the complete information data respectively by adopting an EM (effective electromagnetic tomography) algorithm to obtain a clustering result of the incomplete information data and a clustering result of the complete information data;
and calculating an average value based on the clustering result of the data with complete information, and carrying out interpolation filling on the clustering result of the data with incomplete information by using the average value to obtain the preprocessed running data information.
3. The method for analyzing the board card operating environment according to claim 1, wherein the processing the preprocessed operating data information and the environment data information based on a gray correlation analysis method, converting the processed correlation degree into a weight value, and then performing a product calculation of the weight value and the environment data information to obtain the operating score value of each preprocessed operating data information includes:
carrying out non-dimensionalization processing on each piece of the preprocessed running data information and each piece of the environment data information to obtain the non-dimensionalized running data information and the non-dimensionalized environment data information;
calculating a correlation coefficient of the non-dimensionalized running data information and the environment data information based on the non-dimensionalized running data information and the environment data information, and calculating a correlation degree of each running data information and each environment data information based on the correlation coefficient;
and converting the relevance into a scoring weight of the running data information under the environment data corresponding to the relevance, performing product calculation on the scoring weight and the board running data under the environment data corresponding to the relevance, and taking the result of the product calculation as the running scoring value of each running data information.
4. The method for analyzing the board card operating environment according to claim 1, wherein all the operating score values are processed according to a least square linear fitting method to obtain second information, including:
all the operation score values are sent to a preset space rectangular coordinate system and converted into a first curve graph, each operation score value is fitted in the first curve graph based on a least square linear fitting method to obtain a first fitting curve graph, and the board card operation score fitting value under each environment data is determined;
and performing segmentation processing on the board running score fitting values under all the environmental data according to the same time sequence, and performing statistical processing on the board running score fitting values of each time period under each environmental data obtained through the segmentation processing to obtain the running score fitting value of each time period board under each environmental data and the change rate of the running score fitting value.
5. An analysis device for a board running environment, comprising:
the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring at least one piece of first information, and the first information comprises operation data information of a board card and environment data information of the board card when the operation data is acquired; the operation data information comprises at least two; the environment data information comprises magnetic field intensity information, humidity information and temperature information around the board card when in operation, and the operation data information of the board card comprises sampling speed information, using channel number information, resolution ratio information and acquisition precision information of the board card;
the first processing unit is used for preprocessing the running data information to obtain preprocessed running data information;
the second processing unit is used for processing the preprocessed running data information and the environment data information based on a grey correlation analysis method, converting the correlation degree obtained by processing into a weight value, and then performing product calculation on the weight value and the environment data information to obtain a running score value of each preprocessed running data information;
the third processing unit is used for processing all the operation score values according to a least square linear fitting method to obtain second information, wherein the second information is the operation score fitting value of each time slot board under each environmental data and the change rate of the operation score fitting value;
and the fourth processing unit is used for performing product operation on the operation score fitting value under each environmental data and the change rate of the operation score fitting value according to a preset weight proportion, and taking the environmental data corresponding to the highest score in the operation result as the optimal operation environment of the board card.
6. The apparatus of claim 5, wherein the first processing unit comprises:
the first clustering subunit is used for performing data analysis on all the running data information to obtain incomplete information data and complete information data in the running data information, and performing clustering processing on the incomplete information data and the complete information data respectively by adopting an EM (effective man algorithm) algorithm to obtain a clustering result of the incomplete information data and a clustering result of the complete information data;
and the first processing subunit is used for calculating an average value based on the clustering result of the data with complete information, and carrying out interpolation filling on the clustering result of the data with incomplete information by using the average value to obtain the preprocessed running data information.
7. The apparatus of claim 5, wherein the second processing unit comprises:
the second processing subunit is configured to perform non-dimensionalization processing on each piece of the preprocessed running data information and each piece of the environment data information to obtain non-dimensionalized running data information and non-dimensionalized environment data information;
the first calculating subunit is configured to calculate a correlation coefficient between the non-dimensionalized running data information and the environment data information based on the non-dimensionalized running data information and the environment data information, and calculate a correlation degree between each piece of running data information and each piece of environment data information based on the correlation coefficient;
and the third processing subunit is used for converting the relevance into a scoring weight of the running data information under the environment data corresponding to the relevance, performing product calculation on the scoring weight and the board running data under the environment data corresponding to the relevance, and taking the result of the product calculation as the running scoring value of each running data information.
8. The apparatus of claim 5, wherein the third processing unit comprises:
the fourth processing subunit is used for sending all the operation score values to a preset space rectangular coordinate system to be converted into a first curve graph, fitting each operation score value in the first curve graph based on a least square linear fitting method to obtain a first fitting curve graph, and determining the board card operation score fitting value under each environmental data;
and the fifth processing subunit is configured to perform segmentation processing on the board operation score fitting values under all the environmental data according to the same time sequence, perform statistical processing on the board operation score fitting values of each time period under each environmental data obtained through the segmentation processing, and obtain the operation score fitting value of each time period board under each environmental data and the change rate of the operation score fitting value.
9. An analysis device for a board operating environment, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method for analyzing the board operating environment according to any one of claims 1 to 4 when executing the computer program.
10. A readable storage medium, characterized by: the readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for analyzing the board operating environment according to any of claims 1 to 4.
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