Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide a data collection method and system for an equipment service management platform, which solve the problems set forth in the above-mentioned background art by applying different product inspection methods.
In order to achieve the above object, the present invention provides a data acquisition method for an equipment service management platform, including:
s1, according to equipment updating log database, collecting data platform characteristic information and data service characteristic information, and performing data processing to obtain data calling frequency, data fluctuation value, data valid period duration and data area coverage value;
S2, acquiring data calling frequency, data fluctuation value, data valid period duration and data area coverage value, establishing a data analysis model, and performing logistic regression calculation to obtain screening evaluation coefficients;
S3, acquiring a screening evaluation coefficient, comparing the screening evaluation coefficient with a preset screening threshold value, obtaining equipment data paragraphs with priority screening according to equipment data paragraph results marked as priority paragraphs in the comparison result, and determining data screening paragraphs according to the data similarity of each piece of equipment data with priority screening;
S4, acquiring a data screening paragraph, and acquiring the real-time average uploading speed of a frequency user side and the data distribution frequency of the data screening paragraph in different areas according to the data screening paragraph;
And S5, determining a data screening paragraph calling sequencing result by using fuzzy logic according to the real-time average uploading speed of the data screening paragraph calling data user side and the data distribution frequency of the data screening paragraphs in different areas.
In a preferred embodiment, the data platform characteristic information comprises data calling frequency and data fluctuation value, and the data service characteristic information comprises data valid period duration and data area coverage value;
Recording events of each data call by determining the analyzed unit time, counting the total number of data call occurrences in the set unit time, and calculating the ratio of the total number of data call occurrences in the unit time to the unit time length to obtain the data call frequencyWherein i is the ith unit time, g is the g data tag;
acquiring operation parameter time sequence data of different data labels from equipment operation data records, and calculating standard deviation of operation parameter values relative to mean values of the operation parameter time sequence data to obtain data fluctuation values;
Acquiring the valid period ending time and the current time of the data in the data information database, and subtracting the current time from the valid period ending time of the data to obtain the valid period duration of the data;
Calculating by providing the longitude and latitude coordinates of the data of each data tag point to obtain the geographic position distribution coordinate area of the data tag, and calculating the ratio of the geographic position distribution coordinate area to the coverage area in the time range to obtain the coverage value of the data area。
In a preferred embodiment, the data call frequency, the data fluctuation value, the data validity period duration, and the data area coverage value are substituted into the logistic regression calculation specific formula as follows:
;
In the formula,For the logistic regression calculation result, i.e. screening the evaluation coefficient, e is a natural base, y is a linear combination term of the logistic regression model, and specifically y can be set as follows:
;
In the formula,As a result of the bias term,、、AndRegression coefficients of the data call frequency, the data fluctuation value, the data validity period duration and the data area coverage value are respectively obtained.
In a preferred embodiment, after the screening evaluation coefficients are obtained, the screening evaluation coefficients are compared with a continuously iterated screening threshold value for analysis;
if the screening evaluation coefficient is greater than or equal to the screening threshold value, marking the current equipment data paragraph as a priority paragraph, and generating a screening signal;
If the screening evaluation coefficient is smaller than the screening threshold, marking the current equipment data paragraph as a screened paragraph, and generating an ending signal.
In a preferred embodiment, the current device data paragraph marked as a priority paragraph is marked as a priority screened device data paragraph;
and carrying out vectorization representation on three dimensions of numerical values, distribution and structures in the preferentially screened equipment data paragraphs, and calculating to obtain numerical value similarity, time sequence similarity and feature vector similarity through a cosine similarity formula.
In a preferred embodiment, substituting the numerical similarity, the time sequence similarity and the feature vector similarity into a weighted formula to calculate the data similarity of each device data segment which is preferentially screened;
Comparing the data similarity of the preferentially screened equipment data paragraphs with a preset similarity threshold, screening out the equipment data paragraphs corresponding to the comparison similarity if the data similarity of the preferentially screened equipment data paragraphs is larger than the similarity threshold, otherwise, reserving the equipment data paragraphs corresponding to the comparison similarity until the comparison is completed;
and collecting the equipment data paragraphs reserved by the comparison result to obtain data screening paragraphs.
In a preferred embodiment, the data filtering section comprises a plurality of data tags, and the data tags comprise a plurality of data;
The method comprises the steps of accumulating the real-time uploading speeds of all calling users by acquiring the number of calling users in a current data screening paragraph, and calculating the ratio of the accumulated real-time uploading speeds to the number of calling users in the current data screening paragraph to obtain the real-time average uploading speed of the calling data user side of the data screening paragraph;
and extracting geographic position data corresponding to the data screening paragraphs from the records of the data source, calculating the ratio of the data quantity corresponding to different positions to the total data quantity of the data screening paragraphs, and counting the data distribution frequency in each region to accumulate so as to obtain the data distribution frequency of the data screening paragraphs in different regions.
In a preferred embodiment, the data filtering section calling data user side real-time average uploading speed and the data distribution frequency of the data filtering section in different areas are respectively divided into different fuzzy sets;
defining the data screening paragraph calling sequencing result as an output variable, and dividing the output variable into fuzzy sets;
Formulating a fuzzy rule, and describing the influence of the data screening paragraph calling data user end on the real-time average uploading speed and the data distribution frequency of the data screening paragraphs in different areas on the data screening paragraph calling sequencing result;
and carrying out fuzzy reasoning according to the fuzzy rule, and determining a data screening paragraph calling sequencing result.
The data acquisition system facing to the equipment service management platform comprises a data acquisition module, a data processing module, a screening analysis module and a paragraph ordering module;
The data acquisition module is used for updating the log database according to the equipment, acquiring the characteristic information of the data platform and the characteristic information of the data service, performing data processing to obtain data calling frequency, a data fluctuation value, a data valid period duration and a data area coverage value, and sending the data calling frequency, the data fluctuation value, the data valid period duration and the data area coverage value to the data processing module;
The data processing module is used for acquiring data calling frequency, data fluctuation value, data valid period duration and data area coverage range value, establishing a data analysis model, performing logistic regression calculation to obtain screening evaluation coefficients, and sending the screening evaluation coefficients to the screening analysis module;
The screening analysis module is used for acquiring a screening evaluation coefficient, comparing the screening evaluation coefficient with a preset screening threshold value, obtaining equipment data paragraphs with priority screening according to equipment data paragraph results marked as priority paragraphs in the comparison result, determining data screening paragraphs according to data similarity of each piece of equipment data with priority screening, and sending the data screening paragraphs to the paragraph sorting module;
the paragraph sorting module is used for acquiring data screening paragraphs, acquiring real-time average uploading speed of a data screening paragraph calling frequency user side and data distribution frequency of the data screening paragraphs in different areas according to the data screening paragraphs, and determining a data screening paragraph calling sorting result by using fuzzy logic.
The invention has the technical effects and advantages that:
1. According to the method, a data analysis model is built through collecting data calling frequency, data fluctuation values, data valid period duration and data area coverage values, logistic regression calculation is conducted to obtain screening evaluation coefficients, the screening evaluation coefficients are compared with preset screening thresholds, equipment data paragraphs which are screened preferentially are obtained according to comparison results, then the data screening paragraphs are determined according to the data similarity of the equipment data paragraphs which are screened preferentially, multidimensional comprehensive analysis is conducted, screening accuracy is improved, data collection efficiency is improved, and waste of calculation resources is avoided.
2. According to the method, a group of fuzzy rules are formulated for fuzzy reasoning according to the data screening paragraphs, the data average uploading speed of the data screening paragraph calling frequency user side and the data distribution frequency of the data screening paragraphs in different areas, the data screening paragraph calling sequencing result is determined, the calculation pressure of subsequent data analysis is reduced, the service efficiency is improved, and the user experience is enhanced.
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.
According to the method, a log database is updated according to equipment, equipment data characteristic information and equipment service characteristic information are collected, analysis is carried out on the data quality and service period characteristics, logistic regression calculation is carried out through an analysis data analysis model, screening evaluation coefficients are obtained through output, data screening paragraphs are determined through the screening evaluation coefficients, and data collection results are determined through a fuzzy Bayesian model according to data importance scores and data selection frequencies in the data screening paragraphs;
The equipment service period belongs to a quality guarantee period after a user owns equipment, in the quality guarantee period, when a customer scans a code and needs to upload service required by the customer, data acquisition is carried out, and acquired data needs to be analyzed in advance from a database;
Example 1
Referring to fig. 1, a data collection method for an equipment service management platform includes the following specific operation procedures:
s1, according to equipment updating log database, collecting data platform characteristic information and data service characteristic information, and performing data processing to obtain data calling frequency, data fluctuation value, data valid period duration and data area coverage value;
The data processing comprises data type conversion, missing value processing, data standardization and feature extraction operation;
The data type conversion refers to converting different types of data (such as character strings, dates, values and the like) according to the source format and target application requirements of the data, for example, converting a device log or a data record to floating point type or integer type data by storing the values in the form of the character strings;
The missing value processing is used for solving the problem of null value caused by equipment faults, network transmission problems or incomplete recording and other reasons in equipment logs or data records, and the missing value is processed by a deletion method or a mean filling method preferentially;
Data standardization is carried out on all acquired data according to a unified scale, and data calling frequency, data fluctuation value, data valid period duration and data area coverage range value are subjected to standardization;
specifically, the above data operation methods are all in the prior art, and are not described herein in detail;
The data platform characteristic information comprises data calling frequency and a data fluctuation value, and the data service characteristic information comprises data valid period duration and a data area coverage value;
The data calling frequency refers to the number of times of data calling in a set unit time, and measures the response capability, stability and efficiency of data service, the acquisition logic is used for recording the event of each data calling by determining the analyzed unit time, counting the total number of times of data calling in the set unit time, and calculating the ratio of the total number of times of data calling in the unit time to the length of the unit time to obtain the data calling frequencyWherein i is the ith unit time, g is the g data tag;
It should be noted that, the setting of the unit time may be the lengths of "24 hours", "36 hours" and "72 hours", and the specific length setting is determined by the present experimenter according to the historical data calling frequency and the historical service period length, which is not described herein in detail;
it should be noted that, the data tag refers to that the platform performs classification processing on all data in the database in advance, divides a plurality of groups, and attaches a corresponding data tag to the groups, and the specific format of the data tag is not limited;
The data fluctuation value refers to the change amplitude of the operation parameters (such as temperature, pressure, current and the like) of the equipment in unit time and is used for evaluating the data screenability of the equipment, the acquisition logic acquires the operation parameter time sequence data of different data labels from the equipment operation data record, and the data fluctuation value is obtained by calculating the standard deviation of the operation parameter value relative to the average value;
The device service-oriented management platform preferentially divides the data into a plurality of tags and marks the tags as g, namely g is a g-th data tag, the specific division rule can be based on the classification of the experimenter according to the original data or the similarity among the data, the specific classification method is not limited, but the experimenter is obtained according to the specific implementation mode and is not repeated herein;
specifically, the above operation parameters are not limited, and may be temperature, pressure, current, etc., and in this embodiment, only the operation parameter that can be most represented is selected, for example, for a refrigeration device, a fluctuation value of refrigeration efficiency data of the refrigeration device is obtained, for a heating device, a fluctuation value of a heating indication temperature of the heating device is obtained, etc., and the selection of a specific operation parameter is set by the present experimenter according to a specific device application scenario and a device operation feature, which is not described herein in detail;
wherein, the formula for calculating the standard deviation of the operation parameter value relative to the mean value thereof is expressed as follows:
;
In the formula,Expressing the standard deviation of the g-th data in the ith unit time, namely the data fluctuation value, P is the total number of data tags,As the operating parameter value for the class g data,Is the mean value of the operating parameters;
The data validity period length refers to the time left from the current moment to the end of the data validity period, is an important parameter for measuring the state of the current life cycle of the data and is used for predicting the filterability and the validity period length of the data, and the acquisition logic acquires the validity period end time and the current time of the data from the data information database, and subtracts the current time from the validity period end time of the data to obtain the data validity period length;
Specifically, the data valid period duration selection system defaults to compare the valid period end time of the data with the current time, if the valid period end time of the data is larger than the current time, the current data is indicated to exist in the valid period, otherwise, the valid period end time of the data is indicated, and the data is deleted;
The data area coverage value refers to the data of different data tags, and is generally used for measuring the availability and coverage degree of a data platform at different geographic positions in a corresponding geographic area coverage within a unit time, and the acquisition logic calculates by providing the longitude and latitude coordinates of the data of each data tag point to obtain the geographic position distribution coordinate area of the data tag, and calculates the ratio of the geographic position distribution coordinate area to the coverage area within the time range to obtain the data area coverage value;
The data longitude and latitude coordinates of each data tag point refer to a specific geographic position associated with each data tag, so as to obtain corresponding longitude and latitude points, the area of the data coverage area is calculated by calculating the minimum rectangular boundary of the points, the range of the boundary frame is determined by the maximum longitude and latitude value and the minimum longitude and latitude value, a rectangular area is formed, and the specific area calculation can be calculated by using a spherical triangle formula and the like and is not described herein;
S2, acquiring data calling frequency, data fluctuation value, data valid period duration and data area coverage value, establishing a data analysis model, and performing logistic regression calculation to obtain screening evaluation coefficients;
the data analysis model refers to a logistic regression calculation model, and screening evaluation coefficients are generated through logistic regression calculation;
substituting the data calling frequency, the data fluctuation value, the data valid period duration and the data area coverage range value into a logistic regression calculation specific formula to express as follows:
;
In the formula,For the logistic regression calculation result, i.e. screening the evaluation coefficient, e is a natural base, y is a linear combination term of the logistic regression model, and specifically y can be set as follows:
;
In the formula,As a result of the bias term,、、AndRegression coefficients of the data calling frequency, the data fluctuation value, the data valid period duration and the data area coverage value are respectively obtained;
the data calling frequency, the data fluctuation value, the data valid period duration and the data area coverage range value are all data embodiments for directly expressing the screening selection of the current equipment data paragraph;
The formula shows that when the data calling frequency, the data fluctuation value and the coverage range value of the data area are higher, the current equipment data section is more valuable in aspects of desirability, dynamic property, applicability and the like, the screening evaluation coefficient is required to be preferentially screened, otherwise, the time length of the data validity period is longer, the timeliness of the current equipment data section is higher, the priority is reduced, and the screening evaluation coefficient is lower;
S3, acquiring a screening evaluation coefficient, comparing the screening evaluation coefficient with a preset screening threshold value, obtaining equipment data paragraphs with priority screening according to equipment data paragraph results marked as priority paragraphs in the comparison result, and determining data screening paragraphs according to the data similarity of each piece of equipment data with priority screening;
The acquisition logic of the screening threshold value is that the data set is divided into a training set and a test set by collecting the priority classification set of the historical equipment data paragraph, an evaluation index and a clustering algorithm are set, in each iteration of cross verification, a model is trained on the training set, the performance of the model is evaluated on the test set, and then the screening threshold value is adjusted according to the performance of the verification set, so that the screening threshold value is continuously and iteratively updated;
In the invention, a clustering algorithm is an unsupervised learning algorithm and is used for classifying the priorities of the equipment data paragraphs in a data set into groups or clusters with labeling property, and a common K-means clustering is used for classifying weighing data in the data set into K clusters so as to minimize the distance between each equipment data paragraph priority and the center point (centroid) of the cluster to which each equipment data paragraph belongs, and finally, the priorities of the equipment data paragraph distribution are measured through Euclidean distance, thereby setting a screening threshold value;
After the screening evaluation coefficient is obtained, the screening evaluation coefficient is compared with a continuously iterated screening threshold value for analysis;
if the screening evaluation coefficient is greater than or equal to the screening threshold value, marking the current equipment data paragraph as a priority paragraph, and generating a screening signal;
if the screening evaluation coefficient is smaller than the screening threshold value, marking the current equipment data paragraph as a screened paragraph, and generating an ending signal;
marking the current device data paragraph marked as the priority paragraph as the device data paragraph of the priority screening;
Vectorizing the three dimensions of the numerical value, distribution and structure in the preferentially screened equipment data paragraph, and calculating to obtain numerical value similarity, time sequence similarity and feature vector similarity through a cosine similarity formula;
Specifically, in this embodiment, the numerical similarity, the time sequence similarity and the feature vector similarity are analyzed by three dimensions, and in fact, the experimenter may set denser data similarity according to practical application to more accurately express the data similarity of each device data segment preferentially screened, perform operations of improving screening precision, and so on, which are not described herein in detail;
it should be noted that, when calculating the similarity formula, the present example calculates the cosine similarity, however, in practical application, the euclidean distance may also be used to determine the numerical similarity, etc., and the time sequence similarity may also be determined according to a dynamic time warping method, etc., where the method for calculating the similarity formula is not limited, but is set according to a calculation model preset by the experimenter, and will not be described herein;
Substituting the numerical similarity, the time sequence similarity and the feature vector similarity into a weighted formula to calculate and obtain the data similarity of each preferentially screened equipment data segment;
Comparing the similarity of the preferentially screened equipment data paragraph data with a preset similarity threshold value, screening out the equipment data paragraph corresponding to the comparison similarity if the similarity of the preferentially screened equipment data paragraph data is larger than the similarity threshold value, otherwise, reserving the equipment data paragraph corresponding to the comparison similarity until the comparison is completed;
Collecting the equipment data paragraphs reserved by the comparison result to obtain data screening paragraphs;
According to the method, a data analysis model is built through collecting data calling frequency, data fluctuation values, data valid period duration and data area coverage values, logistic regression calculation is conducted to obtain screening evaluation coefficients, the screening evaluation coefficients are compared with preset screening thresholds, equipment data paragraphs which are screened preferentially are obtained according to comparison results, then the data screening paragraphs are determined according to the data similarity of the equipment data paragraphs which are screened preferentially, multidimensional comprehensive analysis is conducted, screening accuracy is improved, data collection efficiency is improved, and waste of calculation resources is avoided.
Example 2
In the embodiment 1 of the invention, the data collection frequency, the data fluctuation value, the data valid period duration and the coverage range value of the data area are mainly illustrated, a data analysis model is established, the logistic regression calculation is carried out to obtain screening evaluation coefficients, the screening evaluation coefficients are compared with preset screening thresholds, the equipment data paragraphs which are preferentially screened are obtained according to the comparison results, and then the operation strategy of the data screening paragraphs is determined according to the data similarity of the equipment data paragraphs which are preferentially screened;
S4, acquiring a data screening paragraph, and acquiring the real-time average uploading speed of a frequency user side and the data distribution frequency of the data screening paragraph in different areas according to the data screening paragraph;
Specifically, the data filtering section includes a plurality of data tags, and the data tags include a plurality of data;
The acquisition logic of the real-time average uploading speed of the data screening paragraph call data user end is used for accumulating the real-time uploading speeds of all the called users by acquiring the number of the call users in the current data screening paragraph and calculating the ratio of the real-time uploading speeds to the number of the call users in the current data screening paragraph to obtain the real-time average uploading speed of the data screening paragraph call data user end;
The method comprises the steps that when a user performs code scanning or other interactive operations, a client application monitors uploading speed in real time, specifically, uploading speed is collected periodically in the process of the client realizing an uploading module, for example, uploading amount and time consumption are recorded once per second, the real-time uploading speed is stored in a local cache, and the real-time collection mode is not limited and is not repeated herein;
the data distribution frequency of the data screening section in different areas reflects the activity of the data screening section in the geographic position, the acquisition logic extracts geographic position data corresponding to the data screening section from the record of the data source, calculates the ratio of the data quantity corresponding to the different positions to the total data quantity of the data screening section, and counts the data distribution frequency in each area to accumulate so as to obtain the data distribution frequency of the data screening section in different areas;
It should be noted that, for the selection and the number of the geographic locations, the geographic locations with a large number of users or high activity may be preferentially selected by analyzing the activity of the users at each geographic location, which is not described herein;
S5, determining a data screening paragraph calling sequencing result by using fuzzy logic according to the real-time average uploading speed of the data screening paragraph calling data user side and the data distribution frequency of the data screening paragraphs in different areas;
For example, "Fast", "Slow", "Moderate" call the real-time average uploading speed of the data user side for the data screening paragraph, and "High", "Low", "Medium" frequently distribute data in different areas for the data screening paragraph;
A set of fuzzy rules is formulated to describe the influence of different input variables on the output variables. The definition of rules may be based on expertise or may be obtained through data analysis and experimentation. For example:
Marking the real-time average uploading speed of a data screening paragraph call data user end as X, marking the data distribution frequency degree of the data screening paragraph in different areas as U, and marking the data screening paragraph call sequencing result as C_results;
Then it is possible to define:
Rule 1: IF (X is Fast) AND (U is High) THEN (C_results is High)
Rule 2: IF (U is Slow) AND (U is Low) THEN (C_results is Low)
...
performing fuzzy reasoning according to the fuzzy rule, and determining a data screening paragraph calling sequencing result;
It should be noted that, the division of the fuzzy set may be adjusted according to the actual situation, for example, although the embodiment uses three fuzzy sets as examples, the real-time average uploading speed of the data user end for invoking the data screening paragraph and the data distribution frequency of the data screening paragraph in different areas may be actually divided into more than three sets, so as to facilitate better accurate adjustment according to different data labels;
Further, for the judgment of the real-time average uploading speed of the data user side of the data screening paragraph call and the data distribution frequency degree of the data screening paragraph in different areas, the judgment can be performed according to the actual situation, for example, when the real-time average uploading speed of the data user side of the data screening paragraph call exceeds 75%, the data user side is marked as Fast, and when the data distribution frequency degree of the data screening paragraph in different areas is higher than 70%, the data screening paragraph is marked as High, and the like, which are not described herein;
According to the method, a group of fuzzy rules are formulated for fuzzy reasoning according to the data screening paragraphs, the data average uploading speed of the data screening paragraph calling frequency user side and the data distribution frequency of the data screening paragraphs in different areas, the data screening paragraph calling sequencing result is determined, the calculation pressure of subsequent data analysis is reduced, the service efficiency is improved, and the user experience is enhanced.
Example 3
Referring to fig. 2, a data acquisition system facing to an equipment service management platform includes a data acquisition module, a data processing module, a screening analysis module and a paragraph ordering module;
The data acquisition module is used for updating the log database according to the equipment, acquiring the characteristic information of the data platform and the characteristic information of the data service, performing data processing to obtain data calling frequency, a data fluctuation value, a data valid period duration and a data area coverage value, and sending the data calling frequency, the data fluctuation value, the data valid period duration and the data area coverage value to the data processing module;
The data processing module is used for acquiring data calling frequency, data fluctuation value, data valid period duration and data area coverage range value, establishing a data analysis model, performing logistic regression calculation to obtain screening evaluation coefficients, and sending the screening evaluation coefficients to the screening analysis module;
The screening analysis module is used for acquiring a screening evaluation coefficient, comparing the screening evaluation coefficient with a preset screening threshold value, obtaining equipment data paragraphs with priority screening according to equipment data paragraph results marked as priority paragraphs in the comparison result, determining data screening paragraphs according to data similarity of each piece of equipment data with priority screening, and sending the data screening paragraphs to the paragraph sorting module;
the paragraph sorting module is used for acquiring data screening paragraphs, acquiring real-time average uploading speed of a data screening paragraph calling frequency user side and data distribution frequency of the data screening paragraphs in different areas according to the data screening paragraphs, and determining a data screening paragraph calling sorting result by using fuzzy logic.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.