CN115328722A - Data analysis method and device, storage medium and terminal - Google Patents
Data analysis method and device, storage medium and terminal Download PDFInfo
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- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
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- G06F8/00—Arrangements for software engineering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
The embodiment of the application discloses a data analysis method, a data analysis device, a storage medium and a terminal, and belongs to the technical field of computers. The method comprises the following steps: the method comprises the steps that a terminal scans at least one piece of data to be scanned to obtain a data set to be analyzed, the data to be analyzed in the data set to be analyzed are sequentially analyzed based on a preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, accurate analysis processing of the data is achieved by means of applying the preset rule successively to the data with the conditions for processing by applying the preset rule, accurate analysis results are obtained, and data analysis efficiency is greatly improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a data analysis method, an apparatus, a storage medium, and a terminal.
Background
With the development of information technology, the functions of various electronic devices/terminals are increasingly intelligent, and more functions can be realized. When electronic devices/terminals execute various tasks based on various functions, various data processing is involved, and a large amount of corresponding business data is generated, wherein the business data usually contains a large amount of effective information, and analysis of the business data becomes an important link in data processing. In the related art, the data volume of the related service data is large, and an intricate relationship exists among a plurality of service data, so that a plurality of important data are easily omitted when the service data are analyzed, and the analysis result is not accurate.
Disclosure of Invention
The embodiment of the application provides a data analysis method, a data analysis device, a storage medium and a terminal, and can solve the problem that an analysis result obtained when business data is analyzed in the related art is not accurate. The technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for analyzing data, where the method includes:
scanning at least one piece of data to be scanned to obtain a data set to be analyzed; the data set to be analyzed comprises at least one piece of data to be analyzed, and the data to be analyzed is data with a condition for processing by applying a preset rule;
analyzing the at least one piece of data to be analyzed in the data set to be analyzed in sequence based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data; and the target data is the data to be analyzed which accords with the preset rule in the data set to be analyzed.
Optionally, before the scanning at least one piece of data to be scanned to obtain a data set to be analyzed, the method further includes:
and acquiring the at least one piece of data to be scanned.
Optionally, the sequentially analyzing the at least one piece of data to be analyzed in the data set to be analyzed based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data includes:
sequentially judging whether the at least one piece of data to be analyzed in the data set to be analyzed meets the condition in the preset rule;
if so, taking at least one piece of data to be analyzed meeting the conditions in the preset rule as the at least one piece of target data, and obtaining conclusion information corresponding to the at least one piece of target data based on the preset rule.
Optionally, after obtaining conclusion information corresponding to each of the at least one piece of target data based on the preset rule, the method further includes:
and performing association processing on the at least one piece of target data and the corresponding conclusion information thereof, and generating a reminding message of successful analysis.
Optionally, after sequentially determining whether the at least one piece of data to be analyzed in the data set to be analyzed meets the condition in the preset rule, the method further includes:
if not, generating a reminding message of analysis failure.
Optionally, after the analyzing the at least one piece of data to be analyzed in the data set to be analyzed in sequence based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, the method further includes:
analyzing the conclusion information to obtain approval data;
and executing online processing operation based on the approval data.
Optionally, before the analyzing the at least one piece of data to be analyzed in the data set to be analyzed in sequence based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, the method further includes:
receiving user operation for modifying the preset rule;
generating a modification instruction in response to the user operation;
and modifying the preset rule according to the modification instruction.
In a second aspect, an embodiment of the present application provides an apparatus for analyzing data, where the apparatus includes:
the scanning module is used for scanning at least one piece of data to be scanned to obtain a data set to be analyzed; the data set to be analyzed comprises at least one piece of data to be analyzed, and the data to be analyzed is data with a condition for processing by applying a preset rule;
the analysis module is used for sequentially analyzing the at least one piece of data to be analyzed in the data set to be analyzed based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data; and the target data is the data to be analyzed which accords with the preset rule in the data set to be analyzed.
Optionally, the apparatus further comprises:
and the acquisition module is used for acquiring the at least one piece of data to be scanned.
Optionally, the analysis module comprises:
a judging unit, configured to sequentially judge whether the at least one piece of data to be analyzed in the data set to be analyzed meets a condition in the preset rule;
and if so, taking at least one piece of data to be analyzed meeting the condition in the preset rule as the at least one piece of target data, and obtaining conclusion information corresponding to the at least one piece of target data based on the preset rule.
Optionally, the analysis module further comprises:
and the second processing unit is used for performing correlation processing on the at least one piece of target data and the corresponding conclusion information thereof and generating a reminding message of successful analysis.
Optionally, the analysis module further comprises:
and the third processing unit is used for generating a reminding message of analysis failure if the result is negative.
Optionally, the apparatus further comprises:
the first processing module is used for analyzing the conclusion information to obtain approval data;
and the second processing module is used for executing online processing operation based on the approval data.
Optionally, the apparatus further comprises:
the receiving module is used for receiving user operation for modifying the preset rule;
the generating module is used for responding to the user operation and generating a modification instruction;
and the modification module is used for modifying the preset rule according to the modification instruction.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor, a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The beneficial effects brought by the technical scheme provided by some embodiments of the application at least comprise:
when the scheme of the embodiment of the application is executed, the terminal scans at least one piece of data to be scanned to obtain a data set to be analyzed, and analyzes at least one piece of data to be analyzed in the data set to be analyzed in sequence based on a preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data. The data with the conditions for processing by applying the preset rules are subjected to a mode of successively applying the preset rules, so that the data are accurately analyzed and processed, an accurate analysis result is obtained, and the data analysis efficiency is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for analyzing data provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of a data analysis process provided by an embodiment of the present application;
FIG. 3 is another schematic flow chart diagram of a method for analyzing data provided by an embodiment of the present application;
FIG. 4 is a schematic structural diagram of an apparatus provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of an apparatus according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the claims that follow.
In the description of the present application, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The method and the device can be specifically applied to various scenes in which data analysis needs to be performed on complex problems, including but not limited to application scenes such as application program upgrading, system updating and system testing in a terminal, for example: and 360, upgrading the guard version. In this application, a terminal (i.e., a terminal device) includes but is not limited to: electronic equipment such as personal computers, tablet computers, mobile phones, handheld devices, vehicle-mounted devices, wearable devices, computing devices and the like. In different application scenarios, the terminal device may be called different names, for example: user equipment, access terminal, remote terminal, mobile device, user terminal, wireless communication device or user equipment, cellular handset, personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, etc.
In the following method embodiments, for convenience of description, only the execution subject of each step is taken as a terminal for description.
The following describes the data analysis method provided in the embodiments of the present application in detail with reference to fig. 1 to 3.
Referring to fig. 1, a schematic flow chart of a data analysis method is provided in an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the steps of:
s101, scanning at least one piece of data to be scanned to obtain a data set to be analyzed.
The data to be scanned refers to various service data that can be obtained by the terminal, and the data to be scanned may be service data that is generated by the terminal based on various service functions inside the terminal, such as: the service data generated when the user browses news by using the browser in the terminal may also be service data generated and sent to the terminal by other terminals/electronic devices except the terminal based on respective service functions or different requirements, such as: and the service data generated by other electronic equipment is utilized by the staff for upgrading the application program version in the terminal. The data set to be analyzed includes at least one piece of data to be analyzed, and the data to be analyzed is data having a condition for processing by applying a preset rule, such as: the data to be scanned is related service data received by the mobile phone and used for 360 guard upgrading, and the data to be analyzed obtained after preliminary scanning processing of the mobile phone is the service data meeting the conditions of the preset rules applied by the mobile phone operation system.
Specifically, the terminal may obtain at least one piece of data to be scanned, where an initial state of the data to be scanned may be complex and messy, and may not directly apply an analysis rule preset by a user, so that the data to be scanned needs to be scanned, and the data to be scanned is not logically analyzed when the data to be scanned is scanned, that is, compared with the data to be scanned, a data amount of the data to be analyzed is the same as a data amount of the data to be scanned, and a specific data content in the data to be scanned is unchanged in a process of the data to be analyzed obtained after the data to be scanned is scanned. And at least one piece of data to be analyzed obtained after scanning processing has the condition of processing by applying the preset rule, namely the data after scanning processing can be directly analyzed by applying the preset rule, so that the efficiency of data analysis can be greatly improved.
S102, sequentially analyzing at least one piece of data to be analyzed in the data set to be analyzed based on a preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data.
The preset rule refers to a preset rule for analyzing data, and the preset rule can set different rules according to different data processing purposes and is a rule created manually in advance. In the process of analyzing the data, the user can also perform modification operations such as manual addition/deletion on the preset rules according to the actual analysis result. When the preset rule is changed, the terminal can perform related analysis processing on the data based on the new preset rule. The preset rules may include analysis conditions (e.g., conditions such as features to be satisfied, features to be ignored, and features to be eliminated once), corresponding conclusion information when the analysis conditions are satisfied, and data such as a method for analyzing data. The data satisfying the analysis condition in the preset rule may correspond to the conclusion information in the preset rule. There may be one or more pieces of data that meet the preset rule. The target data is data to be analyzed which accords with a preset rule in a data set to be analyzed, the target data is the data which accords with the preset rule, and a conclusion corresponding to the target data is conclusion information in the preset rule. The conclusion information is conclusion file data set in a preset rule, and unique conclusion information is set when the rule is set/created in advance.
Specifically, the terminal sequentially analyzes whether each piece of data to be analyzed in the data set to be analyzed meets the analysis condition in the preset rule based on the preset rule. Analyzing one piece of data to be analyzed in the data set to be analyzed, and when the data to be analyzed is determined to meet the analysis condition in the preset rule, determining that the data to be analyzed is the target data obtained by analysis, wherein the target data corresponds to the conclusion information in the preset rule, that is, the conclusion information of the target data is the conclusion information in the preset rule. After the analysis of one piece of data to be analyzed in the data set to be analyzed is completed, other pieces of data to be analyzed in the data set to be analyzed are sequentially analyzed until the analysis of each piece of data to be analyzed in the data set to be analyzed is completed, any piece of data to be analyzed is not omitted, and accurate full coverage of data analysis is achieved.
And when the data to be analyzed which accords with the analysis conditions in the preset rule exist in the data set to be analyzed, taking the data to be analyzed as target data, and outputting an analysis result comprising the target data and conclusion information corresponding to the target data. The terminal generates a reminding message of successful analysis based on the obtained analysis result, so that a user or the terminal is reminded to perform the next data processing operation (for example, after the data for upgrading the version of the 360 guards is analyzed, the terminal can upgrade the original version of the 360 guards based on the target data obtained by analysis and the corresponding conclusion information of the target data).
When the data to be analyzed which meets the analysis condition in the preset rule does not exist in the data set to be analyzed, the analysis result which does not include any target data is output to indicate that the analysis process fails to analyze the valid data. The terminal generates a reminding message of analysis failure based on the analysis result so as to remind the user or the terminal to perform operations such as checking, reanalyzing, reconfiguring and the like.
Referring to the schematic diagram of the data analysis flow in fig. 2, the data 201 is some service data available to the terminal, i.e. the data to be scanned in the above description. After the terminal acquires the data 201, the terminal may scan 202 the data 201 to obtain a scanning result 203, where the scanning result 203 is also an acquired data set to be analyzed, and the data set to be analyzed includes at least one piece of data to be analyzed. A user presets a preset rule of analysis data by using the rule system 204 according to actual needs, and the analysis hub 205 in the terminal further analyzes the scanning result 203 based on the preset rule in the rule system 204, that is, at least one piece of data to be analyzed (the scanning result 203) in the data set to be analyzed is sequentially analyzed, so that at least one piece of target data and conclusion information (a conclusion 206) corresponding to the target data can be obtained through analysis, and the terminal or the user can perform subsequent related operations based on the conclusion 206.
For example, the following steps are carried out: the data 201 is related service data for 360-guard upgrade in the mobile phone, after the terminal scans the data 201, data (scanning result 203) which can have a preset rule condition in the rule system 204 of the application mobile phone can be obtained, the scanning result 203 is analyzed through the analysis center 205 based on a preset rule in the rule system 204 of the mobile phone, effective service data which can be used for 360-guard upgrade and corresponding conclusion information (conclusion 206) can be obtained, and the subsequent terminal can perform version upgrade processing on the original 360-guard in the mobile phone according to the related data in the conclusion 206.
When the scheme of the embodiment of the application is executed, the terminal scans at least one piece of data to be scanned to obtain a data set to be analyzed, the at least one piece of data to be analyzed in the data set to be analyzed is sequentially analyzed and processed based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, accurate analysis and processing of the data are achieved by means of successively applying the preset rule to the data with the condition of applying the preset rule for processing, an accurate analysis result is obtained, and the efficiency of data analysis is greatly improved.
Referring to fig. 3, a schematic flow chart of a data analysis method is provided in the present embodiment. The present embodiment is exemplified by applying the data analysis method to a terminal. The method of analyzing the data may include the steps of:
s301, at least one piece of data to be scanned is obtained.
The data to be scanned refers to various service data available to the terminal, and the data to be scanned may be generated by the terminal based on various service functions inside the terminal, or may be generated by other electronic devices except the terminal based on respective service functions and sent to the terminal.
Specifically, the terminal can directly acquire various service data generated when the terminal completes various services, or various service data which are preliminarily screened by the terminal based on the analysis purpose and need to be analyzed and processed, and the various service data are used as data to be scanned which need to be subsequently analyzed and processed; or the terminal can receive the service data which is sent by other terminals and is generated by other terminals when the other terminals finish respective services, and the received service data is taken as the data to be scanned which needs to be subjected to subsequent analysis processing.
S302, scanning at least one piece of data to be scanned to obtain a data set to be analyzed.
Specifically, refer to step S101, which is not described herein.
S303, receiving a user operation for modifying the preset rule, and generating a modification instruction in response to the user operation.
The user operation refers to a modification operation executed by a user on the terminal, and the user can perform modification operations including adding/deleting and the like on an original preset rule on a display interface of the terminal, for example: when the application scene changes, the conditions and/or conclusion information in the original preset rules can be modified correspondingly according to the requirements of the application scene. Each time the user performs a modification operation, the terminal will create a new preset rule based on the modification operation. If the preset rule is not set currently, the user can also create the preset rule on the corresponding preset rule setting interface, so that the terminal can subsequently perform corresponding analysis processing based on the preset rule.
Specifically, the user may modify the original preset rule on the display interface of the terminal according to the requirement of analyzing and processing the current data, for example: and modifying the analysis conditions in the preset rules, or modifying the conclusion information in the preset rules, or modifying the analysis methods in the preset rules. After the user executes the operation of modifying the preset rule on the terminal, the terminal identifies and responds to the user operation to generate a modification instruction corresponding to the user operation. The terminal can modify the original preset rule based on the modification instruction so that the current preset rule can meet the requirement of data analysis processing.
And S304, modifying the preset rule according to the modification instruction.
S305, sequentially judging whether at least one piece of data to be analyzed in the data set to be analyzed meets the conditions in the preset rules.
The data to be analyzed is data with conditions for processing by applying preset rules.
Specifically, the terminal analyzes each piece of data to be analyzed in the data set to be analyzed in sequence based on a preset rule, and judges whether each piece of data to be analyzed meets an analysis condition in the preset rule. One or more sub-conditions may be preset in the preset rule, such as: sub-conditions of features that need to be satisfied, features that need to be ignored, features that hit culling, and the like. When the analysis condition in the preset rule includes a plurality of sub-conditions, it can be determined that the data to be analyzed satisfies the condition in the preset rule only if the plurality of sub-conditions are satisfied at the same time.
When the data to be analyzed currently meets the conditions in the preset rules, the data to be analyzed is determined to be the target data obtained by analysis, the target data corresponds to the conclusion information in the preset rules, and then the next piece of data to be analyzed in the data set to be analyzed is determined.
And when judging that the currently analyzed data to be analyzed does not meet the conditions in the preset rules, directly judging the next other data to be analyzed in the data set to be analyzed until the judgment of all the data to be analyzed in the data set to be analyzed is finished.
S306, when it is determined that at least one piece of data to be analyzed meeting the conditions in the preset rules does not exist in the data set to be analyzed, a reminding message of analysis failure is generated.
Specifically, the terminal sequentially analyzes whether each piece of data to be analyzed in the data set to be analyzed meets the analysis condition in the preset rule based on the preset rule. After the judgment of all the data to be analyzed in the data set to be analyzed is completed, if any target data which can meet the preset rule is not obtained, that is, when it is determined that at least one piece of data to be analyzed which meets the condition in the preset rule does not exist in the data set to be analyzed, an analysis result which does not include any target data is output to indicate that the analysis process cannot analyze valid data. The terminal generates a reminding message of analysis failure based on the analysis result so as to remind the user or the terminal to perform operations such as checking, reanalyzing, reconfiguring and the like.
S307, when it is determined that at least one piece of data to be analyzed meeting the conditions in the preset rule exists in the data set to be analyzed, the at least one piece of data to be analyzed meeting the conditions in the preset rule is used as at least one piece of target data, and conclusion information corresponding to the at least one piece of target data is obtained based on the preset rule.
The target data is to-be-analyzed data which accords with a preset rule in the to-be-analyzed data set, namely the target data accords with the preset rule, and the target data is uniquely corresponding to conclusion information in the preset rule. The conclusion information is conclusion file data set in a preset rule, and unique conclusion information is set when the rule is set/created in advance.
Specifically, refer to step S102, which is not described herein.
And S308, performing association processing on at least one piece of target data and the corresponding conclusion information, and generating a reminding message of successful analysis.
Specifically, after determining at least one piece of target data from the data set to be analyzed based on the preset rule, the terminal associates the at least one piece of target data with the corresponding conclusion information thereof, so that the terminal can indirectly obtain the conclusion information corresponding to the at least one piece of target data when obtaining the at least one piece of target data subsequently, and the terminal can conveniently perform subsequent management on the at least one piece of target data obtained through analysis. Meanwhile, the terminal can also generate a reminding message of successful analysis based on at least one piece of target data obtained by analysis and the corresponding conclusion information of the target data, so that a user or the terminal is reminded to perform the next data processing operation.
And S309, analyzing the conclusion information to obtain approval data, and executing online processing operation based on the approval data.
The approval data refers to data obtained by the terminal completing approval processing based on conclusion information, the approval data is data which can be directly used for realizing an online processing process, and after the approval data is applied online, the corresponding application of the approval data can realize one or more specific functions, such as: the approval data can be data for upgrading a 360 guard version, the terminal can upgrade the original 360 guard version based on the approval data, the original 360 guard can be upgraded to a new version, and compared with the original 360 guard, the upgraded 360 guard can be added with a one-key crank call intercepting function and an intelligent virus risk recognizing function. The online processing operation refers to the operation that the terminal realizes online or updating of a specific function based on the approval data.
Specifically, after the terminal associates at least one piece of target data with the corresponding conclusion information thereof, the terminal analyzes the obtained conclusion information (the conclusion information associated with the target data) to obtain approval data which can be used for realizing an online processing process, and the approval data may include the target data and data related to the target data. And the terminal executes online processing operation based on the approval data so as to realize online or updating of functions corresponding to certain applications on the terminal.
For example, the following steps are carried out: the complicated problem that the terminal needs to analyze at present is configuration verification of 360 guard version 5 upgrading, and at least one piece of data to be scanned acquired by the terminal is business data (data to be scanned) of 360 guard version 5 upgrading, and preset rules can be created in advance as shown in table 1:
TABLE 1
The terminal scans at least one piece of data to be scanned to obtain at least one piece of data to be analyzed, analyzes at least one piece of data to be analyzed based on a preset rule (that is, the data to be analyzed sequentially applies the preset rule), and obtains target data meeting the preset rule only when the data to be analyzed meets an analysis condition in the preset rule (including three sub-conditions in table 1 that need to be met at the same time) (as shown in table 2, a filemgr.dll 7.3.0.2471 node is taken as an example for explanation, table 2 is objective data of a filemgr.dll 7.3.0.2471 node, and the objective data is data meeting the preset rule obtained after analysis). When a preset rule is applied to the objective data of the filemgr.dll 7.3.0.2471 node, if the preset rule is determined to be met, the configuration (conclusion information) of the filemgr.dll 7.3.0.2471 node is that 'beta + is on a new node line, the current formal is adjusted to 100%, and an old node is off-line'. The terminal can execute subsequent related online operation based on the conclusion information. The conclusions in table 2 are only some conclusions related to the data obtained after the data is scanned, not subsequently obtained conclusion information, and there may be one or more scanning conclusions that are obtained according to different contents corresponding to the scanned data.
TABLE 2
When a preset rule is applied to the objective data of the filemgr.dll 7.3.0.2471 node (for example, the data which is obtained through analysis processing and does not meet the preset rule in the table 3), if the preset rule is determined not to be met, the objective data of the filemgr.dll 7.3.0.2471 node is indicated to be not met with 'beta + on a new node line is formal, the current formal is adjusted to 100%, and an old node is offline'. The terminal can analyze and process the next data to be analyzed based on the analysis result, or perform subsequent checking and reconfiguration operations based on the analysis result.
TABLE 3
When the scheme of the embodiment of the application is executed, the terminal acquires at least one piece of data to be scanned, and scans the at least one piece of data to be scanned to obtain a data set to be analyzed; receiving user operation for modifying the preset rule, generating a modification instruction in response to the user operation, and modifying the preset rule according to the modification instruction; sequentially judging whether at least one piece of data to be analyzed in the data set to be analyzed meets the condition in a preset rule; when it is determined that at least one piece of data to be analyzed which meets the condition in a preset rule does not exist in the data set to be analyzed, a reminding message of analysis failure is generated; when it is determined that at least one piece of data to be analyzed meeting the conditions in the preset rule exists in the data set to be analyzed, taking the at least one piece of data to be analyzed meeting the conditions in the preset rule as at least one piece of target data, and obtaining conclusion information corresponding to the at least one piece of target data based on the preset rule; performing association processing on at least one piece of target data and corresponding conclusion information thereof, and generating a reminding message of successful analysis; and analyzing the conclusion information to obtain approval data, and executing online processing operation based on the approval data. The data analysis method has the advantages that the data with the conditions for processing by applying the preset rules are analyzed one by one in a mode of applying the preset rules one by one, no data is omitted, the preset rules can be set randomly according to the needs of users, accurate analysis of the data based on the needs of the users can be achieved, accurate analysis results are obtained, follow-up operation can be conducted according to the analysis results, and the data analysis efficiency is greatly improved.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Please refer to fig. 4, which shows a schematic structural diagram of an apparatus for analyzing data according to an exemplary embodiment of the present application. Hereinafter referred to as device 4, the device 4 may be implemented as all or part of a terminal by software, hardware or a combination of both. The apparatus 4 comprises a scanning module 401 and an analyzing module 402.
The scanning module 401 is configured to scan at least one piece of data to be scanned to obtain a data set to be analyzed; the data set to be analyzed comprises at least one piece of data to be analyzed, and the data to be analyzed is data with conditions for processing by applying preset rules;
an analysis module 402, configured to sequentially perform analysis processing on the at least one piece of data to be analyzed in the data set to be analyzed based on the preset rule, so as to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data; and the target data is the data to be analyzed which accords with the preset rule in the data set to be analyzed.
Optionally, the apparatus 4 further comprises:
and the acquisition module is used for acquiring the at least one piece of data to be scanned.
Optionally, the analysis module 402 comprises:
a judging unit, configured to sequentially judge whether the at least one piece of data to be analyzed in the data set to be analyzed meets a condition in the preset rule;
and if so, taking at least one piece of data to be analyzed meeting the condition in the preset rule as the at least one piece of target data, and obtaining conclusion information corresponding to the at least one piece of target data based on the preset rule.
Optionally, the analysis module 402 further comprises:
and the second processing unit is used for performing association processing on the at least one piece of target data and the corresponding conclusion information thereof and generating a reminding message of successful analysis.
Optionally, the analysis module 402 further comprises:
and the third processing unit is used for generating a reminding message of analysis failure if the result is negative.
Optionally, the apparatus 4 further comprises:
the first processing module is used for analyzing the conclusion information to obtain approval data;
and the second processing module is used for executing online processing operation based on the approval data.
Optionally, the apparatus 4 further comprises:
the receiving module is used for receiving user operation for modifying the preset rule;
the generating module is used for responding to the user operation and generating a modification instruction;
and the modification module is used for modifying the preset rule according to the modification instruction.
It should be noted that, when the apparatus 4 provided in the foregoing embodiment executes the data analysis method, only the division of the functional modules is illustrated, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the embodiments of the data analysis method provided by the above embodiments belong to the same concept, and details of implementation processes are shown in the embodiments of the method, which are not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
An embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiments shown in fig. 1 to fig. 3, and a specific execution process may refer to specific descriptions of the embodiments shown in fig. 1 to fig. 3, which is not described herein again.
The present application further provides a computer program product, which stores at least one instruction that is loaded and executed by the processor to implement the method for analyzing data according to the above embodiments.
Fig. 5 is a schematic structural diagram of a data analysis apparatus provided in an embodiment of the present application, which is hereinafter referred to as an apparatus 5 for short, where the apparatus 5 may be integrated in the foregoing terminal or electronic device, as shown in fig. 5, the apparatus includes: memory 502, processor 501, input device 503, output device 504, and communication interface.
The memory 502 may be a separate physical unit, and may be connected to the processor 501, the input device 503, and the output device 504 via a bus. The memory 502, processor 501, input device 503, and output device 504 may also be integrated, implemented in hardware, etc.
The memory 502 is used for storing a program implementing the above method embodiment, or various modules of the apparatus embodiment, and the processor 501 calls the program to perform the operation of the above method embodiment.
Input devices 502 include, but are not limited to, a keyboard, a mouse, a touch panel, a camera, and a microphone; the output device includes, but is not limited to, a display screen.
Communication interfaces are used to send and receive various types of messages and include, but are not limited to, wireless interfaces or wired interfaces.
Alternatively, when part or all of the analysis method of data of the above embodiments is implemented by software, the apparatus may also include only a processor. The memory for storing the program is located outside the device and the processor is connected to the memory by means of circuits/wires for reading and executing the program stored in the memory.
The processor may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP.
The processor may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
The memory may include volatile memory (volatile memory), such as random-access memory (RAM); the memory may also include a non-volatile memory (non-volatile memory), such as a flash memory (flash memory), a Hard Disk Drive (HDD) or a solid-state drive (SSD); the memory may also comprise a combination of the above kinds of memories.
Wherein the processor 501 calls the program code in the memory 502 for executing the following steps:
scanning at least one piece of data to be scanned to obtain a data set to be analyzed; the data set to be analyzed comprises at least one piece of data to be analyzed, and the data to be analyzed is data with conditions for processing by applying preset rules;
sequentially analyzing the at least one piece of data to be analyzed in the data set to be analyzed based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data; and the target data is the data to be analyzed which accords with the preset rule in the data set to be analyzed.
In one or more embodiments, before performing the scan processing on the at least one piece of data to be scanned to obtain the data set to be analyzed, the processor 501 is further configured to:
and acquiring the at least one piece of data to be scanned.
In one or more embodiments, the processor 501 performs, on the basis of the preset rule, analysis processing on the at least one piece of data to be analyzed in the data set to be analyzed in sequence to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, and is configured to:
sequentially judging whether the at least one piece of data to be analyzed in the data set to be analyzed meets the condition in the preset rule;
if so, taking at least one piece of data to be analyzed meeting the conditions in the preset rule as the at least one piece of target data, and obtaining conclusion information corresponding to the at least one piece of target data based on the preset rule.
In one or more embodiments, after the obtaining of the conclusion information corresponding to each of the at least one piece of target data based on the preset rule is executed, the processor 501 is further configured to:
and performing association processing on the at least one piece of target data and the corresponding conclusion information thereof, and generating a reminding message of successful analysis.
In one or more embodiments, after performing the sequential determination, the processor 501 is further configured to:
if not, generating a reminding message of analysis failure.
In one or more embodiments, after performing the analysis processing on the at least one piece of data to be analyzed in the data set to be analyzed sequentially based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, the processor 501 is further configured to:
analyzing the conclusion information to obtain approval data;
and executing online processing operation based on the approval data.
In one or more embodiments, before performing the analysis processing on the at least one piece of data to be analyzed in the data set to be analyzed in sequence based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, the processor 501 is further configured to:
receiving user operation for modifying the preset rule;
generating a modification instruction in response to the user operation;
and modifying the preset rule according to the modification instruction.
The embodiment of the present application further provides a computer storage medium, which stores a computer program, where the computer program is used to execute the data analysis method provided in the foregoing embodiment.
Embodiments of the present application further provide a computer program product containing instructions, which when run on a computer, cause the computer to perform the method for analyzing data provided by the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (10)
1. A method of analyzing data, the method comprising:
scanning at least one piece of data to be scanned to obtain a data set to be analyzed; the data set to be analyzed comprises at least one piece of data to be analyzed, and the data to be analyzed is data with conditions for processing by applying preset rules;
analyzing the at least one piece of data to be analyzed in the data set to be analyzed in sequence based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data; and the target data is the data to be analyzed which accords with the preset rule in the data set to be analyzed.
2. The method according to claim 1, wherein before the scanning at least one piece of data to be scanned to obtain a data set to be analyzed, the method further comprises:
and acquiring the at least one piece of data to be scanned.
3. The method according to claim 1, wherein the analyzing the at least one piece of data to be analyzed in the data set to be analyzed based on the preset rule in sequence to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data includes:
sequentially judging whether the at least one piece of data to be analyzed in the data set to be analyzed meets the condition in the preset rule;
if so, taking at least one piece of data to be analyzed meeting the conditions in the preset rule as the at least one piece of target data, and obtaining conclusion information corresponding to the at least one piece of target data based on the preset rule.
4. The method according to claim 3, wherein after obtaining conclusion information corresponding to each of the at least one target datum based on the preset rule, the method further comprises:
and performing correlation processing on the at least one piece of target data and the corresponding conclusion information thereof, and generating a reminding message of successful analysis.
5. The method according to claim 3, wherein after sequentially determining whether the at least one piece of data to be analyzed in the set of data to be analyzed satisfies a condition in the preset rule, the method further comprises:
if not, generating a reminding message of analysis failure.
6. The method according to claim 1, wherein after the analyzing the at least one piece of data to be analyzed in the data set to be analyzed in sequence based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, the method further comprises:
analyzing the conclusion information to obtain approval data;
and executing online processing operation based on the approval data.
7. The method according to claim 1, wherein before analyzing and processing the at least one piece of data to be analyzed in the data set to be analyzed in sequence based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data, the method further comprises:
receiving user operation for modifying the preset rule;
generating a modification instruction in response to the user operation;
and modifying the preset rule according to the modification instruction.
8. An apparatus for analyzing data, the apparatus comprising:
the scanning module is used for scanning at least one piece of data to be scanned to obtain a data set to be analyzed; the data set to be analyzed comprises at least one piece of data to be analyzed, and the data to be analyzed is data with conditions for processing by applying preset rules;
the analysis module is used for sequentially analyzing the at least one piece of data to be analyzed in the data set to be analyzed based on the preset rule to obtain at least one piece of target data and conclusion information corresponding to the at least one piece of target data; and the target data is the data to be analyzed which accords with the preset rule in the data set to be analyzed.
9. The apparatus of claim 8, further comprising:
and the acquisition module is used for acquiring the at least one piece of data to be scanned.
10. The apparatus of claim 8, wherein the analysis module comprises:
a judging unit, configured to sequentially judge whether the at least one piece of data to be analyzed in the data set to be analyzed meets a condition in the preset rule;
and if so, taking at least one piece of data to be analyzed meeting the condition in the preset rule as the at least one piece of target data, and obtaining conclusion information corresponding to the at least one piece of target data based on the preset rule.
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