CN116070079A - Data verification method and device based on artificial intelligence, electronic equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of data verification, and discloses a data verification method, a device, electronic equipment and a medium based on artificial intelligence, wherein the method comprises the following steps: querying an enterprise architecture corresponding to the enterprise name, and performing attribute analysis on each architecture in the enterprise architecture to obtain architecture attributes; calculating the architecture weight of each architecture in the enterprise architecture, inquiring the historical data of each architecture, and calculating the data error rate of each architecture; determining a verification order for each architecture; scheduling current architecture data of each architecture, performing data planning on the current architecture data to obtain planning data, performing data mining on the current architecture data according to the planning data to obtain mining architecture data, and performing quantization processing on the mining architecture data to obtain quantized architecture data; and carrying out data verification on the quantized architecture data according to the verification sequence to obtain verification results, and generating a verification report of the current architecture data according to the verification results. The invention aims to improve the data verification efficiency of artificial intelligence.
Description
Technical Field
The present invention relates to the field of data verification technologies, and in particular, to a data verification method and apparatus based on artificial intelligence, an electronic device, and a medium.
Background
With the development of the time progress and the information technology, all the data generated in the enterprise operation process can be summarized and verified by each enterprise, all the data in the enterprise are collected, the data are verified by using enterprise rules of the enterprise, some problems in the data can be found, the data are reasonably improved, and the information accuracy of the enterprise is improved.
In the prior art, the method for verifying the enterprise data is mainly to verify the enterprise data by combining a preset verification rule in an artificial intelligence mode, but the method is to verify all the enterprise data, and verify unimportant enterprise data, so that the workload and man-hour of data verification are increased, the efficiency of data verification is further reduced, and therefore, a method capable of improving the efficiency of the artificial intelligence data verification is needed.
Disclosure of Invention
The invention provides a data verification method, a device, electronic equipment and a medium based on artificial intelligence, and aims to improve the data verification efficiency of the artificial intelligence.
In order to achieve the above object, the present invention provides an artificial intelligence based data verification method, comprising:
acquiring enterprise names of data to be verified, inquiring enterprise frameworks corresponding to the enterprise names, and performing attribute analysis on each framework in the enterprise frameworks to obtain framework attributes;
according to the architecture attribute and the enterprise name, calculating the architecture weight of each architecture in the enterprise architecture, inquiring the historical data of each architecture, and according to the historical data, calculating the data error rate of each architecture;
determining a verification order of each architecture according to the architecture weight and the data error rate;
scheduling the current architecture data of each architecture, performing data planning on the current architecture data to obtain planning data, performing data mining on the current architecture data according to the planning data to obtain mining architecture data, and performing quantization processing on the mining architecture data to obtain quantized architecture data;
and carrying out data verification on the quantized architecture data according to the verification sequence to obtain a verification result, and generating a verification report of the current architecture data according to the verification result.
Optionally, the performing attribute analysis on each architecture in the enterprise architecture to obtain architecture attributes includes:
performing functional analysis on each architecture in the enterprise architecture to obtain architecture functions;
vector transformation is carried out on the architecture function and the enterprise architecture respectively, so as to obtain a function vector and an architecture vector;
calculating vector similarity of the function vector and the architecture vector;
screening the architecture function according to the vector similarity to obtain a target function;
and carrying out attribute analysis on the target function to obtain architecture attributes.
Optionally, the calculating the vector similarity of the function vector and the architecture vector includes:
calculating the vector similarity of the function vector and the architecture vector by the following formula:
wherein S represents the vector similarity of the function vector and the architecture vector, i represents the start vector of the function vector and the architecture vector, z represents the total number of the function vector and the architecture vector, A i Representing the vector value of the ith vector in the function vectors, B i+1 A vector value representing the i+1th vector of the architecture vectors.
Optionally, the calculating the architecture weight of each architecture in the enterprise architecture according to the architecture attribute and the enterprise name includes:
Dispatching enterprise business data of the enterprise name, and carrying out frequency statistics on each data in the enterprise business data to obtain data frequency;
determining a camping service of the enterprise name according to the data frequency;
extracting key words from the architecture attribute and the camping service to obtain a first key word and a second key word;
and calculating the association coefficient of the first keyword and the second keyword, and taking the association coefficient as the architecture weight of each architecture in the enterprise architecture.
Optionally, the calculating the data error rate of each architecture according to the historical data includes:
performing error marking on the data in the historical data to obtain error data, and performing data classification on the error data to obtain classified error data;
carrying out semantic analysis on the classified error data to obtain data semantics, and analyzing error factors corresponding to the classified error data according to the data semantics;
counting the factor frequency and the total factor quantity of the error factors, and calculating error coefficients corresponding to the error factors according to the factor frequency and the total factor quantity;
And obtaining the data error rate of each architecture according to the error coefficient.
Optionally, the determining the verification order of each architecture according to the architecture weight and the data error rate includes:
sequencing each architecture according to the data error rate to obtain a first sequence, and sequencing each architecture according to the architecture weight to obtain a second sequence;
combining and optimizing the first sequence and the second sequence by using a preset optimal algorithm to obtain a target sequence;
the target sequence is taken as a verification order of each architecture.
Optionally, the performing data planning on the current architecture data to obtain planning data includes:
extracting the characteristics of the current architecture data to obtain characteristic data;
performing linear transformation on the characteristic data and the current architecture data to obtain first linear data and second linear data;
calculating a correlation coefficient of the first linear data and the second linear data;
and when the correlation coefficient is larger than a preset threshold value, performing data planning on the first linear data and the second linear data to obtain planning data.
An artificial intelligence based data verification device, the device comprising:
the attribute analysis module is used for acquiring enterprise names of data to be verified, inquiring enterprise architectures corresponding to the enterprise names, and carrying out attribute analysis on each architecture in the enterprise architectures to obtain architecture attributes;
the error rate calculation module is used for calculating the architecture weight of each architecture in the enterprise architecture according to the architecture attribute and the enterprise name, inquiring the historical data of each architecture and calculating the data error rate of each architecture according to the historical data;
an order determining module, configured to determine a verification order of each architecture according to the architecture weight and the data error rate;
the quantization processing module is used for scheduling the current architecture data of each architecture, carrying out data planning on the current architecture data to obtain planning data, carrying out data mining on the current architecture data according to the planning data to obtain mining architecture data, and carrying out quantization processing on the mining architecture data to obtain quantized architecture data;
and the report generation module is used for carrying out data verification on the quantized architecture data according to the verification sequence to obtain verification results, and generating a verification report of the current architecture data according to the verification results.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based data verification method described above.
In order to solve the above-mentioned problems, the present invention further provides a computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the artificial intelligence-based data verification method described above.
The invention can inquire the architecture information of enterprises by acquiring the enterprise names of the data to be verified and inquiring the enterprise architectures corresponding to the enterprise names so as to know the composition information of the enterprises, thereby providing convenience for the subsequent processing of the enterprise architectures; in addition, the present invention performs data planning on the present architecture data by scheduling the present architecture data of each architecture, so that data describing the relationship between the present architecture data can be obtained, and convenience is provided for subsequent data mining. Therefore, the data verification method, the device, the electronic equipment and the medium based on the artificial intelligence can improve the data verification efficiency of the artificial intelligence.
Drawings
FIG. 1 is a schematic flow chart of an artificial intelligence based data verification method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a calibration device for calibrating the installation deviation of a full-automatic nonlinear search pan-tilt camera according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device implementing the artificial intelligence-based data verification method according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a data verification method based on artificial intelligence. In the embodiment of the present application, the execution body of the data verification method based on artificial intelligence includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided in the embodiment of the present application. In other words, the artificial intelligence based data verification method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of an artificial intelligence based data verification method according to an embodiment of the invention is shown. In this embodiment, the artificial intelligence based data verification method includes steps S1 to S5.
S1, acquiring enterprise names of data to be verified, inquiring enterprise frameworks corresponding to the enterprise names, and performing attribute analysis on each framework in the enterprise frameworks to obtain framework attributes.
According to the invention, the enterprise name of the data to be verified is obtained, the enterprise architecture corresponding to the enterprise name is queried, and the architecture information of the enterprise can be queried, so that the composition information of the enterprise can be conveniently known, and convenience is provided for subsequent processing of the enterprise architecture.
The enterprise name is an enterprise name needing to be subjected to data verification, such as a hal company, the enterprise architecture is an enterprise component part of the enterprise name, for example, an enterprise comprises a business establishment department, an operation department, a market part, an after-sales department and the like, the departments are architectures of the enterprise, and further, the enterprise architecture corresponding to the enterprise name can be queried through sky eye examination or an enterprise website.
Further, by analyzing the attribute of each architecture in the enterprise architecture, the invention can know the related attribute information of the enterprise architecture and increase the cognition degree of the enterprise architecture, wherein the architecture attribute is the functional attribute information of the enterprise architecture, and the attribute corresponding to the market department is market service and responsible for market activity.
As an embodiment of the present invention, the performing attribute analysis on each architecture in the enterprise architecture to obtain architecture attributes includes: performing functional analysis on each architecture in the enterprise architecture to obtain architecture functions, performing vector conversion on the architecture functions and the enterprise architecture to obtain function vectors and architecture vectors, calculating vector similarity of the function vectors and the architecture vectors, screening the architecture functions according to the vector similarity to obtain target functions, and performing attribute analysis on the target functions to obtain architecture attributes.
The architecture function is an effect corresponding to each architecture in the enterprise architecture, the function vector and the architecture vector are vector expression forms of the architecture function and the enterprise architecture respectively, the vector similarity represents the similarity degree between the function vector and the architecture vector, and the target function is a function obtained after the architecture function is screened according to the numerical value of the vector similarity.
Further, in an alternative embodiment of the present invention, the function analysis of each architecture in the enterprise architecture may be implemented by a function analysis method, such as a correlation analysis method, the architecture function and the vector conversion of the enterprise architecture may be implemented by a vector conversion algorithm, such as a Word2vec algorithm, the screening of the architecture function may be implemented by a screening function, such as a FILTER function, and the attribute analysis of the target function may be implemented by a map function.
Further, as an optional embodiment of the present invention, the calculating the vector similarity between the function vector and the architecture vector includes:
calculating the vector similarity of the function vector and the architecture vector by the following formula:
wherein S represents the vector similarity of the function vector and the architecture vector, i represents the sequence numbers of the function vector and the architecture vector, z represents the sum of the numbers of the function vector and the architecture vector, A i Representing the vector value of the ith vector in the function vectors, B i+1 A vector value representing the i+1th vector of the architecture vectors.
S2, calculating the architecture weight of each architecture in the enterprise architecture according to the architecture attribute and the enterprise name, inquiring the historical data of each architecture, and calculating the data error rate of each architecture according to the historical data.
According to the method and the system, the importance degree of each architecture in the enterprise architecture can be known by calculating the architecture weight of each architecture in the enterprise architecture according to the architecture attribute and the enterprise name, and a guarantee is provided for the follow-up determination of the verification sequence of each architecture, wherein the architecture weight represents the importance degree of each architecture in the enterprise architecture.
As one embodiment of the present invention, the calculating the architecture weight of each of the enterprise architectures according to the architecture attribute and the enterprise name includes: scheduling enterprise business data of the enterprise name, carrying out frequency statistics on each data in the enterprise business data to obtain data frequency, determining a main business of the enterprise name according to the data frequency, extracting key words from the architecture attribute and the main business to obtain a first key word and a second key word, calculating association coefficients of the first key word and the second key word, and taking the association coefficients as architecture weights of each architecture in the enterprise architecture.
The business data are data of an activity item of the business corresponding to the business name, such as production processing data and product related data of a production part, the data frequency is the occurrence frequency of each data in the business data, the primary business is a main business in the business corresponding to the business name, the first keyword and the second keyword are important words in the architecture attribute and the primary business respectively, and the association coefficient represents the association degree of the first keyword and the second keyword.
Further, the invention calculates the data error rate of each architecture according to the historical data by inquiring the historical data of each architecture, so that the probability of data errors in each architecture can be known, and a precondition is provided for the follow-up determination of the verification sequence of each architecture, wherein the data error rate is the probability of data errors in each architecture, and further, the inquiry of the historical data of each architecture can be realized through a SUMIF function.
As an embodiment of the present invention, the calculating the data error rate of each architecture according to the historical data includes: performing error marking on the data in the historical data to obtain error data, performing data classification on the error data to obtain classified error data, performing semantic analysis on the classified error data to obtain data semantics, analyzing error factors corresponding to the classified error data according to the data semantics, counting factor frequency and factor total quantity of the error factors, calculating error coefficients corresponding to the error factors according to the factor frequency and the factor total quantity, and obtaining the data error rate of each architecture according to the error coefficients.
The error data is data with errors in the historical data, the classified error data is data with similar degrees of error types corresponding to the error data, the data semantics are data meanings of the classified error data, the error factors are error factors corresponding to the error data, such as format errors or word errors, the factor frequency is the occurrence frequency of the error factors, the total factor is the total number of the error factors, and the error coefficient is the proportion of the error factors to the total factor.
Further, as an optional embodiment of the present invention, the error marking of the data in the history data may be implemented by a marking tool, the marking tool includes a color marking tool, the data classification of the error data may be implemented by a SUBTOTAL function, the semantic analysis of the classified error data may be implemented by a semantic analysis method, the analysis of the error factor corresponding to the classified error data may be implemented by a concatenated substitution method, the statistics of the factor frequency and the factor total of the error factor may be implemented by a statistical grouping method, and the error coefficient may be obtained by a ratio of the factor frequency to the factor total.
S3, determining the verification sequence of each framework according to the framework weight and the data error rate.
According to the invention, the verification order of each architecture is determined according to the architecture weight and the data error rate, so that the architecture corresponding to the verification order has the characteristics of high weight and high error rate, and further, important data and data with high error rate can be subjected to verification preferentially, and the data verification efficiency is improved, wherein the verification order is the corresponding order when the data of each architecture is subjected to verification.
As one embodiment of the present invention, the determining the verification order of each architecture according to the architecture weight and the data error rate includes: and sequencing each architecture according to the data error rate to obtain a first sequence, sequencing each architecture according to the architecture weight to obtain a second sequence, combining and optimizing the first sequence and the second sequence by using a preset optimal algorithm to obtain a target sequence, and taking the target sequence as a verification sequence of each architecture.
The first sequence is a sequence obtained by sequencing each architecture according to the numerical value of the data error rate, the second sequence is a sequence obtained by sequencing each architecture according to the numerical value of the architecture weight, the preset optimal algorithm is an algorithm for processing a problem according to a certain path or rule to obtain an optimal scheme of the problem, such as a gradient descent algorithm, and the target sequence is a sequence obtained by combining the first sequence and the second sequence through the preset optimal algorithm.
Further, as an optional embodiment of the present invention, the sorting of each architecture may be implemented by a sorting algorithm, such as a bubbling sorting algorithm, and the target sequence may be obtained by calculating gradient values of the first sequence and the second sequence by the preset optimal algorithm, and then performing merging optimization on the first sequence and the second sequence according to the gradient values.
And S4, scheduling the current architecture data of each architecture, performing data planning on the current architecture data to obtain planning data, performing data mining on the current architecture data according to the planning data to obtain mining architecture data, and performing quantization processing on the mining architecture data to obtain quantized architecture data.
The invention performs data planning on the current architecture data by scheduling the current architecture data of each architecture, so that the data describing the relation between the current architecture data can be obtained, and convenience is provided for subsequent data mining, wherein the planning data is the data describing the relation between the current architecture data, and further, the scheduling of the current architecture data of each architecture can be realized by a priority scheduling algorithm.
As an embodiment of the present invention, the performing data planning on the current architecture data to obtain planning data includes: and performing feature extraction on the current architecture data to obtain feature data, performing linear transformation on the feature data and the current architecture data to obtain first linear data and second linear data, calculating correlation coefficients of the first linear data and the second linear data, and performing data planning on the first linear data and the second linear data when the correlation coefficients are larger than a preset threshold value to obtain planning data.
The characteristic data are representative data in the current architecture data, the first linear data and the second linear data are data which are common in the characteristic data and the current architecture data respectively, the correlation coefficient represents a data relationship between the first linear data and the second linear data, and the preset threshold value is a value for judging the correlation coefficient, can be 0.8, and can be set according to an actual service scene.
Further, feature extraction of the current architecture data may be performed by a SIFT feature extraction algorithm, linear transformation of the feature data and the current architecture data may be performed by a linear function, such as a linear function, a correlation coefficient calculator may calculate correlation coefficients of the first linear data and the second linear data, and data planning of the first linear data and the second linear data may be performed by a data planning algorithm, where the data planning algorithm is compiled by a scripting language.
According to the invention, the data with special relation hidden in the current architecture data can be mined by carrying out data mining on the current architecture data according to the planning data, so that the difficulty in subsequent quantization processing of the mined architecture data is reduced, wherein the mined architecture data is obtained after the data with the special relation hidden in the current architecture data is mined.
As an embodiment of the present invention, the data mining the current architecture data according to the planning data to obtain mined architecture data includes: calculating the feature score of each data in the planning data, carrying out data integration on the current architecture data according to the feature score to obtain an architecture data set, carrying out data protocol on the architecture data set to obtain architecture protocol data, and carrying out data mining on the architecture protocol data by using a preset cluster analysis algorithm to obtain mining architecture data.
The feature score is a feature score corresponding to each piece of data in the planning data, the higher the score is, the more obvious the feature of the data is, the architecture data set is the data obtained after the current architecture data is subjected to data set according to the feature score, the architecture specification data is the data of the architecture data set after constraint simplification, and the preset clustering analysis algorithm is an algorithm for deep mining of the data.
Further, the feature score may be obtained by constructing a feature matrix of each data in the planning data and then calculating an average value of the feature matrix, the data integration of the current architecture data may be achieved by an EIP integration method, the data specification of the architecture data set may be achieved by a principal component analysis method, and the cluster analysis algorithm includes a fuzzy cluster method.
The invention can make the mining architecture data express in a concrete form by carrying out quantization processing on the mining architecture data, wherein the quantization architecture data is concrete expression data of the mining architecture data, and further, the quantization processing on the mining architecture data can be realized by an operation research method.
And S5, carrying out data verification on the quantized architecture data according to the verification sequence to obtain verification results, and generating a verification report of the current architecture data according to the verification results.
According to the invention, the data verification is carried out on the quantized architecture data according to the verification sequence, so that the verification efficiency of the quantized architecture data can be improved, wherein the verification result is obtained after the quantized architecture data is subjected to verification, and further, the data verification of the quantized architecture data can be carried out through a man-machine interaction method or according to a verification rule formulated by an enterprise.
According to the invention, the verification result can be displayed more clearly by generating the verification report of the current architecture data according to the verification result, so that a corresponding solution can be formulated according to the verification report, wherein the verification report is a report of the verification result and analysis of the verification result, and further, the verification report can be realized through a report generator which is compiled by Java language.
The invention can inquire the architecture information of enterprises by acquiring the enterprise names of the data to be verified and inquiring the enterprise architectures corresponding to the enterprise names so as to know the composition information of the enterprises, thereby providing convenience for the subsequent processing of the enterprise architectures; in addition, the present invention performs data planning on the present architecture data by scheduling the present architecture data of each architecture, so that data describing the relationship between the present architecture data can be obtained, and convenience is provided for subsequent data mining. Therefore, the data verification method based on the artificial intelligence provided by the embodiment of the invention can improve the data verification efficiency of the artificial intelligence.
FIG. 2 is a functional block diagram of an artificial intelligence based data verification device according to an embodiment of the present invention.
The artificial intelligence based data verification apparatus 100 of the present invention may be installed in an electronic device. Depending on the functionality implemented, the artificial intelligence based data verification device 100 may include an attribute analysis module 101, an error rate calculation module 102, an order determination module 103, a quantization processing module 104, and a report generation module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the attribute analysis module 101 is configured to obtain an enterprise name of data to be verified, query an enterprise architecture corresponding to the enterprise name, and perform attribute analysis on each architecture in the enterprise architecture to obtain an architecture attribute;
the error rate calculation module 102 is configured to calculate an architecture weight of each architecture in the enterprise architecture according to the architecture attribute and the enterprise name, query historical data of each architecture, and calculate a data error rate of each architecture according to the historical data;
The order determining module 103 is configured to determine a verification order of each architecture according to the architecture weight and the data error rate;
the quantization processing module 104 is configured to schedule current architecture data of each architecture, perform data planning on the current architecture data to obtain planning data, perform data mining on the current architecture data according to the planning data to obtain mining architecture data, and perform quantization processing on the mining architecture data to obtain quantized architecture data;
the report generating module 105 is configured to perform data verification on the quantized architecture data according to the verification order, obtain a verification result, and generate a verification report of the current architecture data according to the verification result.
In detail, each module in the calibration device 100 for the installation deviation of the pan-tilt camera in the embodiment of the present application adopts the same technical means as the artificial intelligence-based data verification method described in fig. 1, and can generate the same technical effects, which is not repeated here.
Fig. 3 is a schematic structural diagram of an electronic device 1 implementing an artificial intelligence-based data verification method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as an artificial intelligence based data verification method program.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, executes an artificial intelligence-based data verification method program or the like), and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of an artificial intelligence-based data verification method program, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The artificial intelligence based data verification method program stored in the memory 11 of the electronic device 1 is a combination of instructions which, when executed in the processor 10, may implement:
Acquiring enterprise names of data to be verified, inquiring enterprise frameworks corresponding to the enterprise names, and performing attribute analysis on each framework in the enterprise frameworks to obtain framework attributes;
according to the architecture attribute and the enterprise name, calculating the architecture weight of each architecture in the enterprise architecture, inquiring the historical data of each architecture, and according to the historical data, calculating the data error rate of each architecture;
determining a verification order of each architecture according to the architecture weight and the data error rate;
scheduling the current architecture data of each architecture, performing data planning on the current architecture data to obtain planning data, performing data mining on the current architecture data according to the planning data to obtain mining architecture data, and performing quantization processing on the mining architecture data to obtain quantized architecture data;
and carrying out data verification on the quantized architecture data according to the verification sequence to obtain a verification result, and generating a verification report of the current architecture data according to the verification result.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring enterprise names of data to be verified, inquiring enterprise frameworks corresponding to the enterprise names, and performing attribute analysis on each framework in the enterprise frameworks to obtain framework attributes;
according to the architecture attribute and the enterprise name, calculating the architecture weight of each architecture in the enterprise architecture, inquiring the historical data of each architecture, and according to the historical data, calculating the data error rate of each architecture;
Determining a verification order of each architecture according to the architecture weight and the data error rate;
scheduling the current architecture data of each architecture, performing data planning on the current architecture data to obtain planning data, performing data mining on the current architecture data according to the planning data to obtain mining architecture data, and performing quantization processing on the mining architecture data to obtain quantized architecture data;
and carrying out data verification on the quantized architecture data according to the verification sequence to obtain a verification result, and generating a verification report of the current architecture data according to the verification result.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention 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 integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method of data verification based on artificial intelligence, the method comprising:
acquiring enterprise names of data to be verified, inquiring enterprise frameworks corresponding to the enterprise names, and performing attribute analysis on each framework in the enterprise frameworks to obtain framework attributes;
according to the architecture attribute and the enterprise name, calculating the architecture weight of each architecture in the enterprise architecture, inquiring the historical data of each architecture, and according to the historical data, calculating the data error rate of each architecture;
Determining a verification order of each architecture according to the architecture weight and the data error rate;
scheduling the current architecture data of each architecture, performing data planning on the current architecture data to obtain planning data, performing data mining on the current architecture data according to the planning data to obtain mining architecture data, and performing quantization processing on the mining architecture data to obtain quantized architecture data;
and carrying out data verification on the quantized architecture data according to the verification sequence to obtain a verification result, and generating a verification report of the current architecture data according to the verification result.
2. The artificial intelligence based data verification method of claim 1, wherein performing attribute analysis on each of the enterprise architectures to obtain architecture attributes comprises:
performing functional analysis on each architecture in the enterprise architecture to obtain architecture functions;
vector transformation is carried out on the architecture function and the enterprise architecture respectively, so as to obtain a function vector and an architecture vector;
calculating vector similarity of the function vector and the architecture vector;
screening the architecture function according to the vector similarity to obtain a target function;
And carrying out attribute analysis on the target function to obtain architecture attributes.
3. The artificial intelligence based data verification method of claim 2, wherein the calculating the vector similarity of the functional vector and the architecture vector comprises:
calculating the vector similarity of the function vector and the architecture vector by the following formula:
wherein S represents the vector similarity of the function vector and the architecture vector, i represents the sequence numbers of the function vector and the architecture vector, z represents the sum of the numbers of the function vector and the architecture vector, A i Representing the vector value of the ith vector in the function vectors, B i+1 A vector value representing the i+1th vector of the architecture vectors.
4. The artificial intelligence based data verification method of claim 1, wherein the calculating the architecture weight of each of the enterprise architectures based on the architecture attributes and the enterprise names comprises:
dispatching enterprise business data of the enterprise name, and carrying out frequency statistics on each data in the enterprise business data to obtain data frequency;
determining a camping service of the enterprise name according to the data frequency;
extracting key words from the architecture attribute and the camping service to obtain a first key word and a second key word;
And calculating the association coefficient of the first keyword and the second keyword, and taking the association coefficient as the architecture weight of each architecture in the enterprise architecture.
5. The artificial intelligence based data verification method of claim 1, wherein said calculating the data error rate of each of the architectures from the historical data comprises:
performing error marking on the data in the historical data to obtain error data, and performing data classification on the error data to obtain classified error data;
carrying out semantic analysis on the classified error data to obtain data semantics, and analyzing error factors corresponding to the classified error data according to the data semantics;
counting the factor frequency and the total factor quantity of the error factors, and calculating error coefficients corresponding to the error factors according to the factor frequency and the total factor quantity;
and obtaining the data error rate of each architecture according to the error coefficient.
6. The artificial intelligence based data verification method of claim 1, wherein said determining the verification order of each of the architectures based on the architecture weights and the data error rates comprises:
Sequencing each architecture according to the data error rate to obtain a first sequence, and sequencing each architecture according to the architecture weight to obtain a second sequence;
combining and optimizing the first sequence and the second sequence by using a preset optimal algorithm to obtain a target sequence;
the target sequence is taken as a verification order of each architecture.
7. The artificial intelligence based data verification method of claim 1, wherein the performing data planning on the current architecture data to obtain planning data includes:
extracting the characteristics of the current architecture data to obtain characteristic data;
performing linear transformation on the characteristic data and the current architecture data to obtain first linear data and second linear data;
calculating a correlation coefficient of the first linear data and the second linear data;
and when the correlation coefficient is larger than a preset threshold value, performing data planning on the first linear data and the second linear data to obtain planning data.
8. An artificial intelligence based data verification device, the device comprising:
the attribute analysis module is used for acquiring enterprise names of data to be verified, inquiring enterprise architectures corresponding to the enterprise names, and carrying out attribute analysis on each architecture in the enterprise architectures to obtain architecture attributes;
The error rate calculation module is used for calculating the architecture weight of each architecture in the enterprise architecture according to the architecture attribute and the enterprise name, inquiring the historical data of each architecture and calculating the data error rate of each architecture according to the historical data;
an order determining module, configured to determine a verification order of each architecture according to the architecture weight and the data error rate;
the quantization processing module is used for scheduling the current architecture data of each architecture, carrying out data planning on the current architecture data to obtain planning data, carrying out data mining on the current architecture data according to the planning data to obtain mining architecture data, and carrying out quantization processing on the mining architecture data to obtain quantized architecture data;
and the report generation module is used for carrying out data verification on the quantized architecture data according to the verification sequence to obtain verification results, and generating a verification report of the current architecture data according to the verification results.
9. In order to solve the above-mentioned problems, the present invention also provides an electronic device, the electronic device is characterized by comprising:
at least one processor; the method comprises the steps of,
A memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based data verification method of any one of claims 1 to 7.
10. In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the artificial intelligence based data verification method according to any one of claims 1 to 7.
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