CN115880705B - Material auditing method, device, equipment and storage medium based on image recognition - Google Patents
Material auditing method, device, equipment and storage medium based on image recognition Download PDFInfo
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Abstract
The invention relates to a computer image recognition technology, and discloses a material auditing method, device, equipment and storage medium based on image recognition, wherein the method comprises the following steps: acquiring an original image of an electric power inspection evidence material, and carrying out region division on the original image to obtain a first region and a second region; performing content identification on the first area to obtain material information, and auditing the material information to obtain a first auditing result; calculating a factor correlation coefficient of the second area, determining an evaluation factor of the second area according to the factor correlation coefficient, establishing a judgment matrix according to the evaluation factor, and calculating the maximum characteristic value of the judgment matrix; and auditing the maximum characteristic value to obtain a second auditing result, and integrating the first auditing result and the second auditing result to obtain a target auditing result. The invention can improve the accuracy and efficiency when checking the electric power checking and evidence material.
Description
Technical Field
The present invention relates to the field of computer image recognition technologies, and in particular, to a method, an apparatus, a device, and a storage medium for auditing materials based on image recognition.
Background
Since the auditing of the electric power auditing and evidence-providing material is an indispensable part in engineering construction, the auditing importance is self-evident, the auditing related content needs to be determined before the material auditing starts, and a reasonable, scientific and comprehensive auditing plan is formulated, so that the problems of disorder, omission and the like of auditing are effectively avoided. At present, a general auditing method is mostly adopted for auditing the auditing and evidence materials, but the general auditing method has low efficiency and can not make more accurate judgment, and when auditing the materials to be audited submitted by a certain user, an auditor generally needs to switch display windows to obtain various information related to the materials to be audited, and the information omission easily occurs due to the switching back and forth among a plurality of windows, and more time is required, so that the auditing efficiency of the materials to be audited is low. In summary, the problem of low accuracy and efficiency in checking materials based on image recognition exists in the prior art.
Disclosure of Invention
The invention provides a material auditing method, device, equipment and storage medium based on image recognition, and mainly aims to solve the problem that the accuracy and efficiency are low when auditing electric power auditing and evidence materials are audited.
In order to achieve the above object, the present invention provides a material auditing method based on image recognition, including:
acquiring an original image of an electric power inspection evidence material, and carrying out region division on the original image to obtain a first region and a second region;
performing content identification on the first area to obtain material information, and auditing the material information to obtain a first auditing result;
calculating a factor correlation coefficient of the second area, determining an evaluation factor of the second area according to the factor correlation coefficient, establishing a judgment matrix according to the evaluation factor, and calculating a maximum eigenvalue of the judgment matrix, wherein the factor correlation coefficient of the second area is calculated by using the following formula:
wherein ,
representing the factor correlation coefficient,/->
A +.o representing said second region>
Factor (F)>
Representation houseThe second region->
Factor (F)>
A factor number representing the second region;
and auditing the maximum characteristic value to obtain a second auditing result, and integrating the first auditing result and the second auditing result to obtain a target auditing result.
Optionally, the performing region division on the original image to obtain a first region and a second region includes:
acquiring the image size of the original image, and performing proportional clipping on the original image according to the image size to obtain a clipping image;
acquiring the image category of the clipping image, and judging whether the image category is consistent with a preset standard category or not;
when the image category is consistent with the standard category, taking a clipping image corresponding to the image category as a first area;
and when the image category is inconsistent with the standard category, taking the clipping image corresponding to the image category as a second area.
Optionally, the performing content recognition on the first area to obtain material information includes:
extracting the content of the first area to obtain text data;
and carrying out text recognition on the text data by using a preset image recognition technology to obtain material information.
Optionally, the auditing the material information to obtain a first auditing result includes:
acquiring equipment information in the material information, and determining target financial information according to the equipment information;
calculating average financial information according to the quantity information and the financial information in the material information, and judging whether the average financial information exceeds the target financial information;
when the average financial information does not exceed the target financial information, the first audit result is that the audit is passed;
and when the average financial information exceeds the target financial information, the first audit result is that audit is not passed.
Optionally, the establishing a judgment matrix according to the evaluation factor includes:
randomly selecting two evaluation factors for scaling to obtain a scaling result, and numbering the scaling result to obtain a number;
until the scale and numbering among all the evaluation factors are completed, obtaining a target scale result and a target number;
and taking the target scale result as a row vector, taking the target number as a column vector, and establishing a table, and taking the table as a judgment matrix.
Optionally, the calculating the maximum eigenvalue of the judgment matrix includes:
obtaining matrix elements in the judgment matrix, and calculating the geometric average value of all matrix elements in the row of the matrix elements;
normalizing the geometric mean value to obtain mean value weight, and calculating characteristic roots according to the mean value weight and the judgment matrix;
calculating by utilizing the characteristic root and the average weight to obtain a maximum characteristic value;
the maximum eigenvalue is calculated using the following formula:
wherein ,
representing the maximum characteristic value, +_>
Indicate->
Root of personal characteristics->
Indicate->
Mean weight,/->
Representing the total number of feature roots and the mean weights.
Optionally, the auditing the maximum feature value to obtain a second auditing result includes:
acquiring a characteristic attribute corresponding to the maximum characteristic value, and judging whether the characteristic attribute is consistent with a preset target characteristic attribute or not;
when the characteristic attribute is consistent with the target characteristic attribute, the auditing of the maximum characteristic value is passed, and the auditing is taken as a second auditing result;
and when the characteristic attribute is inconsistent with the target characteristic attribute, the auditing of the maximum characteristic value is not passed, and the auditing is not passed as a second auditing result.
In order to solve the above problems, the present invention also provides a material auditing device based on image recognition, the device comprising:
the original image dividing module is used for obtaining an original image of the electric power inspection evidence material, and dividing the original image into areas to obtain a first area and a second area;
the material information auditing module is used for carrying out content identification on the first area to obtain material information, and auditing the material information to obtain a first auditing result;
the factor correlation coefficient processing module is used for calculating the factor correlation coefficient of the second area, determining the evaluation factor of the second area according to the factor correlation coefficient, establishing a judgment matrix according to the evaluation factor, and calculating the maximum eigenvalue of the judgment matrix, wherein the factor correlation coefficient of the second area is calculated by using the following formula:
wherein ,
representing the factor correlation coefficient,/->
A +.o representing said second region>
Factor (F)>
A +.o representing said second region>
Factor (F)>
A factor number representing the second region;
and the maximum characteristic value auditing module is used for auditing the maximum characteristic value to obtain a second auditing result, and integrating the first auditing result and the second auditing result to obtain a target auditing result.
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 image recognition-based material auditing method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned image recognition-based material auditing method.
According to the embodiment of the invention, the original image of the electric power inspection evidence material is obtained, and the original image is subjected to region division, so that the first region and the second region can be accurately obtained, and the processing range is reduced; the material information can be accurately obtained by carrying out content identification on the first area; the auditing accuracy can be improved by auditing the material information; the factor correlation coefficient of the second area is calculated, the evaluation factor of the second area is determined according to the factor correlation coefficient, a judgment matrix is established according to the evaluation factor, and the maximum characteristic value of the judgment matrix is calculated, so that the calculated amount can be reduced, and the processing efficiency is improved; by auditing the maximum characteristic value, the electric power audit evidence material can be audited from the other side, so that the auditing accuracy is improved; the first checking result and the second checking result are integrated to obtain a target checking result, so that the accuracy and the efficiency can be improved when the material checking based on image identification is performed. Therefore, the material auditing method, device, equipment and storage medium based on image recognition can solve the problem of lower accuracy and efficiency when the material auditing based on image recognition is performed.
Drawings
FIG. 1 is a schematic flow chart of a material auditing method based on image recognition according to an embodiment of the present invention;
FIG. 2 is a flow chart of the method for dividing an original image into a first region and a second region according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating a method for establishing a judgment matrix according to an evaluation factor according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a material auditing apparatus based on image recognition according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the material auditing method based on image recognition 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 material auditing method based on image recognition. The execution subject of the image recognition-based material auditing method 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 by the embodiment of the application. In other words, the image recognition-based material auditing method may be performed by software or hardware installed in a terminal device or a server device, where 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 a material auditing method based on image recognition according to an embodiment of the present invention is shown. In this embodiment, the method for auditing materials based on image recognition includes:
s1, acquiring an original image of the electric power inspection evidence material, and dividing the original image into areas to obtain a first area and a second area.
In the embodiment of the invention, the electric power inspection evidence material comprises materials such as an account book, an accounting voucher, a contract text and the like; the material image refers to an image obtained by storing the electric power inspection evidence material in modes of uploading accessories, photographing and the like.
Referring to fig. 2, in the embodiment of the present invention, the performing region division on the original image to obtain a first region and a second region includes:
s21, acquiring the image size of the original image, and performing proportional clipping on the original image according to the image size to obtain a clipping image;
s22, acquiring the image category of the cut image, and judging whether the image category is consistent with a preset standard category;
s23, when the image category is consistent with the standard category, taking a clipping image corresponding to the image category as a first area;
and S24, when the image category is inconsistent with the standard category, taking the clipping image corresponding to the image category as a second area.
In the embodiment of the present invention, the image size of the original image may be cut into a cut image with a preset proportion size, for example, the preset proportion size may be 20×20; the standard category can be clause content in a contract, an original numerical value in a report and the like, and when the image category of the clipping image is consistent with the standard category, the clipping image can be used as a first area, and the first area can be a content area; when the image category of the cut image is inconsistent with the standard category, the cut image may be regarded as a second area, and the second area may be a signature area.
In the embodiment of the invention, more templated text contents exist in the electric power inspection evidence material, so that the area where the text contents are located is a first area, such as format clauses, template characters and the like; there is also a user signed area within the power audit ticket, i.e., a second area, including user signatures and other information such as first party, second party, year, month, day, etc.
S2, carrying out content identification on the first area to obtain material information, and auditing the material information to obtain a first auditing result.
In the embodiment of the present invention, the content identification of the first area to obtain material information includes:
extracting the content of the first area to obtain text data;
and carrying out text recognition on the text data by using a preset image recognition technology to obtain material information.
In the embodiment of the invention, the image recognition technology refers to the process of eliminating redundant information by classifying and extracting important features in the text region, so that the processing efficiency of a computer is improved; the image recognition technology comprises, but is not limited to, remote sensing image recognition technology and OCR technology; the material information includes technical information, quantity information, and the like, for example, equipment model numbers of the voltage devices, the quantity of the voltage devices, the price of the voltage devices, and the like.
In the embodiment of the present invention, the auditing the material information to obtain a first auditing result includes:
acquiring equipment information in the material information, and determining target financial information according to the equipment information;
calculating average financial information according to the quantity information and the financial information in the material information, and judging whether the average financial information exceeds the target financial information;
when the average financial information does not exceed the target financial information, the first audit result is that the audit is passed;
and when the average financial information exceeds the target financial information, the first audit result is that audit is not passed.
In the embodiment of the invention, according to the equipment information in the material information, searching the market average price corresponding to the equipment information from a preset information base, and taking the market average price as target financial information, wherein the information base comprises various equipment information and corresponding market average price; performing four-rule operation according to the quantity information and the financial information to obtain average financial information; when the average financial information exceeds the target financial information, the price exceeds the standard, namely the price does not meet the normal requirement, and the auditing is not passed; and when the average financial information does not exceed the target financial information, the price is not exceeded, the requirement is met, and the auditing is passed.
S3, calculating a factor correlation coefficient of the second area, determining an evaluation factor of the second area according to the factor correlation coefficient, establishing a judgment matrix according to the evaluation factor, and calculating the maximum eigenvalue of the judgment matrix.
In an alternative embodiment of the present invention, the factor correlation coefficient of the second region is calculated using the following formula:
wherein ,
representing the factor correlation coefficient,/->
A +.o representing said second region>
Factor (F)>
A y-th factor representing said second region, ->
A factor number representing the second region.
Further, in the embodiment of the present invention, the factor correlation coefficient is used to determine the evaluation factor of the second area to ensure the construction premise of the subsequent judgment matrix, and it should be noted that, in the present invention, when the factor correlation coefficient is greater than a preset target value, the factor corresponding to the factor correlation coefficient is used as the evaluation factor, where the target value may be a preset value, for example, the target value is set to be
When the absolute value of the factor correlation coefficient is greater than +.>
In this case, the corresponding factor can be used as the evaluation factor.
Referring to fig. 3, in an embodiment of the present invention, the establishing a judgment matrix according to the evaluation factor includes:
s31, randomly selecting two evaluation factors for scaling to obtain a scaling result, and numbering the scaling result to obtain a number;
s32, until the scale and numbering among all the evaluation factors are completed, obtaining a target scale result and a target number;
s33, taking the target scale result as a row vector, taking the target number as a column vector, and establishing a table, and taking the table as a judgment matrix.
In the embodiment of the invention, the scale result refers to a scale value obtained by scaling by a preset scale method, and a table is established according to the scale value and the target number, so that the data is more standard and unified to manage, and the calculation efficiency can be accelerated.
In the embodiment of the present invention, the calculating the maximum eigenvalue of the judgment matrix includes:
obtaining matrix elements in the judgment matrix, and calculating the geometric average value of all matrix elements in the row of the matrix elements;
normalizing the geometric mean value to obtain mean value weight, and calculating characteristic roots according to the mean value weight and the judgment matrix;
calculating by utilizing the characteristic root and the average weight to obtain a maximum characteristic value;
the maximum eigenvalue is calculated using the following formula:
wherein ,
representing the maximum characteristic value, +_>
Indicate->
Root of personal characteristics->
Indicate->
Mean weight,/->
Representing the total number of feature roots and the mean weights.
In another optional embodiment of the present invention, after the determination matrix is established, verifying that the consistency of the determination matrix can calculate a consistency index by using the maximum eigenvalue, and calculating a consistency ratio according to the consistency index, and when the consistency ratio is smaller than a preset value, determining that the consistency of the determination matrix is acceptable;
the consistency index is calculated using the following:
wherein ,
representing the consistency index->
Representing the maximum characteristic value, +_>
Representing the total number of the feature root and the mean weight;
the consistency ratio is calculated using the formula:
wherein ,
representing said proportion of coherence, +.>
Representing the consistency index->
Indicating a preset average consistency index.
S4, auditing the maximum characteristic value to obtain a second auditing result, and integrating the first auditing result and the second auditing result to obtain a target auditing result.
In the embodiment of the present invention, the auditing the maximum feature value to obtain a second auditing result includes:
acquiring a characteristic attribute corresponding to the maximum characteristic value, and judging whether the characteristic attribute is consistent with a preset target characteristic attribute or not;
when the characteristic attribute is consistent with the target characteristic attribute, the auditing of the maximum characteristic value is passed, and the auditing is taken as a second auditing result;
and when the characteristic attribute is inconsistent with the target characteristic attribute, the auditing of the maximum characteristic value is not passed, and the auditing is not passed as a second auditing result.
In the embodiment of the present invention, integrating the first audit result and the second audit result refers to: only when the first examination result and the second examination result are both examination passing, the target examination result is passing; and under the other conditions, the target auditing result is not passed.
According to the embodiment of the invention, the original image of the electric power inspection evidence material is obtained, and the original image is subjected to region division, so that the first region and the second region can be accurately obtained, and the processing range is reduced; the material information can be accurately obtained by carrying out content identification on the first area; the auditing accuracy can be improved by auditing the material information; the factor correlation coefficient of the second area is calculated, the evaluation factor of the second area is determined according to the factor correlation coefficient, a judgment matrix is established according to the evaluation factor, and the maximum characteristic value of the judgment matrix is calculated, so that the calculated amount can be reduced, and the processing efficiency is improved; by auditing the maximum characteristic value, the electric power audit evidence material can be audited from the other side, so that the auditing accuracy is improved; the first checking result and the second checking result are integrated to obtain a target checking result, so that the accuracy and the efficiency can be improved when the material checking based on image identification is performed. Therefore, the material auditing method based on image recognition can solve the problem of lower accuracy and efficiency when the material auditing based on image recognition is carried out.
Fig. 4 is a functional block diagram of a material auditing device based on image recognition according to an embodiment of the present invention.
The image recognition-based material auditing apparatus 400 of the present invention may be installed in an electronic device. The image recognition-based material auditing apparatus 400 may include an original image dividing module 401, a material information auditing module 402, a factor correlation coefficient processing module 403, and a maximum feature value auditing module 404, according to the implemented functions. 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 original image dividing module 401 is configured to obtain an original image of the electric power inspection evidence material, and divide the original image into areas to obtain a first area and a second area;
the material information auditing module 402 is configured to perform content identification on the first area to obtain material information, and audit the material information to obtain a first auditing result;
the factor correlation coefficient processing module 403 is configured to calculate a factor correlation coefficient of the second area, determine an evaluation factor of the second area according to the factor correlation coefficient, and establish a judgment matrix according to the evaluation factor, and calculate a maximum eigenvalue of the judgment matrix, where the factor correlation coefficient of the second area is calculated using the following formula:
wherein ,
representing the factor correlation coefficient,/->
A +.o representing said second region>
Factor (F)>
A +.o representing said second region>
Factor (F)>
A factor number representing the second region;
the maximum eigenvalue auditing module 404 is configured to audit the maximum eigenvalue to obtain a second auditing result, and integrate the first auditing result and the second auditing result to obtain a target auditing result.
In detail, each module in the image recognition-based material auditing device 400 in the embodiment of the present invention adopts the same technical means as the image recognition-based material auditing method in the drawings, and can produce the same technical effects, which are not described herein.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a material auditing method based on image recognition according to an embodiment of the present invention.
The electronic device 500 may include a processor 501, a memory 502, a communication bus 503, and a communication interface 504, and may also include a computer program stored in the memory 502 and executable on the processor 501, such as an intelligent scheduler based on power supplies.
The processor 501 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 501 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., executing intelligent scheduling programs based on electric power supplies, etc.) stored in the memory 502, and calling data stored in the memory 502.
The memory 502 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 502 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 502 may also be an external storage device of the electronic device in other embodiments, for example, 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. Further, the memory 502 may also include both internal storage units and external storage devices of the electronic device. The memory 502 may be used to store not only application software installed in an electronic device and various data, such as codes of intelligent schedulers based on electric power materials, but also temporarily store data that has been output or is to be output.
The communication bus 503 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 connected communication between the memory 502 and the at least one processor 501 etc.
The communication interface 504 is used for communication between the electronic device and other devices, including network interfaces and user interfaces. 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. 5 illustrates only an electronic device having components, and it will be appreciated by those skilled in the art that the configuration illustrated in fig. 5 is not limiting of the electronic device 500 and may include fewer or more components than illustrated, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 501 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 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not 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 smart scheduler based on power supplies stored in the memory 502 of the electronic device 500 is a combination of instructions that, when executed in the processor 501, may implement:
acquiring an original image of an electric power inspection evidence material, and carrying out region division on the original image to obtain a first region and a second region;
performing content identification on the first area to obtain material information, and auditing the material information to obtain a first auditing result;
calculating a factor correlation coefficient of the second area, determining an evaluation factor of the second area according to the factor correlation coefficient, establishing a judgment matrix according to the evaluation factor, and calculating a maximum eigenvalue of the judgment matrix, wherein the factor correlation coefficient of the second area is calculated by using the following formula:
wherein ,
representing the factor correlation coefficient,/->
A +.o representing said second region>
Factor (F)>
A +.o representing said second region>
Factor (F)>
A factor number representing the second region;
and auditing the maximum characteristic value to obtain a second auditing result, and integrating the first auditing result and the second auditing result to obtain a target auditing result.
In particular, the specific implementation method of the above instruction by the processor 501 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 with the electronic device 500 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. 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 an original image of an electric power inspection evidence material, and carrying out region division on the original image to obtain a first region and a second region;
performing content identification on the first area to obtain material information, and auditing the material information to obtain a first auditing result;
calculating a factor correlation coefficient of the second area, determining an evaluation factor of the second area according to the factor correlation coefficient, establishing a judgment matrix according to the evaluation factor, and calculating a maximum eigenvalue of the judgment matrix, wherein the factor correlation coefficient of the second area is calculated by using the following formula:
wherein ,
representing the factor correlation coefficient,/->
A +.o representing said second region>
Factor (F)>
A +.o representing said second region>
Factor (F)>
A factor number representing the second region; />
And auditing the maximum characteristic value to obtain a second auditing result, and integrating the first auditing result and the second auditing result to obtain a target auditing 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 (8)
1. A method for auditing materials based on image recognition, the method comprising:
acquiring an original image of an electric power inspection evidence material, and carrying out region division on the original image to obtain a first region and a second region;
performing content identification on the first area to obtain material information, and auditing the material information to obtain a first auditing result;
calculating a factor correlation coefficient of the second area, determining an evaluation factor of the second area according to the factor correlation coefficient, establishing a judgment matrix according to the evaluation factor, and calculating a maximum eigenvalue of the judgment matrix, wherein the factor correlation coefficient of the second area is calculated by using the following formula:
wherein ,
representing the factor correlation coefficient,/->
A +.o representing said second region>
Factor (F)>
A +.o representing said second region>
Factor (F)>
A factor number representing the second region;
the establishing a judgment matrix according to the evaluation factors comprises the following steps:
randomly selecting two evaluation factors for scaling to obtain a scaling result, and numbering the scaling result to obtain a number;
until the scale and numbering among all the evaluation factors are completed, obtaining a target scale result and a target number;
taking the target scale result as a row vector, taking the target number as a column vector, and establishing a table, and taking the table as a judgment matrix;
auditing the maximum characteristic value to obtain a second auditing result, and integrating the first auditing result and the second auditing result to obtain a target auditing result;
the checking the maximum characteristic value to obtain a second checking result comprises the following steps:
acquiring a characteristic attribute corresponding to the maximum characteristic value, and judging whether the characteristic attribute is consistent with a preset target characteristic attribute or not;
when the characteristic attribute is consistent with the target characteristic attribute, the auditing of the maximum characteristic value is passed, and the auditing is taken as a second auditing result;
and when the characteristic attribute is inconsistent with the target characteristic attribute, the auditing of the maximum characteristic value is not passed, and the auditing is not passed as a second auditing result.
2. The method for auditing materials based on image recognition according to claim 1, wherein the performing region division on the original image to obtain a first region and a second region includes:
acquiring the image size of the original image, and performing proportional clipping on the original image according to the image size to obtain a clipping image;
acquiring the image category of the clipping image, and judging whether the image category is consistent with a preset standard category or not;
when the image category is consistent with the standard category, taking a clipping image corresponding to the image category as a first area;
and when the image category is inconsistent with the standard category, taking the clipping image corresponding to the image category as a second area.
3. The method for auditing materials based on image recognition according to claim 1, wherein said performing content recognition on the first region to obtain material information includes:
extracting the content of the first area to obtain text data;
and carrying out text recognition on the text data by using a preset image recognition technology to obtain material information.
4. The method for auditing materials based on image recognition according to claim 1, wherein the auditing the material information to obtain a first auditing result includes:
acquiring equipment information in the material information, and determining target financial information according to the equipment information;
calculating average financial information according to the quantity information and the financial information in the material information, and judging whether the average financial information exceeds the target financial information;
when the average financial information does not exceed the target financial information, the first audit result is that the audit is passed;
and when the average financial information exceeds the target financial information, the first audit result is that audit is not passed.
5. The image recognition-based material review method of claim 1 wherein the calculating the maximum eigenvalue of the decision matrix comprises:
obtaining matrix elements in the judgment matrix, and calculating the geometric average value of all matrix elements in the row of the matrix elements;
normalizing the geometric mean value to obtain mean value weight, and calculating characteristic roots according to the mean value weight and the judgment matrix;
calculating by utilizing the characteristic root and the average weight to obtain a maximum characteristic value;
the maximum eigenvalue is calculated using the following formula:
wherein ,
representing the maximum characteristic value, +_>
Indicate->
Root of personal characteristics->
Indicate->
Mean weight,/->
Representing the total number of feature roots and the mean weights.
6. A material auditing apparatus based on image recognition, the apparatus comprising:
the original image dividing module is used for obtaining an original image of the electric power inspection evidence material, and dividing the original image into areas to obtain a first area and a second area;
the material information auditing module is used for carrying out content identification on the first area to obtain material information, and auditing the material information to obtain a first auditing result;
the factor correlation coefficient processing module is used for calculating the factor correlation coefficient of the second area, determining the evaluation factor of the second area according to the factor correlation coefficient, establishing a judgment matrix according to the evaluation factor, and calculating the maximum eigenvalue of the judgment matrix, wherein the factor correlation coefficient of the second area is calculated by using the following formula:
wherein ,
representing the factor correlation coefficient,/->
A +.o representing said second region>
Factor (F)>
A +.o representing said second region>
Factor (F)>
A factor number representing the second region;
the establishing a judgment matrix according to the evaluation factors comprises the following steps:
randomly selecting two evaluation factors for scaling to obtain a scaling result, and numbering the scaling result to obtain a number;
until the scale and numbering among all the evaluation factors are completed, obtaining a target scale result and a target number;
taking the target scale result as a row vector, taking the target number as a column vector, and establishing a table, and taking the table as a judgment matrix;
the maximum characteristic value auditing module is used for auditing the maximum characteristic value to obtain a second auditing result, and integrating the first auditing result and the second auditing result to obtain a target auditing result;
the checking the maximum characteristic value to obtain a second checking result comprises the following steps:
acquiring a characteristic attribute corresponding to the maximum characteristic value, and judging whether the characteristic attribute is consistent with a preset target characteristic attribute or not;
when the characteristic attribute is consistent with the target characteristic attribute, the auditing of the maximum characteristic value is passed, and the auditing is taken as a second auditing result;
and when the characteristic attribute is inconsistent with the target characteristic attribute, the auditing of the maximum characteristic value is not passed, and the auditing is not passed as a second auditing result.
7. An electronic device, the electronic device 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 image recognition-based material auditing method of any of claims 1 to 5.
8. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the image recognition-based material auditing method of any of claims 1 to 5.
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