CN113780285B - License analysis method, device and storage medium - Google Patents
License analysis method, device and storage medium Download PDFInfo
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- CN113780285B CN113780285B CN202111132367.5A CN202111132367A CN113780285B CN 113780285 B CN113780285 B CN 113780285B CN 202111132367 A CN202111132367 A CN 202111132367A CN 113780285 B CN113780285 B CN 113780285B
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- 238000004458 analytical method Methods 0.000 title claims abstract description 19
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000011156 evaluation Methods 0.000 claims abstract description 14
- 238000012552 review Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000002372 labelling Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 abstract description 3
- 238000012015 optical character recognition Methods 0.000 description 14
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/08—Auctions
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
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Abstract
The application discloses license analysis method, device and storage medium, relates to the technical field of computers, and the chassis comprises: identifying license data of the bidder license through character recognition OCR; acquiring the integrity library structured data of the scanning piece of the license uploaded by the bidder; and setting confidence coefficient for the license according to the license data and the integrity library structured data, and storing the confidence coefficient, wherein the confidence coefficient is used as a reference factor in the process of evaluating the mark. The problem that the evaluation accuracy is reduced possibly caused by the fact that OCR recognition is wrong in the prior art is solved; the effect of further evaluating the reliability of the information uploaded by the bidder by setting the confidence level and further improving the evaluation accuracy is achieved.
Description
Technical Field
The invention relates to a license analysis method, a license analysis device and a storage medium, and belongs to the technical field of computers.
Background
The benchmark data of the intelligent analysis review mainly comes from the license and the bidding document uploaded by the bidder. At present, the industry adopts OCR (optical character recognition, character recognition) technology to carry out intelligent recognition analysis on the bidder license and the bidder bidding document, the result of OCR recognition is directly used as a review basis, and a review report is obtained according to the analysis of the recognition result, so that the scoring and the checking of expert reviewers are facilitated.
The accuracy of OCR recognition directly influences the result of intelligent analysis and evaluation to a certain extent, but the accuracy of recognition can not be ensured to reach 100% by OCR recognition technical manufacturers in the market at present. Therefore, in the bid evaluation link, when the OCR recognition accuracy is not high and part of information is not recognized correctly and corrected in time, an expert reviews with wrong data, so that the accuracy of review analysis is greatly reduced, the rights and interests of bidders are damaged, and the post-bid questions and complaints of the bidders are easily caused.
Disclosure of Invention
The invention aims to provide a license analysis method, a license analysis device and a storage medium, which are used for solving the problems existing in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
according to a first aspect, an embodiment of the present invention provides a license analysis method, including:
identifying license data of the bidder license through character recognition OCR;
acquiring the integrity library structured data of the scanning piece of the license uploaded by the bidder;
and setting confidence coefficient for the license according to the license data and the integrity library structured data, and storing the confidence coefficient, wherein the confidence coefficient is used as a reference factor in the process of evaluating the mark.
Optionally, the setting a confidence level for the license according to the license data and the integrity library structured data includes:
if the license data is consistent with the structured data of the honest library, setting the confidence level of the license as the sum of a default value and a preset added value;
and if the license data is inconsistent with the structured data of the honest library, setting the confidence level of the license as the default value.
Optionally, the method further comprises:
when a bid request of a bidder is received, obtaining the confidence coefficient of the license of the bidder;
and determining whether bidding passes or not according to the confidence and whether the bidder meets bidding conditions.
Optionally, the determining whether the bidding passes according to the confidence and whether the bidder meets a bidding condition includes:
if the confidence coefficient reaches a preset threshold value and meets the bidding condition, determining that the verification passes;
if the confidence coefficient does not reach the preset threshold value and meets the bidding condition, the confidence coefficient is manually checked by an expert;
and if the confidence coefficient does not reach the preset threshold value and the bid-inviting condition is not met, determining that the verification is not passed.
Optionally, the manual auditing by the expert includes:
receiving a marking result set by an expert for the scanned piece, wherein the marking result is set after each expert rechecks the scanned piece according to a preset scoring point and the structured data of the honest library;
and modifying the confidence coefficient of the license according to the marking result.
Optionally, the modifying the confidence level of the license according to the marking result includes:
if the marking result is correct, increasing the confidence level of the license by a preset increment value;
if the marking result is wrong, the confidence coefficient of the license is reduced by a preset reduction value.
Optionally, the confidence level is set for the license according to the license data and the integrity library structured data, and after the confidence level is stored, the method further includes:
receiving a refreshing instruction for refreshing the structured data of the honest library of the license;
and after receiving the refreshing instruction, refreshing the confidence of the license.
Optionally, the refreshing the confidence level of the license includes:
refreshing the confidence coefficient of the scanned piece to be the difference value between the current confidence coefficient and the re-warehouse-in reduction value.
In a second aspect, there is provided a license analysis device comprising a memory having stored therein at least one program instruction and a processor loading and executing the at least one program instruction to implement the method of the first aspect.
In a third aspect, there is provided a computer storage medium having stored therein at least one program instruction that is loaded and executed by a processor to implement the method of the first aspect.
Identifying license data of the bidder license through character recognition OCR; acquiring the integrity library structured data of the scanning piece of the license uploaded by the bidder; and setting confidence coefficient for the license according to the license data and the integrity library structured data, and storing the confidence coefficient, wherein the confidence coefficient is used as a reference factor in the process of evaluating the mark. The problem that the evaluation accuracy is reduced possibly caused by the fact that OCR recognition is wrong in the prior art is solved; the effect of further evaluating the reliability of the information uploaded by the bidder by setting the confidence level and further improving the evaluation accuracy is achieved.
In addition, in the method, whether expert rechecking is needed or not can be determined according to the current confidence coefficient when the label is evaluated each time, and the confidence coefficient is updated in an expert rechecking mode, so that the accuracy of the confidence coefficient in the method is improved, and the evaluation accuracy is further improved.
The foregoing description is only an overview of the present invention, and is intended to provide a better understanding of the present invention, as it is embodied in the following description, with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
FIG. 1 is a flow chart of a method for license analysis according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Referring to fig. 1, a flowchart of a method for license analysis according to an embodiment of the present application is shown, and as shown in fig. 1, the method includes:
step 101, recognizing license data of a bidder license through character recognition OCR;
when the enterprise information is input into the enterprise information base, license data of the license of the bidder can be obtained through OCR recognition.
102, acquiring integrity library structured data of a scanned piece of the license uploaded by the bidder;
and step 103, setting a confidence coefficient for the license according to the license data and the integrity library structured data, and storing the confidence coefficient, wherein the confidence coefficient is used as a reference factor in the process of evaluating the mark.
If the license data is consistent with the structured data of the honest library, setting the confidence level of the license as the sum of a default value and a preset added value;
and if the license data is inconsistent with the structured data of the honest library, setting the confidence level of the license as the default value.
That is, when the enterprise information is put in storage, an initial confidence value, that is, the default value described above, may be given to the license of the enterprise, and when the license data and the structured data of the honest library are consistent, the confidence level of the license may be increased by a preset increment value based on the default value.
Optionally, the method may further include:
(1) When a bid request of a bidder is received, obtaining the confidence coefficient of the license of the bidder;
the confidence obtained here is the confidence stored above.
(2) And determining whether bidding passes or not according to the confidence and whether the bidder meets bidding conditions.
Specifically, the method comprises the following steps:
if the confidence coefficient reaches a preset threshold value and meets the bidding condition, determining that the verification passes;
the preset threshold may be a user-defined value or a default value of the system, which is not described herein.
If the confidence coefficient does not reach the preset threshold value and meets the bidding condition, the confidence coefficient is manually checked by an expert;
wherein, the manual auditing by the expert comprises:
firstly, receiving a marking result set by an expert for the scanned piece, wherein the marking result is set by each expert after rechecking the scanned piece according to a preset scoring point and the structured data of the honest library;
specifically, if the structured data of the honest library is consistent with the content on the license scanning piece, the expert marks the structured data as correct, otherwise, if the structured data of the honest library is inconsistent with the content on the license scanning piece, the expert marks the structured data as error.
Second, the confidence level of the license is modified according to the marking result.
If the marking result is correct, increasing the confidence level of the license by a preset increment value;
if the marking result is wrong, the confidence coefficient of the license is reduced by a preset reduction value.
Optionally, if a plurality of marking results are received, in a possible implementation manner, each marking result may be calculated according to the method for calculating the confidence coefficient; in another possible embodiment, the labeling results of multiple experts may be counted, the most number of labeling results is selected as the final labeling result, and the confidence is calculated according to the final labeling result in the above calculation manner.
It should be noted that after the new confidence coefficient is calculated, the already saved confidence coefficient may be refreshed according to the new confidence coefficient.
And if the confidence coefficient does not reach the preset threshold value and the bid-inviting condition is not met, determining that the verification is not passed.
That is, in the process of multiple mark evaluation, the confidence coefficient can be refreshed, so that the accuracy of the confidence coefficient of the license is improved, the accuracy of mark evaluation is improved, and the problem of evaluation errors possibly caused by OCR recognition errors in the prior art is avoided.
In addition, after the bidder uploads the integrity library structured data, the enterprise personnel may re-upload or modify the already uploaded integrity library structured data according to the personal needs, that is, in this embodiment, the integrity library structured data may be refreshed, that is, the method may further include the following steps:
firstly, receiving a refreshing instruction for refreshing the structured data of the honest library of the license;
the refresh instruction may be a re-upload instruction or a modification instruction, which is not limited thereto.
Second, after receiving the refresh command, refreshing the confidence level of the license.
Since the bidder refreshes the structured data of the honest library, it is explained that there may be a deviation in the data uploaded historically, and therefore, in this embodiment, after receiving the refresh command, the saved confidence level may be refreshed. Alternatively, the current stored confidence level may be subtracted by the restocking reduction value, and the calculated difference may be stored as the refreshed confidence level.
In summary, the license data of the bidder license is recognized through character recognition OCR; acquiring the integrity library structured data of the scanning piece of the license uploaded by the bidder; and setting confidence coefficient for the license according to the license data and the integrity library structured data, and storing the confidence coefficient, wherein the confidence coefficient is used as a reference factor in the process of evaluating the mark. The problem that the evaluation accuracy is reduced possibly caused by the fact that OCR recognition is wrong in the prior art is solved; the effect of further evaluating the reliability of the information uploaded by the bidder by setting the confidence level and further improving the evaluation accuracy is achieved.
In addition, in the method, whether expert rechecking is needed or not can be determined according to the current confidence coefficient when the label is evaluated each time, and the confidence coefficient is updated in an expert rechecking mode, so that the accuracy of the confidence coefficient in the method is improved, and the evaluation accuracy is further improved.
The application also provides a license analysis device comprising a memory and a processor, wherein at least one program instruction is stored in the memory, and the processor loads and executes the at least one program instruction to realize the method.
The present application also provides a computer storage medium having stored therein at least one program instruction that is loaded and executed by a processor to implement the method as described above.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (9)
1. A method of license analysis, the method comprising:
identifying license data of the bidder license through character recognition OCR;
acquiring the integrity library structured data of the scanning piece of the license uploaded by the bidder;
setting a confidence level for the license according to the license data and the integrity library structured data, including:
if the license data is consistent with the structured data of the honest library, setting the confidence level of the license as the sum of a default value and a preset added value;
if the license data is inconsistent with the structured data of the honest library, setting the confidence level of the license as the default value;
and storing the confidence coefficient, wherein the confidence coefficient is used as a reference factor in the evaluation of the label.
2. The method according to claim 1, wherein the method further comprises:
when a bid request of a bidder is received, obtaining the confidence coefficient of the license of the bidder;
and determining whether bidding passes or not according to the confidence and whether the bidder meets bidding conditions.
3. The method of claim 2, wherein determining whether the bid passes based on the confidence level and whether the bidder satisfies a bid condition comprises:
if the confidence coefficient reaches a preset threshold value and meets the bidding condition, determining that the verification passes;
if the confidence coefficient does not reach the preset threshold value and meets the bidding condition, the confidence coefficient is manually checked by an expert;
and if the confidence coefficient does not reach the preset threshold value and the bid-inviting condition is not met, determining that the verification is not passed.
4. A method according to claim 3, wherein said manual review by an expert comprises:
receiving a marking result set by an expert for the scanned piece, wherein the marking result is set after each expert rechecks the scanned piece according to a preset scoring point and the structured data of the honest library;
and modifying the confidence coefficient of the license according to the marking result.
5. The method of claim 4, wherein the modifying the confidence level of the license based on the labeling result comprises:
if the marking result is correct, increasing the confidence level of the license by a preset increment value;
if the marking result is wrong, the confidence coefficient of the license is reduced by a preset reduction value.
6. The method according to any one of claims 1 to 5, wherein the setting a confidence level for the license according to the license data and the integrity library structured data, and after saving the confidence level, the method further comprises:
receiving a refreshing instruction for refreshing the structured data of the honest library of the license;
and after receiving the refreshing instruction, refreshing the confidence of the license.
7. The method of claim 6, wherein the refreshing the confidence level of the license comprises:
refreshing the confidence coefficient of the scanned piece to be the difference value between the current confidence coefficient and the re-warehouse-in reduction value.
8. A license analysis device, characterized in that it comprises a memory in which at least one program instruction is stored and a processor that loads and executes the at least one program instruction to implement the method according to any of claims 1 to 7.
9. A computer storage medium having stored therein at least one program instruction that is loaded and executed by a processor to implement the method of any of claims 1 to 7.
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| CN202111132367.5A CN113780285B (en) | 2021-09-27 | 2021-09-27 | License analysis method, device and storage medium |
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| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Liu Yang Inventor after: Wang Renxu Inventor after: Zhang Jianfeng Inventor after: Wei Zhifeng Inventor after: Chen Zhou Inventor after: Zhu Bin Inventor after: He Yonglong Inventor after: Bao Rubin Inventor after: Pan Zhengfei Inventor after: Wu Bin Inventor before: Liu Yang Inventor before: Wang Renxu Inventor before: Zhang Jianfeng Inventor before: Wei Zhifeng Inventor before: Chen Zhou Inventor before: Zhu Bin Inventor before: He Yonglong Inventor before: Bao Rubin Inventor before: Pan Zhengfei Inventor before: Wu Bin |