CN111489056A - Law enforcement monitoring system - Google Patents
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Abstract
The invention provides a law enforcement supervision system, which comprises a law enforcement information evaluation module, wherein the law enforcement information evaluation module is used for grading law enforcement information according to four grading items of fairness, effectiveness, normalization and scientificity; and each scoring item is respectively provided with a total score and comprises one or more scoring factors, the scoring factors matched with the law enforcement information under each scoring item are counted, then the total number of the scores of each scoring item is counted according to the corresponding scoring value of each scoring factor, and the total number of the scores is subtracted from the total score to obtain the final score of the law enforcement information under each scoring item. The constructed big data law enforcement scoring method is used for analyzing and studying the police law enforcement situation, and evaluating the justice score, the effectiveness score, the normalization score and the scientific score of the police law enforcement, so that the more professional and scientific evaluation can be made on the police law enforcement information, and the police law enforcement quality can be effectively supervised.
Description
Technical Field
The invention relates to the field of supervision, in particular to a law enforcement supervision system.
Background
Currently, the evaluation of police law enforcement action is generally carried out by using a customer evaluator, and the evaluation content can be divided into four types of 'very satisfied', 'basically satisfied', 'not satisfied' or 'satisfied', 'basically satisfied', 'not satisfied'. This evaluation does not provide a comprehensive assessment of police enforcement. At present, the evaluation aiming at the police law enforcement behaviors is mainly artificial subjective evaluation, lacks data support and sometimes evaluates the police law enforcement behaviors according to personal preference, and the police law enforcement behaviors cannot be evaluated objectively and comprehensively.
Disclosure of Invention
Aiming at the problems, the invention provides a law enforcement supervision system which comprises a law enforcement information evaluation module, a law enforcement information management module and a law enforcement information management module, wherein the law enforcement information evaluation module is used for grading law enforcement information according to four evaluation items of fairness, effectiveness, normalization and scientificity; and each scoring item is respectively provided with a total score and comprises one or more scoring factors, the scoring factors matched with the law enforcement information under each scoring item are counted, then the total number of the scores of each scoring item is counted according to the corresponding scoring value of each scoring factor, and the total number of the scores is subtracted from the total score to obtain the final score of the law enforcement information under each scoring item.
The law enforcement supervision system further comprises a law enforcement information storage module, and the law enforcement information storage module is used for storing the law enforcement information of the police.
The law enforcement supervision system further comprises a login control module, wherein the login control module is used for verifying the identity of the user using the law enforcement information evaluation module, so that only the user passing the identity verification can use the law enforcement information evaluation module.
The invention has the beneficial effects that:
according to law enforcement standards, police law enforcement conditions are analyzed and researched through a constructed big data law enforcement scoring method, the policeman law enforcement official score, the effectiveness score, the normalization score and the scientific score are evaluated, and a professional and scientific law enforcement scoring system is established. The system can make more professional and scientific evaluation on the police law enforcement information and effectively supervise the police law enforcement quality.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a diagram of an exemplary embodiment of a law enforcement surveillance system of the present invention.
Reference numerals:
the system comprises a law enforcement information storage module 1, a login control module 2 and a law enforcement information evaluation module 3.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the law enforcement supervision system of the present invention includes a law enforcement information evaluation module 3, where the law enforcement information evaluation module 3 is configured to grade law enforcement information according to four evaluation items, namely fairness, effectiveness, normalization, and scientificity; and each scoring item is respectively provided with a total score and comprises one or more scoring factors, the scoring factors matched with the law enforcement information under each scoring item are counted, then the total number of the scores of each scoring item is counted according to the corresponding scoring value of each scoring factor, and the total number of the scores is subtracted from the total score to obtain the final score of the law enforcement information under each scoring item.
In one embodiment, the law enforcement supervision system further comprises a law enforcement information storage module 1, and the law enforcement information storage module 1 is used for storing the law enforcement information of the police.
As shown in fig. 1, in one embodiment, the law enforcement surveillance system further comprises a login control module 2, wherein the login control module 2 is used for verifying the identity of the user using the law enforcement information evaluation module, so that only the authenticated user can use the law enforcement information evaluation module 3.
According to the embodiment of the invention, the login control module 2 is arranged, so that the identity of the personnel using the law enforcement information evaluation module 3 is limited, the situation that irrelevant personnel use the law enforcement information rating module 3 is avoided, the safety of the scoring of the law enforcement information is effectively ensured, and the scoring of the law enforcement information is prevented from leaking.
In one embodiment, the law enforcement supervision system further comprises a display module for displaying the scoring result.
In one embodiment, the login control module 2 includes a fingerprint identification unit, which is used to authenticate the user by means of fingerprint identification.
In another embodiment, the login control module 2 includes a face recognition unit, configured to perform identity authentication on the user through a face recognition method.
In one embodiment, the law enforcement information storage module 1 comprises a hard disk.
In one embodiment, the display module includes an O L ED display screen.
In one embodiment, the law enforcement information types include:
the record information of the examination and approval of the examiners who are in the prison of the criminal and the criminal; the approver of criminal movement approves the record information; the examination and approval record information of the examiners changed in the criminal security place; appraiser appraisal record information, dealer management record information and approver approval record information of criminal death appraisal; the management record information of the manager who keeps the article and the storage record information of the maintainer; the management record information of the manager for receiving the articles; supervision houses, beds, mutual supervision groups and the information of the examination and approval records of the approvers supervising the police changes; managing the examination and approval record information of the grade change approver; the approval record information of the approver with the change of housekeeping division; approval record information of an approver of the prison administration reward; the examination and approval record information of the examiners at prison punishment positions; the approver who endangers key criminals has approval record information; returning the re-audited dealer handling record information; returning the re-examination-postponed dealer management record information; the system comprises management record information of a manager for criminal consultation and examination and approval record information of an approver; a monitor of the family telephone monitors the recorded information; examining and approving record information of an approver of a visiting relative away from home; the examiner who registers the mail examines the record information and processes the approver approval record information; the reception record information of the reception person who externally executes the official business and the approval record information of the approver; the presenters who report the change of the penalty report the record information; temporarily giving approval record information to an approver who performs overseas execution; the management record information of the manager declared by the criminal; the management record information of the manager checked by the criminal; the management record information of the managers of the crimes; the management record information of the manager controlled by the criminal; the appraiser of the prison education assesses the record information; conversation record information of a talker for individual education; evaluating record information of an evaluator of the monitored education; the person who fills in the psychological correction result fills in the record information; the escort person who is in hospital of the criminal checks the record information; appraiser appraisal record information and auditor audit record information which are determined by a sick person; the appraiser appraisal record information and the auditor audit record information identified by the residual person; the passerby who prisoners the quilt and clothes and gives out records information through the passerby; the prisoner's obeyed purchase plan manager management record information, the auditor audit record information and the auditor audit record information; detecting the detection record information of the detector when the prisoner bedding and clothing enters or exits the warehouse; the labor post approver approves the record information; the eardrop management object builder recording information and the reward manager management recording information.
In one embodiment, the scoring the law enforcement information according to a preset rule includes:
scoring each type of law enforcement information according to four scoring items of fairness, effectiveness, normalization and scientificity;
the deduction factors of the fairness scoring items comprise complaints, accusations and biased tips; the deduction factors of the effectiveness scoring items comprise little effect; the normative deduction factor comprises a non-walking flow; the scientific deduction factor comprises lag;
the score of each deduction factor is 1, and the initial score of each scoring item is 100;
the number of the deduction factors of four scores of fairness, effectiveness, normalization and scientificity contained in each type of law enforcement information is recorded as N1,m、N2,m、N3,m、N4,mM denotes law enforcement information of type M, M ∈ [1, M]M represents the type total number of the law enforcement information;
the final total score of each score item for all types of law enforcement information is obtained as follows:
fairness score FC:
efficacy score AE:
normative score AE:
efficacy score SN:
In one embodiment, the face recognition unit comprises an image acquisition subunit, a graying processing subunit, a primary noise reduction subunit, a quality judgment subunit, a secondary noise reduction subunit, a feature extraction subunit and a feature comparison subunit;
the image acquisition subunit is used for acquiring a face image of the user;
the graying processing subunit converts the face image into a grayscale image;
the primary noise reduction subunit is used for performing primary fast noise reduction processing on the gray level image to obtain a primary noise reduction image;
the quality judgment subunit is used for calculating the primary noise reduction image, acquiring a noise reduction effect score of the primary noise reduction image, comparing the noise reduction effect score with a preset primary noise reduction threshold score, and if the noise reduction effect score is larger than the primary noise reduction threshold score, sending the primary noise reduction image to a secondary noise reduction image; otherwise, informing the image acquisition subunit to acquire the face image of the user again;
the secondary noise reduction subunit is used for carrying out secondary noise reduction processing on the primary noise reduction image to obtain a secondary noise reduction image;
the feature extraction subunit is configured to extract facial feature information of the user according to the secondary noise reduction image;
the feature comparison subunit is configured to compare the facial feature information with pre-stored feature information in a law enforcement supervision system, and calculate an error between the facial feature information and the pre-stored feature information, where if the error is smaller than a preset feature comparison threshold, the user passes face identification verification, and otherwise, the user does not pass face identification verification.
According to the embodiment of the invention, the primary processing is carried out in a fast noise reduction mode before the face image is used for identification, then whether the primary noise reduction image is usable or not is judged through the quality judgment subunit, if the noise is too much, the primary noise reduction image is unusable, and the face image is obtained again by the face identification unit, so that useless subsequent operation is avoided, and the operation amount required in the feature comparison stage is relatively large. The method can greatly increase the accuracy and the recognition speed of face recognition.
In an embodiment, the image acquisition subunit includes a camera and a light supplement lamp, the camera is used for acquiring the face image of the user, and the light supplement lamp is used for providing illumination for the camera when light is insufficient.
In one embodiment, the converting the face image into a grayscale image includes:
firstly, carrying out illumination adjustment on the face image to obtain an illumination adjustment image, and then carrying out gray processing on the illumination adjustment image to obtain a gray image;
the lighting adjustment of the face image to obtain a lighting adjustment image includes:
converting the facial image from an RGB color space to an L ab color space;
recording the position of a pixel point of the face image as (x, y), acquiring a L channel component, an a channel component and a b channel component of the pixel point with the position of (x, y) in L ab color space, and recording the positions as L (x, y), a (x, y) and b (x, y) in sequence;
l (x, y) is adjusted as follows:
wherein a L (x, y) represents L channel component at (x, y) in the face image after illumination adjustment, Nx and Ny represent column number and row number of horizontal pixel points of the face image respectively;
h represents a preset coefficient;
converting a L (x, y), a (x, y), b (x, y) from L ab color space to RGB color space, resulting in a lighting adjustment image.
In the above embodiments of the present invention, the illumination adjustment is performed before the face image is converted into the gray image, so that the problem of inaccurate feature extraction caused by uneven brightness of the image can be avoided. The above embodiment of the present invention considers the number of rows and columns of the image during the calculation, and can effectively solve the above problems.
In one embodiment, the graying the illumination adjustment image to obtain a grayed image includes:
carrying out graying processing on pixel points of the illumination adjustment image by using the following formula:
f(x,y)＝a1R(x,y)+a2G(x,y)+a3B(x,y)
in the formula, f (x, y) represents a gray scale value of a pixel point at (x, y) in the grayed image, R (x, y), G (x, y), and B (x, y) represent components of three color channels of red, green, and blue of the illumination adjustment image, respectively, a1, a2, and a3 represent weight parameters, and a1+ a2+ a3 is 1.
In one embodiment, the performing a primary fast noise reduction process on the grayscale image to obtain a primary noise-reduced image includes:
and carrying out primary fast noise reduction processing on the gray level image by using a mean value filtering mode to obtain a primary noise reduction image.
In one embodiment, the calculating the primary noise-reduced image to obtain the noise reduction effect score thereof includes:
calculating the noise reduction effect score of the primary noise reduction image by using the following formula:
in the formula, aveg represents the gray-scale average value of the primary noise-reduced image, and f (x1, y1) represents the gray-scale value of the pixel point at (x1, y1) in the primary noise-reduced image.
In one embodiment, the performing the secondary noise reduction processing on the primary noise-reduced image to obtain a secondary noise-reduced image includes:
acquiring high-frequency coefficient image GP of primary noise reduction imagecmAnd low-frequency coefficient image DP, cm ∈ [ H L H, HH]H L H, HH denote three high-frequency subband images in wavelet decomposition, respectively;
for GPblThe following operations are performed:
in the formula, aGPcmRepresenting the high frequency coefficient image after the operation, (x2, y2) representing GPcmCoordinates of the middle pixel, GPcm(x2, y2) represents the gradation value at the high-frequency coefficient image (x2, y2) before the operation; aGPcm(x2, y2) represents the gradation value at the high-frequency coefficient image (x2, y2) after the operation; threa and threb represent two preset threshold parameters, JY represents a sigmoid function;
for DP, the following operations are performed:
in the formula, aDP represents the low frequency system after calculationNumber of pictures, NBx2,y2Set of lnb × lnb-sized neighborhood pixels representing pixels with coordinates (x2, y2) in DP, and (i, j) representing NBx2,y2Coordinates of the element in (1), nbnumx2,y2Represents NBx2,y2The number of the middle elements β 1 and β 2 are weight coefficients, aDP (x2, y2) represents the gray value of the pixel with the position (x2, y2) in the low-frequency coefficient image after operation, DP (i, j) represents the gray value of the pixel with the position (i, j) in the low-frequency coefficient image before operation, avex2,y2Represents NBx2,y2Mean gray level of middle elements;
wherein g represents an adjustment coefficient, and GC represents the variance of the Gauss filter;
wherein R represents a scale coefficient, adjC (x, y) is a control function,
thr denotes the control function threshold parameter, jdx2,y2Represents NBx2,y2The number of elements with gray values larger than the pixel point with coordinates (x2, y2) in the DP; h1x2,y2、h2x2,y2Respectively represent NBx2,y2The maximum value and the minimum value of the gray scale of the element(s);
aDP high frequency coefficient image GP2 is acquiredcmAnd a low frequency coefficient image DP2, aGPcmAnd GP2cmAnd reconstructing to obtain a secondary noise reduction image.
In the above embodiment of the present invention, the GP is paired with the preset threa and threb threshold parametersblWhen the wavelet decomposition technology is used for calculating high-frequency coefficients, different operation functions are adopted for different pixel points, so that the problems that edge details are lost and the noise reduction effect is poor when the same function is used for processing the high-frequency coefficients in the conventional wavelet decomposition technology are solved. While taking into account the processing point when processing DPThe influence of the difference between the neighborhood and the processing point in space and gray value is also considered, and the influence of the Gaussian filter variance and the scale coefficient on the DP processing operation is also considered, so that the problem of image blurring when a low-frequency coefficient image is processed in the prior art is avoided.
According to law enforcement standards, the police law enforcement situation is analyzed and researched through the constructed big data law enforcement scoring method, the policeman law enforcement official score, the effectiveness score, the normalization score and the scientific score are evaluated, and a professional and scientific law enforcement scoring system is established. The system can make more professional and scientific evaluation on the police law enforcement information and effectively supervise the police law enforcement quality.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (8)
1. A law enforcement supervision system is characterized by comprising a law enforcement information evaluation module, wherein the law enforcement information evaluation module is used for scoring law enforcement information according to four scoring items of fairness, effectiveness, normalization and scientificity; and each scoring item is respectively provided with a total score and comprises one or more scoring factors, the scoring factors matched with the law enforcement information under each scoring item are counted, then the total number of the scores of each scoring item is counted according to the corresponding scoring value of each scoring factor, and the total number of the scores is subtracted from the total score to obtain the final score of the law enforcement information under each scoring item.
2. A law enforcement surveillance system according to claim 1, further comprising a law enforcement information storage module for storing the police law enforcement information.
3. A law enforcement surveillance system according to claim 1, further comprising a login control module for verifying the identity of a user using the law enforcement information evaluation module, such that only authenticated users can use the law enforcement information evaluation module.
4. The system of claim 1, further comprising a display module for displaying the scoring result.
5. A law enforcement supervision system according to claim 3, wherein the login control module comprises a face recognition unit for authenticating the user by means of face recognition.
6. The enforcement surveillance system according to claim 5, wherein the face recognition unit comprises an image acquisition subunit, a graying processing subunit, a primary noise reduction subunit, a quality determination subunit, a secondary noise reduction subunit, a feature extraction subunit, and a feature comparison subunit;
the image acquisition subunit is used for acquiring a face image of the user;
the graying processing subunit converts the face image into a grayscale image;
the primary noise reduction subunit is used for performing primary fast noise reduction processing on the gray level image to obtain a primary noise reduction image;
the quality judgment subunit is used for calculating the primary noise reduction image, acquiring a noise reduction effect score of the primary noise reduction image, comparing the noise reduction effect score with a preset primary noise reduction threshold score, and if the noise reduction effect score is larger than the primary noise reduction threshold score, sending the primary noise reduction image to a secondary noise reduction image; otherwise, informing the image acquisition subunit to acquire the face image of the user again;
the secondary noise reduction subunit is used for carrying out secondary noise reduction processing on the primary noise reduction image to obtain a secondary noise reduction image;
the feature extraction subunit is configured to extract facial feature information of the user according to the secondary noise reduction image;
the feature comparison subunit is configured to compare the facial feature information with pre-stored feature information in a law enforcement supervision system, and calculate an error between the facial feature information and the pre-stored feature information, where if the error is smaller than a preset feature comparison threshold, the user passes face identification verification, and otherwise, the user does not pass face identification verification.
7. The system of claim 6, wherein the image capturing sub-unit comprises a camera for capturing an image of the face of the user and a fill-in light for providing illumination to the camera when the light is insufficient.
8. The law enforcement surveillance system of claim 6, wherein the converting the face image into a grayscale image comprises:
firstly, carrying out illumination adjustment on the face image to obtain an illumination adjustment image, and then carrying out gray processing on the illumination adjustment image to obtain a gray image;
the lighting adjustment of the face image to obtain a lighting adjustment image includes:
converting the facial image from an RGB color space to an L ab color space;
recording the position of a pixel point of the face image as (x, y), acquiring a L channel component, an a channel component and a b channel component of the pixel point with the position of (x, y) in L ab color space, and recording the positions as L (x, y), a (x, y) and b (x, y) in sequence;
l (x, y) is adjusted as follows:
wherein a L (x, y) represents L channel component at (x, y) in the face image after illumination adjustment, Nx and Ny represent column number and row number of horizontal pixel points of the face image respectively;
h represents a preset coefficient;
converting a L (x, y), a (x, y), b (x, y) from L ab color space to RGB color space, resulting in a lighting adjustment image.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112132455A (en) * | 2020-09-22 | 2020-12-25 | 平安国际智慧城市科技股份有限公司 | Traffic law enforcement evaluation method, device, equipment and storage medium |
| CN112968859A (en) * | 2020-11-27 | 2021-06-15 | 长威信息科技发展股份有限公司 | Encryption storage system for work privacy data |
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| CN113408396A (en) * | 2021-06-15 | 2021-09-17 | 广西交科集团有限公司 | Bridge intelligent sensing system based on cloud computing |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112132455A (en) * | 2020-09-22 | 2020-12-25 | 平安国际智慧城市科技股份有限公司 | Traffic law enforcement evaluation method, device, equipment and storage medium |
| CN112968859A (en) * | 2020-11-27 | 2021-06-15 | 长威信息科技发展股份有限公司 | Encryption storage system for work privacy data |
| CN113011849A (en) * | 2021-03-24 | 2021-06-22 | 宁波莱博网络科技有限公司 | Intelligent matching system for blue collar employment and enterprise employment |
| CN113011850A (en) * | 2021-03-24 | 2021-06-22 | 宁波莱博网络科技有限公司 | Blue-collar employment matching system based on cloud computing technology |
| CN113408396A (en) * | 2021-06-15 | 2021-09-17 | 广西交科集团有限公司 | Bridge intelligent sensing system based on cloud computing |
| CN116092170A (en) * | 2023-04-06 | 2023-05-09 | 广东聚智诚科技有限公司 | Patent value analysis system based on big data technology |
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