Electronic signboard acceptance method and system based on face and fingerprint recognition

Abstract

The invention relates to the technical field of project acceptance, and discloses an electronic signboard acceptance method and system based on face and fingerprint recognition, and the method comprises the steps: obtaining the position data of each electronic signboard in a project, and determining the acceptance personnel data of the corresponding electronic signboard according to the position data of each electronic signboard; obtaining acceptance personnel fingerprint information collected by the current electronic signboard, and determining fingerprint matching parameters according to the acceptance personnel fingerprint information; obtaining acceptance personnel face information collected by the current electronic signboard, and determining face matching parameters according to the acceptance personnel face information; and evaluating the acceptance quality of the current project according to the fingerprint matching parameters and the face matching parameters of the acceptance personnel. According to the invention, the fingerprint and face information of the project acceptance personnel can be collected through the electronic signboard, the acceptance accuracy of project acceptance is effectively improved, and the probability of safety accidents is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of project acceptance, and more specifically, to a method and system for electronic signage acceptance based on face and fingerprint recognition. Background Art

[0002] Concealed engineering refers to a construction technology that uses various measures to hide and conceal the structures, equipment or lines inside buildings or facilities, as well as underground, in order to maintain the overall appearance and function of the building.

[0003] In the existing technology, the acceptance management of concealed projects mostly adopts manual inspection, manual data entry and manual quality judgment. Due to the long construction period and complex projects of railway engineering projects, different acceptance personnel are required for each construction process. Manual management and acceptance are time-consuming, labor-intensive and prone to errors. Summary of the Invention

[0004] The present invention provides a method and system for electronic signage acceptance based on face and fingerprint recognition, which is used to solve the problem of easy errors in concealed engineering acceptance in the prior art, including:

[0005] Obtain the location data of each electronic sign in the project, and determine the acceptance personnel data of the corresponding electronic sign based on the location data of each electronic sign;

[0006] Obtain the fingerprint information of the acceptance personnel collected by the current electronic signboard, and determine the fingerprint matching parameters based on the fingerprint information of the acceptance personnel;

[0007] Obtain the facial information of the acceptance personnel collected by the current electronic signboard, and determine the face matching parameters based on the facial information of the acceptance personnel;

[0008] The current project acceptance quality is evaluated based on the fingerprint matching parameters and face matching parameters of the acceptance personnel.

[0009] Furthermore, the step of determining the acceptance personnel data of the corresponding electronic signboard according to the position data of each electronic signboard includes:

[0010] Obtain construction project information and determine the construction process for the corresponding location of the electronic sign based on the construction project information;

[0011] Obtain construction site data corresponding to the location of the electronic signboard, and establish an engineering simulation model based on the construction site data and construction process;

[0012] Perform engineering simulation on the corresponding position of the electronic signboard according to the engineering simulation model, and determine the acceptance personnel data of the acceptance personnel required for the project based on the engineering simulation results.

[0013] Furthermore, establishing an engineering simulation model based on construction site data and construction process includes:

[0014] Determine image data of the construction site based on the construction site data, and determine geographic environment structure data based on the image data of the construction site;

[0015] Establish a BIM analysis model, input the construction process into the BIM analysis model, and obtain a 3D comprehensive model of the corresponding project;

[0016] The simulation linkage is carried out according to the geographical environment structural data and the 3D comprehensive model to form a simulation effect, and the linked models are integrated to obtain an engineering simulation model.

[0017] Furthermore, the step of determining fingerprint matching parameters based on the fingerprint information of the acceptance personnel includes:

[0018] Obtain the acceptance personnel database and determine the standard fingerprint information of the acceptance personnel based on the acceptance personnel database;

[0019] Extracting standard feature points from the standard fingerprint information, and drawing a standard convex hull of the standard feature points based on the standard feature points in the standard fingerprint information;

[0020] Determine the fingerprint feature points of the acceptance personnel based on the fingerprint information of the current acceptance personnel, and draw the acceptance convex hull based on the fingerprint feature points of the acceptance personnel;

[0021] The fingerprint matching parameters of the acceptance personnel are determined according to the acceptance convex hull and the standard convex hull, and the fingerprint matching parameters are determined according to the fingerprint matching parameters of the acceptance personnel.

[0022] Furthermore, the step of determining the fingerprint matching parameters of the acceptance personnel based on the acceptance convex hull and the standard convex hull includes:

[0023] Extract the center point of the acceptance convex hull and the standard convex hull, and draw several radial lines at preset angles based on the center point of the acceptance convex hull and the standard convex hull;

[0024] Calculate the length difference between the radial lines corresponding to the acceptance convex hull and the standard convex hull, and sum the length differences of all radial lines corresponding to the acceptance convex hull and the standard convex hull;

[0025] Obtaining the preset standard matching degree, calculating the ratio of the sum of the differences in the lengths of all radial lines corresponding to the acceptance convex hull and the standard convex hull to the preset standard matching degree, and obtaining the matching degree ratio;

[0026] The fingerprint matching parameters of the acceptance personnel are determined according to the ratio of the sum of the differences in the lengths of all radial lines corresponding to the acceptance convex hull and the standard convex hull to the preset standard matching degree.

[0027] Furthermore, determining face matching parameters based on the facial information of the acceptance personnel includes:

[0028] Obtain facial image information of the acceptance personnel, perform grayscale processing on the facial image information, and obtain a facial grayscale image;

[0029] Obtain a preset grid, and divide the grayscale face image into a number of grid blocks according to the preset grid;

[0030] Calculate the average grayscale value of the pixels in each grid block, and filter out shadow grid blocks whose average grayscale value is less than a first preset threshold;

[0031] Obtain the average grayscale value of the remaining grid blocks, and perform image enhancement on the filtered shadow grid blocks according to the average grayscale value of the remaining grid blocks;

[0032] The face matching parameters of the acceptance personnel are determined based on the enhanced face grayscale image.

[0033] Furthermore, performing image enhancement on the screened shadow grid blocks according to the average grayscale value of the remaining grid blocks includes:

[0034] Obtaining the grayscale value of each pixel in the shadow grid block, and screening out shadow pixels whose grayscale value is less than a second preset threshold;

[0035] The shadow pixel points are grayscale corrected according to the image enhancement formula to obtain the shadow grid block after image enhancement. The image enhancement formula is specifically as follows:

[0036]

[0037] Among them, G * is the gray value of the shadow pixel after correction, G is the original gray value of the shadow pixel, Q is the average gray value of the remaining grid blocks, and Q α is the preset standard correction coefficient, and exp is the natural exponential function.

[0038] Furthermore, the method of determining the facial matching parameters of the acceptance personnel based on the enhanced facial grayscale image includes:

[0039] Obtain historical face detection data of the electronic signboard, and determine face matching parameters between the corresponding face detection data and standard face detection data in the acceptance personnel database based on the historical face detection data of the electronic signboard;

[0040] Establishing a training sample set based on historical face detection data of electronic signboards and the corresponding face matching degree, establishing a face matching degree evaluation model based on the training sample set, and training the face matching degree evaluation model;

[0041] Obtain the enhanced face grayscale image, input the enhanced face grayscale image into the trained face matching degree evaluation model, and obtain the corresponding face matching parameters.

[0042] Furthermore, the evaluation of the current project acceptance quality based on the fingerprint matching parameters and face matching parameters of the acceptance personnel includes:

[0043] Determine the comprehensive matching parameters based on the fingerprint matching parameters and face matching parameters of all acceptance personnel, and calculate the difference between the comprehensive matching parameters and the preset standard matching parameters;

[0044] Determine whether the difference between the comprehensive matching parameter and the preset standard matching parameter is greater than a third preset threshold value, and if the difference between the comprehensive matching parameter and the preset standard matching parameter is greater than the third preset threshold value, determine that the quality of the acceptance personnel of the current project is qualified;

[0045] If the difference between the comprehensive matching parameter and the preset standard matching parameter is less than or equal to the third preset threshold, it is determined that the quality of the acceptance personnel of the current project is unqualified.

[0046] In order to achieve the above object, the present invention also provides an electronic sign acceptance system based on face and fingerprint recognition, comprising:

[0047] The first module is used to obtain the location data of each electronic sign in the project and determine the acceptance personnel data of the corresponding electronic sign according to the location data of each electronic sign;

[0048] The second module is used to obtain the fingerprint information of the acceptance personnel collected by the current electronic signboard and determine the fingerprint matching parameters based on the fingerprint information of the acceptance personnel;

[0049] The third module is used to obtain the facial information of the acceptance personnel collected by the current electronic signboard and determine the face matching parameters based on the facial information of the acceptance personnel;

[0050] The fourth module is used to evaluate the quality of the current project acceptance based on the fingerprint matching parameters and face matching parameters of the acceptance personnel.

[0051] The beneficial effects of the present invention are:

[0052] By applying the above technical solution, the present invention obtains the location data of each electronic signboard in the project, determines the acceptance personnel data corresponding to the electronic signboard based on the location data of each electronic signboard; obtains the acceptance personnel's fingerprint information collected by the current electronic signboard, and determines the fingerprint matching parameters based on the acceptance personnel's fingerprint information; obtains the acceptance personnel's facial information collected by the current electronic signboard, and determines the facial matching parameters based on the acceptance personnel's facial information; and evaluates the current project acceptance quality based on the acceptance personnel's fingerprint matching parameters and facial matching parameters. The ability to collect the fingerprint and facial information of project acceptance personnel through electronic signboards effectively improves the accuracy of project acceptance, prevents acceptance personnel errors caused by personnel changes during the construction process, and reduces the probability of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 A schematic diagram showing a flow chart of an electronic signage acceptance method based on face and fingerprint recognition proposed in an embodiment of the present invention;

[0055] Figure 2 The figure shows the overall structure of an electronic signage acceptance system based on face and fingerprint recognition proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0057] The present application embodiment provides an electronic sign acceptance method based on face and fingerprint recognition, such as Figure 1 Shown, including:

[0058] S101, obtaining the location data of each electronic signboard in the project, and determining the acceptance personnel data of the corresponding electronic signboard according to the location data of each electronic signboard;

[0059] In some embodiments of the present application, the method of determining the acceptance personnel data corresponding to the electronic signboard based on the location data of each electronic signboard includes: obtaining construction project information, and determining the construction process of the corresponding position of the electronic signboard based on the construction project information; obtaining construction site data of the corresponding position of the electronic signboard, and establishing an engineering simulation model based on the construction site data and the construction process; performing engineering simulation on the corresponding position of the electronic signboard based on the engineering simulation model, and determining the acceptance personnel data of the acceptance personnel required for the project based on the engineering simulation results.

[0060] In this embodiment, electronic signboards are set up at the construction site of the concealed project. The acceptance personnel start the acceptance work after registering with the electronic signboards. Different engineering simulation models are established through electronic signboards at different locations, so that the acceptance personnel data required for each construction process can be determined through simulation.

[0061] In some embodiments of the present application, the engineering simulation model is established based on the construction site data and the construction process, including: determining the image data of the construction site based on the construction site data, and determining the geographical environment structure data based on the image data of the construction site; establishing a BIM analysis model, inputting the construction process into the BIM analysis model, and obtaining a 3D comprehensive model of the corresponding project; performing simulation linkage based on the geographical environment structure data and the 3D comprehensive model to form a simulation effect, and integrating the linked models to obtain an engineering simulation model.

[0062] In this embodiment, the construction site is simulated by combining the geographic structure data before construction and the 3D comprehensive model after construction to obtain the corresponding engineering simulation model. The acceptance personnel data of the corresponding construction process is determined through the engineering simulation model and stored in the acceptance personnel database.

[0063] S102, obtaining the fingerprint information of the acceptance personnel collected by the current electronic signboard, and determining the fingerprint matching parameters according to the fingerprint information of the acceptance personnel;

[0064] In some embodiments of the present application, the determination of fingerprint matching parameters based on the fingerprint information of the acceptance personnel includes: obtaining an acceptance personnel database, determining the standard fingerprint information of the acceptance personnel based on the acceptance personnel database; extracting standard feature points in the standard fingerprint information, and drawing the standard convex hull of the standard feature points based on the standard feature points in the standard fingerprint information; determining the fingerprint feature points of the acceptance personnel based on the fingerprint information of the current acceptance personnel, and drawing the acceptance convex hull based on the fingerprint feature points of the acceptance personnel; determining the fingerprint matching parameters of the acceptance personnel based on the acceptance convex hull and the standard convex hull, and determining the fingerprint matching parameters based on the fingerprint matching parameters of the acceptance personnel.

[0065] In some embodiments of the present application, the fingerprint matching parameters of the acceptance personnel are determined based on the acceptance convex hull and the standard convex hull, including: extracting the center points of the acceptance convex hull and the standard convex hull, drawing a number of radial lines at preset angles based on the center points of the acceptance convex hull and the standard convex hull; calculating the length difference of the radial lines corresponding to the acceptance convex hull and the standard convex hull, and summing the length differences of all radial lines corresponding to the acceptance convex hull and the standard convex hull; obtaining the preset standard matching degree, calculating the ratio of the sum of the length differences of all radial lines corresponding to the acceptance convex hull and the standard convex hull to the preset standard matching degree, and obtaining the matching degree ratio; determining the fingerprint matching parameters of the acceptance personnel based on the ratio of the sum of the length differences of all radial lines corresponding to the acceptance convex hull and the standard convex hull to the preset standard matching degree.

[0066] In this embodiment, the fingerprint information of the on-site acceptance personnel is extracted from the feature points of the standard fingerprint information in the acceptance personnel database, the extracted feature points are connected to obtain the acceptance convex hull, and the standard convex hull is matched with the acceptance convex hull to obtain the fingerprint matching parameters.

[0067] S103, obtaining the facial information of the acceptance personnel collected by the current electronic signboard, and determining the facial matching parameters according to the facial information of the acceptance personnel;

[0068] In some embodiments of the present application, determining the facial matching parameters based on the facial information of the acceptance personnel includes: obtaining facial image information of the acceptance personnel, grayscale processing the facial image information, and obtaining a facial grayscale image; obtaining a preset grid, and dividing the facial grayscale image into a number of grid blocks according to the preset grid; calculating the average grayscale value of the pixel points in each grid block, and screening out shadow grid blocks whose average grayscale value is less than a first preset threshold; obtaining the average grayscale value of the remaining grid blocks, and performing image enhancement on the screened shadow grid blocks according to the average grayscale value of the remaining grid blocks; and determining the facial matching parameters of the acceptance personnel based on the facial grayscale image after image enhancement.

[0069] In some embodiments of the present application, the image enhancement of the filtered shadow grid block according to the average grayscale value of the remaining grid blocks includes: obtaining the grayscale value of each pixel in the shadow grid block, filtering out shadow pixels whose grayscale value is less than a second preset threshold; performing grayscale correction on the shadow pixels according to an image enhancement formula to obtain an image-enhanced shadow grid block, wherein the image enhancement formula is specifically:

[0070]

[0071] Among them, G * is the gray value of the shadow pixel after correction, G is the original gray value of the shadow pixel, Q is the average gray value of the remaining grid blocks, and Q α is the preset standard correction coefficient, and exp is the natural exponential function.

[0072] In this embodiment, the grayscale value of the shadow grid block is corrected by the average grayscale value of the remaining grid blocks, thereby effectively reducing the impact of the shadow and improving the accuracy of face recognition.

[0073] In some embodiments of the present application, determining the facial matching parameters of the acceptance personnel based on the image-enhanced facial grayscale image includes: obtaining historical face detection data of the electronic signboard, and determining the facial matching parameters of the corresponding face detection data and the standard face detection data in the acceptance personnel database based on the historical face detection data of the electronic signboard; establishing a training sample set based on the historical face detection data of the electronic signboard and the corresponding face matching degree, establishing a face matching degree evaluation model based on the training sample set and training the face matching degree evaluation model; obtaining the image-enhanced facial grayscale image, inputting the image-enhanced facial grayscale image into the trained face matching degree evaluation model to obtain the corresponding face matching parameters.

[0074] In this embodiment, a CNN neural network is trained based on historical face detection data and face matching parameters of a large number of electronic signboards, such as an SSD (Single Shot MultiBox Detector) neural network. The loss function used is a cross-entropy loss function. The face matching parameters are evaluated by the trained CNN neural network to obtain the face matching parameters.

[0075] S104: Evaluate the quality of the current project acceptance based on the fingerprint matching parameters and face matching parameters of the acceptance personnel.

[0076] In some embodiments of the present application, the evaluation of the current project acceptance quality based on the fingerprint matching parameters and face matching parameters of the acceptance personnel includes: determining the comprehensive matching parameters based on the fingerprint matching parameters and face matching parameters of all acceptance personnel, and calculating the difference between the comprehensive matching parameters and the preset standard matching parameters; judging whether the difference between the comprehensive matching parameters and the preset standard matching parameters is greater than a third preset threshold value; if the difference between the comprehensive matching parameters and the preset standard matching parameters is greater than the third preset threshold value, determining that the quality of the acceptance personnel of the current project is qualified; if the difference between the comprehensive matching parameters and the preset standard matching parameters is less than or equal to the third preset threshold value, determining that the quality of the acceptance personnel of the current project is unqualified.

[0077] In this embodiment, the matching parameters of the acceptance personnel are obtained by calculating the sum of the fingerprint matching parameters and the face matching parameters of the acceptance personnel, and the comprehensive matching parameters are obtained by calculating the sum of the matching parameters of all acceptance personnel. The quality evaluation of the acceptance personnel is performed based on the difference between the comprehensive matching parameters and the preset standard matching parameters.

[0078] Based on the same technical concept, such as Figure 2 As shown, the present invention also provides an electronic sign acceptance system based on face and fingerprint recognition, comprising:

[0079] The first module is used to obtain the location data of each electronic signboard in the project, and determine the acceptance personnel data corresponding to the electronic signboard based on the location data of each electronic signboard; the second module is used to obtain the fingerprint information of the acceptance personnel collected by the current electronic signboard, and determine the fingerprint matching parameters based on the fingerprint information of the acceptance personnel; the third module is used to obtain the facial information of the acceptance personnel collected by the current electronic signboard, and determine the facial matching parameters based on the facial information of the acceptance personnel; the fourth module is used to evaluate the current project acceptance quality based on the fingerprint matching parameters and facial matching parameters of the acceptance personnel.

[0080] By applying the above technical solution, the present invention obtains the location data of each electronic sign in the project, determines the acceptance personnel data of the corresponding electronic sign based on the location data of each electronic sign; obtains the acceptance personnel's fingerprint information collected by the current electronic sign, and determines the fingerprint matching parameters based on the acceptance personnel's fingerprint information; obtains the acceptance personnel's facial information collected by the current electronic sign, and determines the facial matching parameters based on the acceptance personnel's facial information; and evaluates the current project acceptance quality based on the acceptance personnel's fingerprint matching parameters and facial matching parameters. The present invention can collect the fingerprint and facial information of project acceptance personnel through electronic sign, effectively improving the acceptance accuracy of the project and reducing the probability of safety accidents.

[0081] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented through hardware or by using software plus the necessary general hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) and includes a number of instructions for enabling a computer device (such as a personal computer, a server, or a network device) to execute the methods described in various implementation scenarios of the present invention.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for accepting electronic signage based on face and fingerprint recognition, characterized in that: The method comprises: Obtain the location data of each electronic sign in the project, and determine the acceptance personnel data of the corresponding electronic sign based on the location data of each electronic sign; Obtain the fingerprint information of the acceptance personnel collected by the current electronic signboard, and determine the fingerprint matching parameters based on the fingerprint information of the acceptance personnel; Obtain the facial information of the acceptance personnel collected by the current electronic signboard, and determine the face matching parameters based on the facial information of the acceptance personnel; The current project acceptance quality is evaluated based on the fingerprint matching parameters and face matching parameters of the acceptance personnel.

2. The electronic signage acceptance method based on face and fingerprint recognition according to claim 1 is characterized in that: The method of determining the acceptance personnel data of the corresponding electronic signboard according to the position data of each electronic signboard includes: Obtain construction project information and determine the construction process for the corresponding location of the electronic sign based on the construction project information; Obtain construction site data corresponding to the location of the electronic signboard, and establish an engineering simulation model based on the construction site data and construction process; Perform engineering simulation on the corresponding position of the electronic signboard according to the engineering simulation model, and determine the acceptance personnel data of the acceptance personnel required for the project based on the engineering simulation results.

3. The electronic signage acceptance method based on face and fingerprint recognition according to claim 2 is characterized in that: The engineering simulation model is established based on the construction site data and construction process, including: Determine image data of the construction site based on the construction site data, and determine geographic environment structure data based on the image data of the construction site; Establish a BIM analysis model, input the construction process into the BIM analysis model, and obtain a 3D comprehensive model of the corresponding project; The simulation linkage is carried out according to the geographical environment structural data and the 3D comprehensive model to form a simulation effect, and the linked models are integrated to obtain an engineering simulation model.

4. The electronic signage acceptance method based on face and fingerprint recognition according to claim 3 is characterized in that: Determining fingerprint matching parameters based on the fingerprint information of the acceptance personnel includes: Obtain the acceptance personnel database and determine the standard fingerprint information of the acceptance personnel based on the acceptance personnel database; Extracting standard feature points from the standard fingerprint information, and drawing a standard convex hull of the standard feature points based on the standard feature points in the standard fingerprint information; Determine the fingerprint feature points of the acceptance personnel based on the fingerprint information of the current acceptance personnel, and draw the acceptance convex hull based on the fingerprint feature points of the acceptance personnel; The fingerprint matching parameters of the acceptance personnel are determined according to the acceptance convex hull and the standard convex hull, and the fingerprint matching parameters are determined according to the fingerprint matching parameters of the acceptance personnel.

5. The electronic signage acceptance method based on face and fingerprint recognition according to claim 4 is characterized in that: Determining the fingerprint matching parameters of the acceptance personnel based on the acceptance convex hull and the standard convex hull includes: Extract the center point of the acceptance convex hull and the standard convex hull, and draw several radial lines at preset angles based on the center point of the acceptance convex hull and the standard convex hull; Calculate the length difference between the radial lines corresponding to the acceptance convex hull and the standard convex hull, and sum the length differences of all radial lines corresponding to the acceptance convex hull and the standard convex hull; Obtaining the preset standard matching degree, calculating the ratio of the sum of the differences in the lengths of all radial lines corresponding to the acceptance convex hull and the standard convex hull to the preset standard matching degree, and obtaining the matching degree ratio; The fingerprint matching parameters of the acceptance personnel are determined according to the ratio of the sum of the differences in the lengths of all radial lines corresponding to the acceptance convex hull and the standard convex hull to the preset standard matching degree.

6. The electronic signage acceptance method based on face and fingerprint recognition according to claim 5 is characterized in that: Determining face matching parameters based on the facial information of the acceptance personnel includes: Obtain facial image information of the acceptance personnel, perform grayscale processing on the facial image information, and obtain a facial grayscale image; Obtain a preset grid, and divide the grayscale face image into a number of grid blocks according to the preset grid; Calculate the average grayscale value of the pixels in each grid block, and filter out shadow grid blocks whose average grayscale value is less than a first preset threshold; Obtain the average grayscale value of the remaining grid blocks, and perform image enhancement on the filtered shadow grid blocks according to the average grayscale value of the remaining grid blocks; The face matching parameters of the acceptance personnel are determined based on the enhanced face grayscale image.

7. The electronic signage acceptance method based on face and fingerprint recognition according to claim 6 is characterized in that: The image enhancement of the filtered shadow grid blocks according to the average grayscale value of the remaining grid blocks includes: Obtaining the grayscale value of each pixel in the shadow grid block, and screening out shadow pixels whose grayscale value is less than a second preset threshold; The shadow pixel points are grayscale corrected according to the image enhancement formula to obtain the shadow grid block after image enhancement. The image enhancement formula is specifically as follows: Among them, G * is the gray value of the shadow pixel after correction, G is the original gray value of the shadow pixel, Q is the average gray value of the remaining grid blocks, and Q α is the preset standard correction coefficient, and exp is the natural exponential function.

8. The electronic signage acceptance method based on face and fingerprint recognition according to claim 7 is characterized in that: The method of determining the face matching parameters of the acceptance personnel based on the enhanced face grayscale image includes: Obtain historical face detection data of the electronic signboard, and determine face matching parameters between the corresponding face detection data and standard face detection data in the acceptance personnel database based on the historical face detection data of the electronic signboard; Establishing a training sample set based on historical face detection data of electronic signboards and the corresponding face matching degree, establishing a face matching degree evaluation model based on the training sample set, and training the face matching degree evaluation model; Obtain the enhanced face grayscale image, input the enhanced face grayscale image into the trained face matching degree evaluation model, and obtain the corresponding face matching parameters.

9. The electronic signage acceptance method based on face and fingerprint recognition according to claim 8 is characterized in that: The evaluation of the current project acceptance quality based on the fingerprint matching parameters and face matching parameters of the acceptance personnel includes: Determine the comprehensive matching parameters based on the fingerprint matching parameters and face matching parameters of all acceptance personnel, and calculate the difference between the comprehensive matching parameters and the preset standard matching parameters; Determine whether the difference between the comprehensive matching parameter and the preset standard matching parameter is greater than a third preset threshold value, and if the difference between the comprehensive matching parameter and the preset standard matching parameter is greater than the third preset threshold value, determine that the quality of the acceptance personnel of the current project is qualified; If the difference between the comprehensive matching parameter and the preset standard matching parameter is less than or equal to the third preset threshold, it is determined that the quality of the acceptance personnel of the current project is unqualified.

10. An electronic signage acceptance system based on face and fingerprint recognition, characterized in that: include: The first module is used to obtain the location data of each electronic sign in the project and determine the acceptance personnel data of the corresponding electronic sign according to the location data of each electronic sign; The second module is used to obtain the fingerprint information of the acceptance personnel collected by the current electronic signboard and determine the fingerprint matching parameters based on the fingerprint information of the acceptance personnel; The third module is used to obtain the facial information of the acceptance personnel collected by the current electronic signboard and determine the face matching parameters based on the facial information of the acceptance personnel; The fourth module is used to evaluate the quality of the current project acceptance based on the fingerprint matching parameters and face matching parameters of the acceptance personnel.

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