A standardized examination room intelligent management system and method based on artificial intelligence

Abstract

The present invention discloses a standardized examination room intelligent management system and method based on artificial intelligence, belonging to the field of artificial intelligence technology. It includes: obtaining a target examination room, which includes a number of monitoring devices and examination room seats, obtaining each monitoring device, and obtaining a characteristic examination room seat corresponding to the monitoring device; obtaining a monitoring combination, and determining a correlation coefficient corresponding to the monitoring combination; using the monitoring device corresponding to the picture being displayed on the monitoring large screen as the target device, and determining the picture display duration of the target device; determining the monitoring device corresponding to the next picture display of the monitoring large screen as the next monitoring device, and determining the picture display duration of the next monitoring device. According to the actual situation of the target examination room, the present invention analyzes the characteristic examination room seats corresponding to each monitoring device in the target examination room, which not only effectively improves the efficiency of monitoring screen switching, but also reasonably sets the picture display sequence and display duration of the monitoring device.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based standardized examination room intelligent management system and method. Background Art

[0002] With the continuous advancement and popularization of educational informatization and intelligent examination room, the importance of monitoring in modern education examinations, especially large-scale examinations, is unquestionable. Monitoring cameras can not only monitor the behavior of examinees in real time and reduce the risk of examinees cheating, but also review the examination after the examination. It is of great significance to analyze the behavior of examinees. Monitoring the examination room through monitoring cameras not only saves manpower, but also enables fast switching of multiple scenes. At present, an examination room generally includes multiple monitoring cameras, and the display mode of the monitoring screen mainly includes fixed display of monitoring pictures and switching to display the pictures of each monitoring in the examination room in turn, which allows the invigilator to quickly browse the real-time situation of the examination room. However, for switching the monitoring pictures in turn, only manual operation or simple timed switching is currently used. Switching not only requires multiple operations, which is not efficient enough, but also does not set the display order and display duration of the monitoring pictures according to the actual situation of the examination room. Therefore, according to the actual situation of the examination room, automatically adjusting the display order and display duration of each monitoring picture in the examination room has a significant impact on the examination in the examination room. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent management system and method for standardized examination rooms based on artificial intelligence to solve the problems raised in the above-mentioned background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] An intelligent management method for standardized examination rooms based on artificial intelligence comprises the following steps:

[0006] Step S100: obtaining a target examination room, which includes a number of monitoring devices and examination room seats, obtaining a monitoring screen corresponding to each monitoring device, which includes a number of examination room seats, and obtaining a number of characteristic examination room seats corresponding to each monitoring device;

[0007] This solution is to analyze the target examination room. Usually, in order to ensure comprehensive monitoring without blind spots, all examination areas can be covered. And because the image has the characteristics of large near and small far away, the image of the seat area farther away from the monitoring is more blurred, which can easily lead to wrong judgment by the invigilator, so there will be multiple monitoring devices in one examination room. This step establishes a three-dimensional coordinate system for the target examination room, and determines the coordinate points corresponding to each monitoring device and the examination room seat in the three-dimensional coordinate system, and analyzes it in combination with the monitoring image to determine the characteristic examination room seat corresponding to the monitoring device, where the characteristic examination room seat is the examination room seat that can be clearly seen in the monitoring image and accurately judge the behavior of the examinee;

[0008] Step S200: any two monitoring devices are taken as a monitoring combination to obtain a plurality of monitoring combinations, and according to the characteristic examination room seats corresponding to each monitoring combination, all characteristic punctuation points corresponding to each monitoring combination in the target examination room are determined, and then the correlation coefficient corresponding to each monitoring combination is determined;

[0009] Step S300: taking the monitoring device corresponding to the picture being displayed on the monitoring screen as the target device, and determining the picture display time of the target device according to the characteristic examination room seat in the target device;

[0010] Step S400: According to the correlation coefficient between the target device and the other monitoring devices in the target examination room, determine the monitoring device corresponding to the next screen display of the monitoring screen as the next monitoring device, and determine the screen display duration of the next monitoring device according to the characteristic examination room seat corresponding to the target device and the next monitoring device.

[0011] Furthermore, step S100 includes:

[0012] Step S110: Obtain the specific location of each monitoring device and examination room seat in the target examination room, and establish a three-dimensional coordinate system of the target examination room, the three-dimensional coordinate system includes the coordinate points corresponding to each monitoring device and examination room seat; obtain the monitoring device ME, the monitoring screen corresponding to the monitoring device ME is G ME , monitoring screen G ME The monitoring screen G is obtained through the pre-trained neural network model. ME Each examination room seat in the test is used as a marked examination room seat, and the coordinate points corresponding to the monitoring device ME and each marked examination room seat are marked in the three-dimensional coordinate system;

[0013] Step S120: Monitor the screen G ME Connect the upper left corner and the lower right corner, and the lower left corner and the upper right corner, and the intersection point is the center point CP of the picture. ME , establish monitoring screen G ME The corresponding two-dimensional coordinate system obtains the center point CP of the picture MEand the coordinate points corresponding to each marked examination room seat in the two-dimensional coordinate system, and then obtain the marked examination room seat closest to the center point CP of the screen in the two-dimensional coordinate system ME which is denoted as the first examination room seat TS1;

[0014] Step S130: Calculate the average value based on the coordinate points corresponding to each marked examination room seat in the three-dimensional coordinate system to obtain the seat center point CP TS , and then obtain the marked examination room seat closest to the seat center point CP TS in the three-dimensional coordinate system, which is denoted as the second examination room seat TS2; According to the coordinate points corresponding to the first examination room seat TS1 and the second examination room seat TS2 in the three-dimensional coordinate system, obtain the coordinate distance CD between the first examination room seat TS1 and the second examination room seat TS2. In the three-dimensional coordinate system, the coordinate distances from the monitoring device ME to the first examination room seat TS1 and the second examination room seat TS2 are CD1 and CD2 respectively;

[0015] Step S140: If the coordinate distance CD < CD0, where CD0 is the coordinate distance threshold, obtain the maximum coordinate distance CD m = max(CD 1， CD2), obtain the coordinate distances from the monitoring device ME to each marked examination room seat, and use the marked examination room seats corresponding to the coordinate distances less than the maximum coordinate distance CD m as the characteristic examination room seats; If the coordinate distance CD ≥ CD0, obtain the coordinate distances from the monitoring device ME to each marked examination room seat, and use the marked examination room seats corresponding to the coordinate distances less than CD2 as the characteristic examination room seats.

[0016] Furthermore, step S200 includes:

[0017] Step S210: Obtain a certain monitoring combination MA, where a certain monitoring combination MA includes a first monitoring device ME1 and a second monitoring device ME2, and use the same characteristic examination room seats in the first monitoring device ME1 and the second monitoring device ME2 as the common characteristic examination room seats to obtain all the common characteristic examination room seats corresponding to a certain monitoring combination MA; Obtain, in the three-dimensional coordinate system, a first vector with the coordinate corresponding to a certain common characteristic examination room seat CTS as the starting point and the coordinate corresponding to the first monitoring device ME1 as the ending point and a second vector with the coordinate corresponding to a certain common characteristic examination room seat CTS as the starting point and the coordinate corresponding to the second monitoring device ME2 as the ending point If the angle between the vectors and is greater than the angle threshold or the value of is less than the modulus distance threshold, then use the common characteristic examination room seat CTS as the characteristic punctuation in a certain monitoring combination MA in the target examination room; Furthermore, obtain all the characteristic punctuations in a certain monitoring combination MA in the target examination room;

[0018] Step S220: Obtain a first seat vector starting from the coordinates corresponding to a common characteristic examination room seat CTS1 corresponding to a certain monitoring combination MA and ending at the coordinates corresponding to the first monitoring device ME1, and a second seat vector starting from the coordinates corresponding to a common characteristic examination room seat CTS1 and ending at the coordinates corresponding to the second monitoring device ME2, and use the angle between the two seat vectors as the seat angle of a common characteristic examination room seat CTS1, and then obtain the seat angle of each common characteristic examination room seat corresponding to a certain monitoring combination MA, and calculate the average value to obtain Obtain a first eigenvector with the coordinates of a certain characteristic punctuation point CM1 corresponding to a certain monitoring combination MA as the starting point and the coordinates of the first monitoring device ME1 as the end point, and a second eigenvector with the coordinates of a certain characteristic punctuation point CM1 as the starting point and the coordinates of the second monitoring device ME2 as the end point, and use the angle between the two eigenvectors as the characteristic angle of a certain characteristic punctuation point CM1, and then obtain the characteristic angle of each characteristic punctuation point corresponding to a certain monitoring combination MA, and calculate the average value;

[0019] Step S230: Obtain the coordinates of a common characteristic examination room seat CTS1 corresponding to a monitoring combination MA, the first seat distance of the coordinates corresponding to the first monitoring device ME1, and the coordinates of a common characteristic examination room seat CTS1, the second seat distance of the coordinates corresponding to the second monitoring device ME2, and add the two seat distances to obtain the seat distance corresponding to the common characteristic examination room seat CTS1, and then obtain the seat distance corresponding to each common characteristic examination room seat corresponding to a monitoring combination MA, and calculate the average value to obtain Obtain the coordinates of a certain characteristic point CM1 corresponding to a certain monitoring combination MA, the first characteristic distance of the coordinates corresponding to the first monitoring device ME1, and the coordinates of a certain characteristic point CM1, the second characteristic distance of the coordinates corresponding to the second monitoring device ME2, and add the two characteristic distances to obtain the characteristic distance corresponding to the characteristic point CM1, and then obtain the characteristic distance corresponding to each characteristic point corresponding to a certain monitoring combination MA, and calculate the average value to obtain

[0020] Step S240: Set the weight corresponding to the common feature examination room seat to α, and the weight corresponding to the characteristic punctuation point to β, and obtain

[0021] Then, according to the common characteristic number of examination room seats N1 and characteristic punctuation marks N2 corresponding to the monitoring combination, where N1≥N2, the correlation coefficient COF=α*N1+(β-α)*N2 corresponding to the monitoring combination is obtained and normalized.

[0022] The common characteristic examination room seats are only the common characteristic examination room seats corresponding to the two monitoring devices. If the examination room seats are far apart and difficult to be found in the monitoring, they may also become common characteristic examination room seats. However, the characteristic punctuation is used to screen the common characteristic examination room seats. The characteristic examination room seats that are closer to the two monitoring devices have greater reference significance and are also easier to find in the process of switching from one monitoring screen to another. Therefore, the weight β of the characteristic punctuation should not be less than the weight α of the common characteristic examination room seats.

[0023] Furthermore, step S300 includes:

[0024] Step S310: Obtain the coordinate point corresponding to each marked examination room seat corresponding to the target device TME in the three-dimensional coordinate system, take the number of marked examination room seats corresponding to the target device TME as W1, set a rectangular range with a length of L1 and a width of L2, take the coordinate point corresponding to the marked examination room seat MTS as the center point of the rectangular range, and rotate the rectangular range clockwise around the center point, with a rotation angle of θ 转 If, during the rotation process, there are always coordinate points corresponding to other marked examination room seats within the rectangular range, the marked examination room seats MTS will be marked twice, and the number of all the twice marked examination room seats will be recorded as W2, and the distribution anomaly coefficient is: X TME =W2 / W1, where 0≤X TME ≤1;

[0025] Step S320: Obtain the historical timed switching interval length T, and use the number of seats in the characteristic examination room corresponding to the target device TME as Y TME , determine the screen display time t of the target device TME TME =(1+X TME / X0)(Y TME / Y0)*T, and when t TME <T-T0 or t TME >T+T0, let t TME =T-T0 or t TME =T+T0, where X0 is the distribution anomaly coefficient threshold, Y0 is the characteristic examination room seat number threshold, and T0 is the interval time threshold.

[0026] This solution analyzes the seat distribution of the target examination room and the number of seats in the characteristic examination room. Since the rationality of the seat layout is related to the cheating behavior of candidates, a reasonable seat layout can reduce the chance of cheating by candidates. Therefore, when the distribution anomaly coefficient X TME When it is smaller, the screen display time is t TME should also be small; and because the number of examination room seats is too small, the number of observable candidates is also small, so when the characteristic examination room seat number Y TMEWhen it is smaller, the screen display time is t TME It should also be smaller.

[0027] Furthermore, step S400 includes:

[0028] Step S410: According to the correlation coefficients corresponding to the target device TME and the other monitoring devices, if the correlation coefficients between the target device TME and the other monitoring devices are all 0, obtain the monitoring device closest to the target device TME in the three-dimensional coordinate system as the monitoring device displayed on the next screen; otherwise, if the correlation coefficients between the target device TME and the other monitoring devices are not all 0, use the monitoring device corresponding to the largest correlation coefficient as the monitoring device displayed on the next screen of the monitoring large screen; record the monitoring device displayed on the next screen as the monitoring device NME;

[0029] Step S420: Obtain the number of seats Y in the characteristic examination room corresponding to the target device TME and the monitoring device NME TME and Y NME , and according to the screen display time t of the target device TME TME , determine the display time t of the monitoring device NME NME =t TME *Y NME / Y TME , and when t NME <T-T0 or t NME >T+T0, let t NME =T-T0 or t NME =T+T0, where T is the historical timed switching interval and T0 is the interval threshold.

[0030] An artificial intelligence-based standardized examination room intelligent management system, comprising a characteristic examination room seat module, a correlation coefficient module, a target device module and a screen display duration module;

[0031] Characteristic examination room seat module: used to obtain the target examination room, which includes several monitoring devices and examination room seats, obtain the monitoring screen corresponding to each monitoring device, the monitoring screen includes several examination room seats, and obtain several characteristic examination room seats corresponding to each monitoring device;

[0032] Correlation coefficient module: used to take any two monitoring devices as a monitoring combination, obtain several monitoring combinations, and determine all the characteristic punctuation points corresponding to each monitoring combination in the target examination room according to the characteristic examination room seats corresponding to each monitoring combination, and then determine the correlation coefficient corresponding to each monitoring combination;

[0033] Target device module: used to take the monitoring device corresponding to the image being displayed on the monitoring screen as the target device, and determine the image display time of the target device according to the characteristic examination room seat in the target device;

[0034] Screen display duration module: used to determine the monitoring device corresponding to the next screen display of the monitoring screen as the next monitoring device based on the correlation coefficient between the target device and the other monitoring devices in the target examination room, and determine the screen display duration of the next monitoring device based on the characteristic examination room seat corresponding to the target device and the next monitoring device.

[0035] Furthermore, the correlation coefficient module includes a characteristic punctuation unit, an angle average unit, a distance average unit and a correlation coefficient unit;

[0036] Characteristic punctuation unit: used to take any two monitoring devices as a monitoring combination, obtain several monitoring combinations, and obtain all common characteristic examination room seats corresponding to the monitoring combination according to the characteristic examination room seats corresponding to each monitoring combination, and then determine all characteristic punctuations corresponding to each monitoring combination in the target examination room;

[0037] Angle average unit: used to obtain the angle average corresponding to the common characteristic examination room seats and characteristic punctuation points;

[0038] Distance average unit: used to obtain the distance average corresponding to the common characteristic examination room seats and characteristic punctuation points;

[0039] Correlation coefficient unit: used to obtain the correlation coefficient corresponding to the monitoring combination according to the common characteristic number of examination room seats and the number of characteristic punctuation marks corresponding to the monitoring combination.

[0040] Further, the picture display duration module includes a next monitoring device unit and a picture display duration unit;

[0041] Next monitoring device unit: used to determine the monitoring device corresponding to the next screen display of the monitoring screen as the next monitoring device according to the correlation coefficient between the target device and the other monitoring devices in the target examination room;

[0042] Screen display duration unit: used to determine the screen display duration of the next monitoring device based on the characteristic examination room seats corresponding to the target device and the next monitoring device.

[0043] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention provides a standardized examination room intelligent management system and method based on artificial intelligence, including: obtaining a target examination room, the target examination room includes a number of monitoring devices and examination room seats, obtaining each monitoring device, and obtaining a characteristic examination room seat corresponding to the monitoring device; obtaining a monitoring combination, and determining a correlation coefficient corresponding to the monitoring combination; using the monitoring device corresponding to the picture being displayed on the monitoring large screen as the target device, and determining the picture display duration of the target device; determining the monitoring device corresponding to the next picture display of the monitoring large screen as the next monitoring device, and determining the picture display duration of the next monitoring device. According to the actual situation of the target examination room, the present invention analyzes the characteristic examination room seats corresponding to each monitoring device in the target examination room, which not only effectively improves the efficiency of monitoring screen switching, but also reasonably sets the picture display sequence and display duration of the monitoring device. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a flow chart of a standardized examination room intelligent management method based on artificial intelligence of the present invention;

[0046] Figure 2 It is a structural diagram of an artificial intelligence-based standardized examination room intelligent management system of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1-Figure 2 The present invention provides an intelligent management system and method for standardized examination rooms based on artificial intelligence, and a flowchart of an intelligent management method for standardized examination rooms based on artificial intelligence is shown as follows: Figure 1 As shown. The following steps are included:

[0049] Step S100: Acquire the target examination room, which includes a number of monitoring devices and examination room seats, obtain the monitoring screen corresponding to each monitoring device, the monitoring screen includes a number of examination room seats, and obtain a number of characteristic examination room seats corresponding to each monitoring device.

[0050] This solution is to analyze the target examination room. Usually, in order to ensure comprehensive monitoring without blind spots, all examination areas can be covered. And because the picture has the characteristics of large near and small far, the farther the seat area is from the monitoring, the more blurred the picture is, which is easy to cause the invigilator to make wrong judgments, so there will be multiple monitoring devices in one examination room. This step establishes the three-dimensional coordinate system of the target examination room, and determines the coordinate points corresponding to each monitoring device and the examination room seat in the three-dimensional coordinate system, and analyzes it in combination with the monitoring picture to determine the characteristic examination room seat corresponding to the monitoring device, where the characteristic examination room seat is the examination room seat that can be clearly seen in the monitoring picture and accurately judge the behavior of the examinee.

[0051] Step S110: Obtain the specific location of each monitoring device and examination room seat in the target examination room, and establish a three-dimensional coordinate system of the target examination room, the three-dimensional coordinate system includes the coordinate points corresponding to each monitoring device and examination room seat; obtain the monitoring device ME, the monitoring screen corresponding to the monitoring device ME is G ME , monitoring screen G ME The monitoring screen G is obtained through the pre-trained neural network model. ME Each examination room seat in the test is used as a marked examination room seat, and the coordinate points corresponding to the monitoring device ME and each marked examination room seat are marked in the three-dimensional coordinate system.

[0052] In this embodiment, the neural network model is DNN. A large number of monitoring images are collected to obtain a training data set, which is substituted into the DNN network model to obtain a trained neural network model. The specific training process is existing technology and will not be repeated here.

[0053] Step S120: Monitor the screen G ME Connect the upper left corner and the lower right corner, and the lower left corner and the upper right corner, and the intersection point is the center point CP of the picture. ME , establish monitoring screen G ME The corresponding two-dimensional coordinate system obtains the center point CP of the picture ME And the coordinate point corresponding to each marked examination room seat in the two-dimensional coordinate system, and then obtain the distance from the center point of the screen CP in the two-dimensional coordinate system ME The nearest marked examination room seat is recorded as the first examination room seat TS1.

[0054] Step S130: Calculate the average value of the coordinate points corresponding to each marked examination room seat in the three-dimensional coordinate system to obtain the seat center point CP TS , and then get the distance from the seat center point CP in the three-dimensional coordinate system TSThe most recently marked examination room seat is denoted as the second examination room seat TS2; according to the coordinate points corresponding to the first examination room seat TS1 and the second examination room seat TS2 in the three-dimensional coordinate system, the coordinate distance CD between the first examination room seat TS1 and the second examination room seat TS2 is obtained. In the three-dimensional coordinate system, the coordinate distances between the monitoring device ME and the first examination room seat TS1 and the second examination room seat TS2 are CD1 and CD2 respectively.

[0055] Step S140: If the coordinate distance CD < CD0, where CD0 is the coordinate distance threshold, obtain the maximum coordinate distance CD m = max(CD 1， CD2), obtain the coordinate distances between the monitoring device ME and each marked examination room seat, and use the marked examination room seats corresponding to the coordinate distances less than the maximum coordinate distance CD m as the characteristic examination room seats; if the coordinate distance CD ≥ CD0, obtain the coordinate distances between the monitoring device ME and each marked examination room seat, and use the marked examination room seats corresponding to the coordinate distances less than CD2 as the characteristic examination room seats.

[0056] Step S200: Take any two monitoring devices as a monitoring combination to obtain several monitoring combinations, and determine all the characteristic punctuation marks corresponding to each monitoring combination in the target examination room according to the characteristic examination room seats corresponding to each monitoring combination, and then determine the correlation coefficient corresponding to each monitoring combination.

[0057] Step S210: Obtain a certain monitoring combination MA, where a certain monitoring combination MA includes a first monitoring device ME1 and a second monitoring device ME2, and use the same characteristic examination room seats in the first monitoring device ME1 and the second monitoring device ME2 as the common characteristic examination room seats to obtain all the common characteristic examination room seats corresponding to a certain monitoring combination MA; obtain, in the three-dimensional coordinate system, a first vector with the coordinate corresponding to a certain common characteristic examination room seat CTS as the starting point and the coordinate corresponding to the first monitoring device ME1 as the ending point and a second vector with the coordinate corresponding to a certain common characteristic examination room seat CTS as the starting point and the coordinate corresponding to the second monitoring device ME2 as the ending point If the angle between the vectors and is greater than the angle threshold or the value of is less than the modulus distance threshold, then use the common characteristic examination room seat CTS as the characteristic punctuation mark of a certain monitoring combination MA in the target examination room; and then obtain all the characteristic punctuation marks of a certain monitoring combination MA in the target examination room;

[0058] Step S220: Obtain a first seat vector starting from the coordinates corresponding to a common characteristic examination room seat CTS1 corresponding to a certain monitoring combination MA and ending at the coordinates corresponding to the first monitoring device ME1, and a second seat vector starting from the coordinates corresponding to a common characteristic examination room seat CTS1 and ending at the coordinates corresponding to the second monitoring device ME2, and use the angle between the two seat vectors as the seat angle of a common characteristic examination room seat CTS1, and then obtain the seat angle of each common characteristic examination room seat corresponding to a certain monitoring combination MA, and calculate the average value to obtain Obtain a first eigenvector with the coordinates of a certain characteristic punctuation point CM1 corresponding to a certain monitoring combination MA as the starting point and the coordinates of the first monitoring device ME1 as the end point, and a second eigenvector with the coordinates of a certain characteristic punctuation point CM1 as the starting point and the coordinates of the second monitoring device ME2 as the end point, and use the angle between the two eigenvectors as the characteristic angle of a certain characteristic punctuation point CM1, and then obtain the characteristic angle of each characteristic punctuation point corresponding to a certain monitoring combination MA, and calculate the average value;

[0059] Step S230: Obtain the coordinates of a common characteristic examination room seat CTS1 corresponding to a monitoring combination MA, the first seat distance of the coordinates corresponding to the first monitoring device ME1, and the coordinates of a common characteristic examination room seat CTS1, the second seat distance of the coordinates corresponding to the second monitoring device ME2, and add the two seat distances to obtain the seat distance corresponding to the common characteristic examination room seat CTS1, and then obtain the seat distance corresponding to each common characteristic examination room seat corresponding to a monitoring combination MA, and calculate the average value to obtain Obtain the coordinates of a certain characteristic point CM1 corresponding to a certain monitoring combination MA, the first characteristic distance of the coordinates corresponding to the first monitoring device ME1, and the coordinates of a certain characteristic point CM1, the second characteristic distance of the coordinates corresponding to the second monitoring device ME2, and add the two characteristic distances to obtain the characteristic distance corresponding to the characteristic point CM1, and then obtain the characteristic distance corresponding to each characteristic point corresponding to a certain monitoring combination MA, and calculate the average value to obtain

[0060] Step S240: Set the weight corresponding to the common feature examination room seat to α, and the weight corresponding to the characteristic punctuation point to β, and obtain Then, according to the common characteristic number of examination room seats N1 and characteristic punctuation marks N2 corresponding to the monitoring combination, where N1≥N2, the correlation coefficient COF=α*N1+(β-α)*N2 corresponding to the monitoring combination is obtained and normalized.

[0061] The common characteristic examination room seats are only the common characteristic examination room seats corresponding to the two monitoring devices. If the examination room seats are far away and difficult to be found in the monitoring, they may also become common characteristic examination room seats. However, the characteristic punctuation is used to screen the common characteristic examination room seats. The characteristic examination room seats that are closer to the two monitoring devices have greater reference significance and are also easier to find in the process of switching from one monitoring screen to another. Therefore, the weight β of the characteristic punctuation should not be less than the weight α of the common characteristic examination room seats. In this embodiment, α=1.

[0062] Taking the example of two monitors before and after the examination room, since the number of seats in the common feature of the first two monitors is larger than that of the two monitors before and after, the correlation coefficients corresponding to the first two monitors should also be larger.

[0063] Step S300: The monitoring device corresponding to the image being displayed on the monitoring screen is taken as the target device, and the image display time of the target device is determined according to the characteristic examination room seats in the target device.

[0064] Step S310: Obtain the coordinate point corresponding to each marked examination room seat corresponding to the target device TME in the three-dimensional coordinate system, take the number of marked examination room seats corresponding to the target device TME as W1, set a rectangular range with a length of L1 and a width of L2, take the coordinate point corresponding to the marked examination room seat MTS as the center point of the rectangular range, and rotate the rectangular range clockwise around the center point, with a rotation angle of θ 转 If, during the rotation process, there are always coordinate points corresponding to other marked examination room seats within the rectangular range, the marked examination room seats MTS will be marked twice, and the number of all the twice marked examination room seats will be recorded as W2, and the distribution anomaly coefficient is: X TME =W2 / W1, where 0≤X TME ≤1.

[0065] In this embodiment, the rotation angle is θ 转 =360°.

[0066] Step S320: Obtain the historical timed switching interval length T, and use the number of seats in the characteristic examination room corresponding to the target device TME as Y TME , determine the screen display time t of the target device TME TME =(1+X TME / X0)(Y TME / Y0)*T, and when t TME <T-T0 or t TME >T+T0, let t TME =T-T0 or t TME=T+T0, where X0 is the distribution anomaly coefficient threshold, Y0 is the characteristic examination room seat number threshold, and T0 is the interval time threshold.

[0067] This solution analyzes the seat distribution of the target examination room and the number of seats in the characteristic examination room. Since the rationality of the seat layout is related to the cheating behavior of candidates, a reasonable seat layout can reduce the chance of cheating by candidates. Therefore, when the distribution anomaly coefficient X TME When it is smaller, the screen display time is t TME should also be small; and because the number of examination room seats is too small, the number of observable candidates is also small, so when the characteristic examination room seat number Y TME When it is smaller, the screen display time is t TME It should also be small. In actual examinations, the front-to-back distance between candidates in two seats should not be less than 50cm, and the left-to-right distance should not be less than 80cm. Since seats are represented by coordinate points in this scheme, the length L1 = 100cm and the width L2 = 70cm in this embodiment. The distribution anomaly coefficient threshold X0 = 0.6, the characteristic examination room seat number threshold Y0 = 20, and the interval time threshold T0 = T / 3.

[0068] Step S400: According to the correlation coefficient between the target device and the other monitoring devices in the target examination room, determine the monitoring device corresponding to the next screen display of the monitoring screen as the next monitoring device, and determine the screen display duration of the next monitoring device according to the characteristic examination room seat corresponding to the target device and the next monitoring device.

[0069] Step S410: According to the correlation coefficients corresponding to the target device TME and the remaining monitoring devices, if the correlation coefficients between the target device TME and the remaining monitoring devices are all 0, obtain the monitoring device that is closest to the target device TME in the three-dimensional coordinate system as the monitoring device displayed on the next screen; otherwise, if the correlation coefficients between the target device TME and the remaining monitoring devices are not all 0, the monitoring device corresponding to the largest correlation coefficient is used as the monitoring device displayed on the next screen of the monitoring screen; the monitoring device displayed on the next screen is recorded as the monitoring device NME.

[0070] Step S420: Obtain the number of seats Y in the characteristic examination room corresponding to the target device TME and the monitoring device NME TME and Y NME , and according to the screen display time t of the target device TME TME , determine the display time t of the monitoring device NME NME =t TME *Y NME / Y TME , and when t NME <T-T0 or t NME >T+T0, let t NME=T-T0 or t NME =T+T0, where T is the historical timed switching interval and T0 is the interval threshold.

[0071] If all target devices in the target examination room are to be displayed once in a round of screen cycles, when the monitoring device NME searches for the next monitoring device, since the target device TME has been displayed in this round, the next monitoring device needs to exclude the target device TME.

[0072] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0073] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A standardized examination room intelligent management method based on artificial intelligence, characterized in that: The following steps are involved: Step S100: obtaining a target examination room, wherein the target examination room includes a plurality of monitoring devices and examination room seats, obtaining a monitoring screen corresponding to each monitoring device, wherein the monitoring screen includes a plurality of examination room seats, and obtaining a plurality of characteristic examination room seats corresponding to each monitoring device; Step S200: any two monitoring devices are taken as a monitoring combination to obtain a plurality of monitoring combinations, and according to the characteristic examination room seats corresponding to each monitoring combination, all characteristic punctuation points corresponding to each monitoring combination in the target examination room are determined, and then the correlation coefficient corresponding to each monitoring combination is determined; Step S300: taking the monitoring device corresponding to the picture being displayed on the monitoring screen as the target device, and determining the picture display time of the target device according to the characteristic examination room seat in the target device; Step S400: According to the correlation coefficient between the target device and the other monitoring devices in the target examination room, determine the monitoring device corresponding to the next screen display of the monitoring large screen as the next monitoring device, and determine the screen display time of the next monitoring device according to the characteristic examination room seat corresponding to the target device and the next monitoring device; Step S200 includes: Step S210: Obtain a monitoring combination MA, wherein the monitoring combination MA includes a first monitoring device ME1 and a second monitoring device ME2, and take the same characteristic examination room seats in the first monitoring device ME1 and the second monitoring device ME2 as common characteristic examination room seats, and obtain all common characteristic examination room seats corresponding to the monitoring combination MA; obtain a first vector in a three-dimensional coordinate system, with the coordinates corresponding to a certain common characteristic examination room seat CTS as the starting point and the coordinates corresponding to the first monitoring device ME1 as the end point , and the coordinates corresponding to a common characteristic examination room seat CTS as the starting point, and the coordinates corresponding to the second monitoring device ME2 as the end point of the second vector , if the vector and The angle between them is greater than the angle threshold or | |+| | is less than the modulus distance threshold, the common characteristic examination room seat CTS is used as the characteristic punctuation point of the certain monitoring combination MA in the target examination room; and then all the characteristic punctuation points of the certain monitoring combination MA in the target examination room are obtained; Step S220: Obtain a first seat vector starting from the coordinates corresponding to a certain common characteristic examination room seat CTS1 corresponding to a certain monitoring combination MA and ending at the coordinates corresponding to the first monitoring device ME1, and a second seat vector starting from the coordinates corresponding to the certain common characteristic examination room seat CTS1 and ending at the coordinates corresponding to the second monitoring device ME2, and use the angle between the two seat vectors as the seat angle of the certain common characteristic examination room seat CTS1, and then obtain the seat angle of each common characteristic examination room seat corresponding to a certain monitoring combination MA, and calculate the average value to obtain ; Obtain a first feature vector with the coordinates of a certain characteristic punctuation point CM1 corresponding to a certain monitoring combination MA as the starting point and the coordinates of the first monitoring device ME1 as the end point, and a second feature vector with the coordinates of the certain characteristic punctuation point CM1 as the starting point and the coordinates of the second monitoring device ME2 as the end point, and use the angle between the two feature vectors as the characteristic angle of the certain characteristic punctuation point CM1, and then obtain the characteristic angle of each characteristic punctuation point corresponding to the certain monitoring combination MA, and calculate the average value to obtain ; Step S230: Obtain the coordinates of a common characteristic examination room seat CTS1 corresponding to a monitoring combination MA, the first seat distance of the coordinates corresponding to the first monitoring device ME1, and the coordinates of the common characteristic examination room seat CTS1 corresponding to the second monitoring device ME2, and add the two seat distances to obtain the seat distance corresponding to the common characteristic examination room seat CTS1, and then obtain the seat distance corresponding to each common characteristic examination room seat corresponding to a monitoring combination MA, and calculate the average value to obtain ; Obtain the coordinates of a certain characteristic punctuation point CM1 corresponding to the certain monitoring combination MA, and the first characteristic distance of the coordinates corresponding to the first monitoring device ME1, and the coordinates of the certain characteristic punctuation point CM1, and the second characteristic distance of the coordinates corresponding to the second monitoring device ME2, and add the two characteristic distances to obtain the characteristic distance corresponding to the certain characteristic punctuation point CM1, and then obtain the characteristic distance corresponding to each characteristic punctuation point corresponding to the certain monitoring combination MA, and calculate the average value to obtain ; Step S240: Set the weight corresponding to the common feature examination room seat to α, and the weight corresponding to the characteristic punctuation to β, and obtain β=max(α* / , α* / , α); and then according to the common characteristic examination room seat number N1 and characteristic punctuation number N2 corresponding to the monitoring combination, where N1≥N2, the correlation coefficient COF=α*N1+(β-α)*N2 corresponding to the monitoring combination is obtained and normalized.

2. According to claim 1, a standardized examination room intelligent management method based on artificial intelligence is characterized in that: Step S100 includes: Step S110: Obtain the specific location of each monitoring device and examination room seat in the target examination room, and establish a three-dimensional coordinate system of the target examination room, wherein the three-dimensional coordinate system includes coordinate points corresponding to each monitoring device and examination room seat; obtain the monitoring device ME, and the monitoring screen corresponding to the monitoring device ME is G ME , monitoring screen G ME The monitoring screen G is obtained through the pre-trained neural network model. ME Each examination room seat in the test is used as a marked examination room seat, and the coordinate points corresponding to the monitoring device ME and each marked examination room seat are marked in the three-dimensional coordinate system; Step S120: Monitor the screen G ME Connect the upper left corner and the lower right corner, and the lower left corner and the upper right corner, and the intersection point is the center point CP of the picture. ME , establish monitoring screen G ME The corresponding two-dimensional coordinate system obtains the center point CP of the picture ME And the coordinate point corresponding to each marked examination room seat in the two-dimensional coordinate system, thereby obtaining the distance from the center point CP of the screen in the two-dimensional coordinate system ME The nearest marked examination room seat is recorded as the first examination room seat TS1; Step S130: Calculate the average value of the coordinate points corresponding to each marked examination room seat in the three-dimensional coordinate system to obtain the seat center point CP TS , and then obtain the distance from the seat center point CP in the three-dimensional coordinate system TS The nearest marked examination room seat is recorded as the second examination room seat TS2; according to the corresponding coordinate points of the first examination room seat TS1 and the second examination room seat TS2 in the three-dimensional coordinate system, the coordinate distance CD between the first examination room seat TS1 and the second examination room seat TS2 is obtained, and the coordinate distances of the monitoring device ME to the first examination room seat TS1 and the second examination room seat TS2 in the three-dimensional coordinate system are CD1 and CD2 respectively; Step S140: If the coordinate distance CD < CD0, where CD0 is the coordinate distance threshold, obtain the maximum coordinate distance CD m = max(CD 1， CD2), obtain the coordinate distances of the monitoring device ME from each marked examination hall seat, and use the marked examination hall seats corresponding to the coordinate distances less than the maximum coordinate distance CD m as the characteristic examination hall seats; if the coordinate distance CD ≥ CD0, obtain the coordinate distances of the monitoring device ME from each marked examination hall seat, and use the marked examination hall seats corresponding to the coordinate distances less than CD2 as the characteristic examination hall seats.

3. The method for intelligent management of standardized examination rooms based on artificial intelligence according to claim 2, characterized in that: Step S300 includes: Step S310: Obtain the coordinate point corresponding to each marked examination room seat corresponding to the target device TME in the three-dimensional coordinate system, take the number of marked examination room seats corresponding to the target device TME as W1, set a rectangular range with a length of L1 and a width of L2, take the coordinate point corresponding to the marked examination room seat MTS as the center point of the rectangular range, and rotate the rectangular range clockwise around the center point, with a rotation angle of If, during the rotation process, there are always coordinate points corresponding to other marked examination room seats within the rectangular range, the marked examination room seats MTS will be marked twice, and the number of all the twice marked examination room seats will be recorded as W2, and the distribution anomaly coefficient is: X TME =W2 / W1, where 0≤X TME ≤1; Step S320: Obtain the historical timed switching interval length T, and use the number of seats in the characteristic examination room corresponding to the target device TME as Y TME , determine the screen display time t of the target device TME TME =(1+X TME / X0)(Y TME / Y0)*T, and when t TME <T-T0 or t TME >T+T0, let t TME =T-T0 or t TME =T+T0, where X0 is the distribution anomaly coefficient threshold, Y0 is the characteristic examination room seat number threshold, and T0 is the interval duration threshold.

4. The method for intelligent management of standardized examination rooms based on artificial intelligence according to claim 3 is characterized in that: Step S400 includes: Step S410: According to the correlation coefficients corresponding to the target device TME and the other monitoring devices, if the correlation coefficients between the target device TME and the other monitoring devices are all 0, obtain the monitoring device closest to the target device TME in the three-dimensional coordinate system as the monitoring device displayed on the next screen; otherwise, if the correlation coefficients between the target device TME and the other monitoring devices are not all 0, use the monitoring device corresponding to the largest correlation coefficient as the monitoring device displayed on the next screen of the monitoring large screen; record the monitoring device displayed on the next screen as the monitoring device NME; Step S420: Obtain the number of seats Y in the characteristic examination room corresponding to the target device TME and the monitoring device NME TME and Y NME , and according to the screen display time t of the target device TME TME , determine the display time t of the monitoring device NME NME =t TME *Y NME / Y TME , and when t NME <T-T0 or t NME >T+T0, let t NME =T-T0 or t NME =T+T0, where T is the historical timed switching interval and T0 is the interval threshold.

5. A standardized examination room intelligent management system, used to implement an artificial intelligence-based standardized examination room intelligent management method as described in any one of claims 1 to 4, characterized in that: The system includes a characteristic examination room seat module, a correlation coefficient module, a target device module and a screen display duration module; Characteristic examination room seat module: used to obtain the target examination room, which includes a number of monitoring devices and examination room seats, obtain the monitoring screen corresponding to each monitoring device, the monitoring screen includes a number of examination room seats, and obtain a number of characteristic examination room seats corresponding to each monitoring device; Correlation coefficient module: used to take any two monitoring devices as a monitoring combination, obtain several monitoring combinations, and determine all the characteristic punctuation points corresponding to each monitoring combination in the target examination room according to the characteristic examination room seats corresponding to each monitoring combination, and then determine the correlation coefficient corresponding to each monitoring combination; Target device module: used to take the monitoring device corresponding to the image being displayed on the monitoring screen as the target device, and determine the image display time of the target device according to the characteristic examination room seat in the target device; Screen display duration module: used to determine the monitoring device corresponding to the next screen display of the monitoring screen as the next monitoring device based on the correlation coefficient between the target device and the remaining monitoring devices in the target examination room, and determine the screen display duration of the next monitoring device based on the characteristic examination room seat corresponding to the target device and the next monitoring device.

6. A standardized examination room intelligent management system according to claim 5, characterized in that: The correlation coefficient module includes a characteristic punctuation unit, an angle average unit, a distance average unit and a correlation coefficient unit; Characteristic punctuation unit: used to take any two monitoring devices as a monitoring combination, obtain several monitoring combinations, and obtain all common characteristic examination room seats corresponding to the monitoring combination according to the characteristic examination room seats corresponding to each monitoring combination, and then determine all characteristic punctuations corresponding to each monitoring combination in the target examination room; Angle average unit: used to obtain the angle average corresponding to the common characteristic examination room seats and characteristic punctuation points; Distance average unit: used to obtain the distance average corresponding to the common characteristic examination room seats and characteristic punctuation points; Correlation coefficient unit: used to obtain the correlation coefficient corresponding to the monitoring combination according to the number of common characteristic examination room seats and the number of characteristic punctuation marks corresponding to the monitoring combination.

7. A standardized examination room intelligent management system according to claim 6, characterized in that: The picture display duration module includes a next monitoring device unit and a picture display duration unit; Next monitoring device unit: used to determine the monitoring device corresponding to the next screen display of the monitoring screen as the next monitoring device according to the correlation coefficient between the target device and the other monitoring devices in the target examination room; Screen display duration unit: used to determine the screen display duration of the next monitoring device according to the characteristic examination room seat corresponding to the target device and the next monitoring device.

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