A smart adjustment system and method for projection devices

Abstract

The present invention discloses an intelligent adjustment system and method for projection equipment, and relates to the field of artificial intelligence technology. The present invention uses an image sensor and a projection equipment to work together, and uses grayscale values ​​to extract image data without interference to generate a reference image, thereby reducing misjudgment of foreign objects, improving projection accuracy and clarity, and ensuring the best projection environment. The pre-projection area image data is collected in real time, and combined with the grayscale co-occurrence matrix analysis method, the influencing factors such as the color, area and position of foreign objects are comprehensively evaluated, and foreign object interference is accurately identified and reduced, so as to select the best area with the least impact on the projection quality. Avoiding interference improves the projection stability and ensures efficient operation of the system. In addition, the matching degree between the projection equipment and the best area is calculated, and the position and projection origin are automatically adjusted in case of deviation to ensure accurate mapping, improve the automatic adjustment capability of the equipment, and achieve high-quality projection effects.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an intelligent adjustment system and method for projection equipment. Background Art

[0002] Projection equipment converts image signals into visual light and projects it onto a screen or wall. It is widely used in education, business, entertainment and other fields. Its basic principle is to use light sources such as bulbs, lasers and lens systems to process the input image signals, and then adjust the projection distance and focal length to clearly display the image. The importance of projection equipment is reflected in its ability to improve the efficiency of information transmission and enhance the visual experience. It is especially indispensable in teaching, conference presentations and home entertainment. With the development of technology, smart projection equipment has gradually become popular. By automatically adjusting the image and optimizing the image quality, it further improves the user experience and becomes an important tool in modern life.

[0003] After purchasing projection equipment, many consumers often choose a large white wall at home as the projection area. However, problems such as pits and cracks on the wall will seriously affect the projection effect, causing image distortion, color difference or occlusion, thereby reducing the viewing experience. Therefore, an intelligent system combined with data analysis is needed to identify and evaluate the flatness and obstacles of the wall, and optimize the selection of the projection area to ensure that the projection equipment can project a clear and uniform image in the best position, thereby improving the overall visual effect and viewing experience. Summary of the invention

[0004] The object of the present invention is to provide an intelligent adjustment system and method for projection equipment to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: an intelligent adjustment method for a projection device, the intelligent adjustment method comprising the following steps:

[0006] Step S1, projecting a detection pattern partially on the pre-projection area through a projection device, and using an image sensor to collect data on the detection pattern in the pre-projection area, extracting image data in a foreign body-free state and saving it as a reference image;

[0007] Step S1-1, selecting any local area of ​​the pre-projection area, wherein the pre-projection area is represented by an area to be projected defined by an image sensor before the projection device performs actual projection; using the projection device to project a detection pattern, wherein the detection pattern is a plurality of n*m grids;

[0008] Step S1-2, using an image sensor to collect image data of the detection pattern in the pre-projection area to extract image data in a foreign matter-free state, where the foreign matter-free state refers to a projection environment in which no interference affects the projection; marking the n*m ​​grids with the most identical grayscale values, and saving the image data consisting of the marked continuous multiple n*m ​​grids as a reference image.

[0009] By extracting grid data with consistent grayscale values ​​in the absence of foreign objects to generate a reference image, the accuracy of foreign object recognition can be effectively improved and the chance of misjudgment can be reduced.

[0010] Step S2, collecting global image data of the pre-projection area to construct global image data, constructing two-dimensional coordinates for the global image data, identifying foreign objects in the global image data according to the reference image, and constructing a projection foreign object set to store the identification results;

[0011] Step S2-1, performing omnidirectional image acquisition on the pre-projection area through an image sensor, wherein the omnidirectional image acquisition is performed on the entire area used for projection in the pre-projection area, acquiring detection pattern data projected by the projection device on the pre-projection area, generating complete image data covering the pre-projection area, and constructing global image data, wherein the global image data includes pixel information of all n*m grids in the pre-projection area;

[0012] Step S2-2, in the global image data, each n*m grid is taken as a unit and a unique two-dimensional coordinate is assigned to it. With one corner of the pre-projection area as the origin, the horizontal coordinate is constructed in the horizontal direction, and the vertical coordinate is constructed in the vertical direction. The coordinate values ​​are assigned in sequence according to the arrangement order of the n*m ​​grids;

[0013] Step S2-3, constructing a projected foreign body set, performing foreign body recognition on the global image data according to the reference image, and storing the recognition result in the projected foreign body set;

[0014] Step S2-3-1, taking the n*m ​​grid grayscale value of the reference image as a reference, combining the grayscale co-occurrence matrix to extract the foreign body data in the pre-projection area, and obtain the grid two-dimensional coordinate data where the foreign body is located;

[0015] Step S2-3-2, construct a set of projected foreign bodies, and identify foreign bodies in the pre-projection area through the gray-level co-occurrence matrix, where each gray-level co-occurrence matrix includes multiple n*m ​​grids, and one n*m ​​grid corresponds to one element of the gray-level co-occurrence matrix. The contrast of the n*m ​​grids in the pre-projection area is calculated according to the gray-level co-occurrence matrix. The calculation formula of the contrast is as follows:

[0016] ;

[0017] Where D is the contrast of multiple n*m ​​grids in the gray-level co-occurrence matrix; B is the total number of gray levels; P(i, j) is the frequency of occurrence of gray values ​​i and j in the gray-level co-occurrence matrix at a given direction and distance; i and j are the indexes of the row and column in the gray-level co-occurrence matrix, respectively, representing the gray value of a pixel in the image; (ij) 2 It is expressed as the intensity value of the gray value change in the gray-level co-occurrence matrix;

[0018] Contrast is a measure of grayscale differences in an image, which helps distinguish foreign objects from the background. Higher contrast indicates that foreign objects are clearly different from the environment, which helps to accurately detect and locate foreign objects, thereby improving projection quality and recognition accuracy.

[0019] The energy of the n*m ​​grid in the pre-projection area is calculated according to the gray-level co-occurrence matrix. The energy calculation formula is as follows:

[0020] ;

[0021] Where E represents the complex value of the image texture in the pre-projection area; P(ij) 2 It is expressed as the intensity of the frequency of occurrence of gray values ​​i and j in the gray-level co-occurrence matrix;

[0022] Energy is a measure of the uniformity of the texture in an image. Higher energy values ​​indicate uniform textures, while lower energy values ​​indicate complex or irregular textures. It helps distinguish between normal and foreign areas, as foreign objects typically lower the energy value of an image, thus helping to identify and locate them.

[0023] The entropy value of the n*m ​​grid in the pre-projection area is calculated according to the gray-level co-occurrence matrix. The entropy value calculation formula is as follows:

[0024] ;

[0025] Where C represents the randomness intensity of the gray value distribution of the image in the pre-projection area; log2P(i, j) represents the quantization calculation used for the image;

[0026] Entropy is used to measure the complexity and randomness of an image, with higher entropy values ​​indicating more random and complex image content. In projected foreign body recognition, entropy helps distinguish between normal and foreign areas, as foreign objects usually increase the entropy of an image, thus helping to identify abnormal areas.

[0027] The color influence value of the n*m ​​grid in the pre-projection area is calculated using the following formula:

[0028] ;

[0029] In the formula, G realIt represents the color influence value of the n*m grid in the pre-projection area on the projection of the projection device; w1, w2, and w3 represent the weights of each eigenvalue on the total influence value;

[0030] Calculate the average value g and the standard deviation b of the color influence value of the reference image based on the reference image, and set the foreign object judgment threshold G according to the average value g and the standard deviation b max , and the setting uses the following formula:

[0031] G max =Q*b;

[0032] In the formula, G max represents the maximum value of the color influence fluctuation within the n*m grid; Q represents the color influence coefficient;

[0033] When |G real -g|≥G max , it means that the image data within the n*m grid is foreign object image data;

[0034] When |G real -g|<G max , it means that the image data within the n*m grid is foreign object-free image data;

[0035] G=G real -g;

[0036] In the formula, G represents the color influence value of the n*m grid in the pre-projection area on the projection of the projection device;

[0037] Step S2-3-3, construct a projection foreign object set. After calculating the color influence value G of the n*m grid on the projection of the projection device, obtain the corresponding two-dimensional coordinate data, and store the color influence value G of each grid and the corresponding two-dimensional coordinate data into the projection foreign object set. The projection foreign object set includes the color influence value G of the n*m grid on the projection of the projection device and the two-dimensional coordinate data.

[0038] By analyzing the gray-level co-occurrence matrix to extract features such as the contrast, energy, and entropy value of the foreign object, and calculating the color influence value of each n*m grid, the foreign objects in the pre-projection area can be accurately identified.

[0039] Step S3, perform a projection comprehensive analysis based on the projection foreign object set. The projection comprehensive analysis includes foreign object color influence, area influence, and foreign object position influence, and select the best projection area for projection;

[0040] Step S3-1, perform a projection comprehensive analysis based on the projection foreign object set. The projection comprehensive analysis includes foreign object color influence, area influence, and foreign object position influence, and the value of the color influence is the color influence value G;

[0041] The calculation formula for the area influence is as follows:

[0042] ;

[0043] In the formula, S color，C It is expressed as the total area of ​​the grid adjacent to the G position whose color influence value is; H C It is represented by the number of grids adjacent to the G position whose color influence value is n; n is the length of the grid; m is the width of the grid;

[0044] The projected foreign body set is analyzed and calculated to obtain the geometric center coordinates of the foreign body. The position influence of the foreign body is calculated in combination with the projection origin. The specific calculation process is as follows:

[0045] The abscissa of the geometric center of the foreign body is the color influence value G, and the average value of the abscissas of the adjacent grids;

[0046] The ordinate of the geometric center of the foreign body is the color impact value G, and the average ordinate of the grids adjacent to it;

[0047] The calculation of the influence of the foreign body position is combined with the calculation of the projection origin, using the following formula:

[0048] ;

[0049] Where A represents the influence value of the n*m ​​grid in the pre-projection area on the projection position of the projection device; R represents the coefficient for controlling the magnitude of the influence value; x0 represents the horizontal coordinate of the projection origin; y0 represents the vertical coordinate of the projection origin; x center Expressed as the horizontal coordinate of the foreign body, y center It is represented as the ordinate of the foreign body; p is represented as the attenuation index of the foreign body position from the projection origin;

[0050] Step S3-2: According to the color impact value G and the area impact S color，C The area with the least foreign matter influence in the pre-projection area is calculated by the foreign matter position influence A, and is taken as the optimal projection area. The comprehensive foreign matter influence calculation is performed on the pre-projection area, and the calculation is performed using the following formula:

[0051] ;

[0052] Where Z represents the impact value of foreign matter in the pre-projection area; w G Expressed as the weight of the color influence value; w S Expressed as the weight of the area impact value; w A The weight expressed as the position influence value;

[0053] The area with the smallest Z value is selected as the best projection area. By comprehensively analyzing the color, area and position effects of foreign objects, the projection effect of each area can be accurately evaluated. The calculated foreign object impact value helps identify the area with the least impact as the best projection area, thereby reducing the interference of foreign objects on the projected image, optimizing the projection effect, and improving the accuracy and stability of the system.

[0054] Step S4, obtaining the projection distance of the projection device, and calculating the matching degree between the projection area of ​​the current position of the projection device and the optimal projection area in combination with the projection distance and the projection offset of the projection device, wherein the projection offset includes a translation offset and an angle adjustment amount, and feeding back the position adjustment range of the projection device;

[0055] Step S4-1, obtain the projection distance of the projection device through the sensor, and calculate the matching degree between the projection area of ​​the current position of the projection device and the optimal projection area according to the projection origin and projection offset of the projection device. The matching degree calculation formula is as follows:

[0056] ;

[0057] Where F represents the maximum value of the matching degree when the current projection device position is used for projection; x now It is represented as the horizontal coordinate of the origin of the best projection area; y now It is expressed as the ordinate of the origin of the best projection area; x obj The horizontal coordinate of the projection origin of the current projection device when the matching degree between the position projection of the current projection device and the optimal projection area reaches the maximum value; y obj The ordinate of the projection origin of the current projection device when the matching degree between the position projection of the current projection device and the optimal projection area reaches the maximum value;

[0058] Step S4-2: judge the calculated matching degree. The judging process is as follows:

[0059] When F=0, the current position of the projection device is determined. By adjusting the translation offset and angle adjustment, the optimal projection area can be fully matched and projection can be performed directly.

[0060] When F≠0, the current position of the projection device is determined to be unable to fully match the optimal projection area by adjusting the translation offset and the angle adjustment, and a position adjustment signal is issued.

[0061] By calculating the matching degree between the current position of the projection device and the optimal projection area, the projection effect of the current projection device can be effectively judged. When the matching degree is zero, it means that direct projection is possible, and when the matching degree is not zero, the position adjustment signal is triggered to ensure accurate matching of the projection area, optimize the projection effect, reduce errors, and improve system stability and accuracy.

[0062] Step S5: acquiring the projection distance of the projection device according to the adjusted position of the projection device, and adjusting the projection offset of the projection device in combination with the projection origin of the optimal projection area.

[0063] Step S5-1, obtaining the two-dimensional coordinate information corresponding to the adjusted position of the projection device, and obtaining the projection distance of the adjusted position of the projection device again through the sensor;

[0064] Step S5-2, controlling the projection origin adjustment of the projection device according to the two-dimensional coordinate information corresponding to the adjusted position of the projection device and the projection origin coordinates of the optimal projection area, so that the projection origin of the projection device after the position adjustment finally coincides with the projection origin of the optimal projection area.

[0065] By precisely adjusting the two-dimensional coordinates and projection distance of the projection device, it is ensured that the projection origin coincides with the optimal area, thereby eliminating deviations and optimizing the projection effect.

[0066] Further, an intelligent adjustment system for a projection device, the intelligent adjustment system comprising a reference image acquisition module, a global image coordinate module, a foreign body recognition module, a projection intelligent selection module and a projection offset adjustment module;

[0067] The reference image acquisition module is used to project a detection pattern on a part of the pre-projection area, and analyze and extract image data in a foreign-matter-free state; the global image coordinate module is used to collect comprehensive image data of the pre-projection area, and construct a two-dimensional coordinate to assign a unique two-dimensional coordinate to each grid; the foreign matter identification module is used to identify foreign matter information in the pre-projection area; the projection intelligent selection module is used to select the best projection area from the pre-projection area; the projection offset adjustment module is used to control the projection offset of the projection device;

[0068] The output end of the reference image acquisition module is electrically connected to the input end of the global image coordinate module; the output end of the global image coordinate module is electrically connected to the input end of the foreign object recognition module; the output end of the foreign object recognition module is electrically connected to the input end of the projection intelligent selection module; the output end of the projection intelligent selection module is electrically connected to the input end of the projection offset adjustment module.

[0069] The reference image acquisition module includes a local image acquisition unit and a reference image generation unit; the local image acquisition unit is used to acquire the detection pattern; the reference image generation unit is used to extract the image in the state without foreign matter and generate the reference image;

[0070] The global image coordinate module includes a global image acquisition unit and a coordinate management unit; the global image acquisition unit is used to acquire an omnidirectional image of the pre-projection area; the coordinate management unit is used to generate a two-dimensional coordinate according to the projected detection pattern;

[0071] The foreign object recognition module includes a foreign object recognition unit and a foreign object coordinate unit; the foreign object recognition unit is used to recognize foreign object data in the pre-projection area; and the foreign object coordinate unit is used to calculate the geometric center coordinates of the foreign object.

[0072] The projection intelligent selection module includes a foreign body impact calculation unit and an optimal projection origin unit; the foreign body position calculation unit is used to calculate the comprehensive projection impact value according to the color, area and position of the foreign body; the optimal projection origin unit is used to manage the projection origin coordinate data of the optimal projection area;

[0073] The projection offset adjustment module includes a projection origin adjustment unit and a projection offset correction unit; the projection origin adjustment unit is used to adjust the projection origin coordinates of the current projection device to align with the optimal area; the projection offset correction unit is used to control the translation offset and angle adjustment amount.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] 1. The present invention uses an image sensor and a projection device to work together. The image data without interference objects is first extracted according to the grayscale value to generate a reference image. This can effectively reduce misjudgments caused by foreign objects, thereby improving the accuracy and clarity of the projected image and ensuring that the projection device projects in the best projection environment.

[0076] 2. The present invention collects global image data of the pre-projection area in real time and combines it with the gray-level co-occurrence matrix analysis method to comprehensively evaluate the influencing factors of foreign matter in the area, including multiple dimensions such as color, area and position. This analysis method can accurately identify and evaluate the potential interference of foreign matter on the projection effect, and help the system select the best projection area with the least impact on the projection quality. By avoiding interference from foreign matter, the quality and stability of the projection effect are significantly improved, thereby ensuring the efficient operation of the system in complex environments.

[0077] 3. The present invention calculates the matching degree between the projection device and the optimal projection area in real time, and automatically sends a position adjustment signal when a deviation is found. The projection device quickly adjusts its own position and projection origin according to the feedback information to ensure that the mapping of the optimal projection area can be accurately achieved. This optimization process significantly improves the automatic adjustment capability of the equipment, allowing the system to flexibly adapt to different environments and conditions, and ultimately achieves the effect of high-quality projection in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A schematic diagram of a flow chart of an intelligent adjustment method for a projection device according to the present invention;

[0079] Figure 2 The present invention is a schematic structural diagram of an intelligent adjustment system for projection equipment. DETAILED DESCRIPTION

[0080] 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.

[0081] Embodiment 1: Figure 1 As shown, the present invention provides a technical solution, an intelligent adjustment method for a projection device, and the intelligent adjustment method comprises the following steps:

[0082] Step S1, projecting a detection pattern partially on the pre-projection area through a projection device, and using an image sensor to collect data on the detection pattern in the pre-projection area, extracting image data in a foreign body-free state and saving it as a reference image;

[0083] Step S1-1, selecting any local area of ​​the pre-projection area, wherein the pre-projection area is represented by an area to be projected defined by an image sensor before the projection device performs actual projection; using the projection device to project a detection pattern, wherein the detection pattern is a plurality of n*m grids;

[0084] Step S1-2, using an image sensor to collect image data of the detection pattern in the pre-projection area to extract image data in a foreign matter-free state, where the foreign matter-free state refers to a projection environment in which no interference affects the projection; marking the n*m ​​grids with the most identical grayscale values, and saving the image data consisting of the marked continuous multiple n*m ​​grids as a reference image.

[0085] Step S2, collecting global image data of the pre-projection area to construct global image data, constructing two-dimensional coordinates for the global image data, identifying foreign objects in the global image data according to the reference image, and constructing a projection foreign object set to store the identification results;

[0086] Step S2-1, performing omnidirectional image acquisition on the pre-projection area through an image sensor, wherein the omnidirectional image acquisition is performed on the entire area used for projection in the pre-projection area, acquiring detection pattern data projected by the projection device on the pre-projection area, generating complete image data covering the pre-projection area, and constructing global image data, wherein the global image data includes pixel information of all n*m grids in the pre-projection area;

[0087] Step S2-2, in the global image data, each n*m grid is taken as a unit and a unique two-dimensional coordinate is assigned to it. With one corner of the pre-projection area as the origin, the horizontal coordinate is constructed in the horizontal direction, and the vertical coordinate is constructed in the vertical direction. The coordinate values ​​are assigned in sequence according to the arrangement order of the n*m ​​grids;

[0088] Step S2-3, constructing a projected foreign body set, performing foreign body recognition on the global image data according to the reference image, and storing the recognition result in the projected foreign body set;

[0089] Step S2-3-1, taking the n*m ​​grid grayscale value of the reference image as a reference, combining the grayscale co-occurrence matrix to extract the foreign body data in the pre-projection area, and obtain the grid two-dimensional coordinate data where the foreign body is located;

[0090] Step S2-3-2, construct a set of projected foreign bodies, and identify foreign bodies in the pre-projection area through the gray-level co-occurrence matrix, where each gray-level co-occurrence matrix includes multiple n*m ​​grids, and one n*m ​​grid corresponds to one element of the gray-level co-occurrence matrix. The contrast of the n*m ​​grids in the pre-projection area is calculated according to the gray-level co-occurrence matrix. The calculation formula of the contrast is as follows:

[0091] ;

[0092] Where D is the contrast of multiple n*m ​​grids in the gray-level co-occurrence matrix; B is the total number of gray levels; P(i, j) is the frequency of occurrence of gray values ​​i and j in the gray-level co-occurrence matrix at a given direction and distance; i and j are the indexes of the row and column in the gray-level co-occurrence matrix, respectively, representing the gray value of a pixel in the image; (ij) 2 It is expressed as the intensity value of the gray value change in the gray-level co-occurrence matrix;

[0093] The energy of the n*m ​​grid in the pre-projection area is calculated according to the gray-level co-occurrence matrix. The energy calculation formula is as follows:

[0094] ;

[0095] Where E represents the complex value of the image texture in the pre-projection area; P(ij) 2 It is expressed as the intensity of the frequency of occurrence of gray values ​​i and j in the gray-level co-occurrence matrix;

[0096] Calculate the entropy value of the n*m grid in the pre-projection area according to the gray-level co-occurrence matrix. The calculation formula of the entropy value is as follows:

[0097] ;

[0098] In the formula, C represents the randomness intensity of the image gray value distribution in the pre-projection area; log2P(i, j) represents the quantization calculation for the image;

[0099] In the formula, G real represents the color influence value of the n*m grid in the pre-projection area on the projection of the projection device; w1, w2, and w3 represent the weights of each eigenvalue on the total influence value;

[0100] Calculate the average value g and the standard deviation b of the reference image color influence value according to the reference image, and set the foreign object judgment threshold G max , and the setting uses the following formula:

[0101] G max =Q*b;

[0102] In the formula, G max represents the maximum value of the color influence fluctuation in the n*m grid; Q represents the color influence coefficient;

[0103] When |G real -g|≥G max , it means that the image data in the n*m grid is foreign object image data;

[0104] When |G real -g|<G max , it means that the image data in the n*m grid is foreign object-free image data;

[0105] G=G real -g;

[0106] In the formula, G represents the color influence value of the n*m grid in the pre-projection area on the projection of the projection device;

[0107] For example, the co-occurrence gray matrix P(i, j) is as follows:

[0108]

[0109] Calculate the contrast according to the formula, and we can get: D = 0.125 + 0.25 + 0.125 + 0.0625 + 0.25 + 0.25 + 0.0625 + 0.125 + 0.25 + 0.125 = 1.625;

[0110] Calculate the energy according to the formula, and we can get:

[0111] E=(16+4+1+0+4+9+1+1+1+1+9+4+0+1+4+16=72) / 16=4.5;

[0112] According to the formula, the entropy value can be calculated:

[0113] C=(8+6+4+6+7.245+4+4+4+7.245+6+4+6+8) / 16≈4.73;

[0114] Contrast weight w1=0.4;

[0115] The weight of capability w1=0.3;

[0116] The entropy weight w1=0.3;

[0117] The color influence value G can be calculated as follows:

[0118] G=0.4×1.75+0.3×2.609375+0.3×4.73≈2.9;

[0119] Step S2-3-4, construct a projection foreign body set, calculate the color influence value G of the n*m ​​grid on the projection device, obtain the corresponding two-dimensional coordinate data, and store the color influence value G and the corresponding two-dimensional coordinate data of each grid into the projection foreign body set, wherein the projection foreign body set includes the color influence value G and the two-dimensional coordinate data of the n*m ​​grid on the projection device.

[0120] Step S3, performing a comprehensive projection analysis based on the projection foreign body set, wherein the comprehensive projection analysis includes the influence of foreign body color, area and position, and selecting the best projection area for projection;

[0121] Step S3-1, performing a comprehensive projection analysis based on the projection foreign body set, wherein the comprehensive projection analysis includes the influence of foreign body color, area and position, and the value of color influence is a color influence value G;

[0122] The area impact is calculated as follows:

[0123] ;

[0124] In the formula, S color，C It is expressed as the total area of ​​the grid adjacent to the G position whose color influence value is; H C It is represented by the number of grids adjacent to the G position whose color influence value is n; n is the length of the grid; m is the width of the grid;

[0125] For example, the length and width of an n*m grid are both 1 cm, the color influence value is G=2.9, and the number of adjacent grids is 4. According to the formula, we can get:

[0126] S color，C =4*1*1=4;

[0127] The projected foreign body set is analyzed and calculated to obtain the geometric center coordinates of the foreign body. The position influence of the foreign body is calculated in combination with the projection origin. The specific calculation process is as follows:

[0128] The abscissa of the geometric center of the foreign body is the color influence value G, and the average value of the abscissas of the adjacent grids;

[0129] The ordinate of the geometric center of the foreign body is the color impact value G, and the average ordinate of the grids adjacent to it;

[0130] The calculation of the influence of the foreign body position is combined with the calculation of the projection origin, using the following formula:

[0131] ;

[0132] Where A represents the influence value of the n*m ​​grid in the pre-projection area on the projection position of the projection device; R represents the coefficient for controlling the magnitude of the influence value; x0 represents the horizontal coordinate of the projection origin; y0 represents the vertical coordinate of the projection origin; x center Expressed as the horizontal coordinate of the foreign body, y center It is represented as the ordinate of the foreign body; p is represented as the attenuation index of the foreign body position from the projection origin;

[0133] For example, the coordinates of the projection origin are (0, 0), the coordinates of the foreign body are (4, 5), the control influence value coefficient is R=1, and the attenuation exponent p=2. The influence value of the foreign body position can be calculated according to the formula as follows:

[0134] A=1*(1 / (4-0)*(4-0)+(5-0)*(5-0))=1 / 41≈0.0244;

[0135] Step S3-2: According to the color impact value G and the area impact S color，C The area with the least foreign matter influence in the pre-projection area is calculated by the foreign matter position influence A, and is taken as the optimal projection area. The comprehensive foreign matter influence calculation is performed on the pre-projection area, and the calculation is performed using the following formula:

[0136] ;

[0137] Where Z represents the impact value of foreign matter in the pre-projection area; w G Expressed as the weight of the color influence value; w S Expressed as the weight of the area impact value; w A The weight expressed as the position influence value;

[0138] Select the area with the smallest Z value as the best projection area;

[0139] For example, the weight w of the color influence value G =0.5; weight of area influence value w G =0.3; weight of position influence value w G =0.2; Substituting the above calculation results into the formula, we can get:

[0140] Z=(0.5*2.9)+(0.3*4)+(0.2*0.0244)=2.65488;

[0141] Region 1, three foreign bodies, {2.5, 2.0, 3.0};

[0142] Region 2, four foreign bodies, {1.2, 1.8, 0.2, 1.3};

[0143] Area 3, 1 foreign object, {6};

[0144] Then calculate the sum of the effects of foreign matter, Z all It is expressed as the sum of the influence values ​​of foreign matter in any projection area, and we can get:

[0145] Area 1: Z all =2.5+2.0+3.0=7.5;

[0146] Area 2: Z all =1.2+1.8+0.2+1.3=5;

[0147] Area 3: Z all =6;

[0148] By Z all Compare the size values ​​and select area 2 as the best projection area;

[0149] Step S4, obtaining the projection distance of the projection device, and calculating the matching degree between the projection area of ​​the current position of the projection device and the optimal projection area in combination with the projection distance and the projection offset of the projection device, wherein the projection offset includes a translation offset and an angle adjustment amount, and feeding back the position adjustment range of the projection device;

[0150] Step S4-1, obtain the projection distance of the projection device through the sensor, and calculate the matching degree between the projection area of ​​the current position of the projection device and the optimal projection area according to the projection origin and projection offset of the projection device. The matching degree calculation formula is as follows:

[0151] ;

[0152] Where F represents the maximum value of the matching degree when the current projection device position is used for projection; x now It is represented as the horizontal coordinate of the origin of the best projection area; ynow It is expressed as the ordinate of the origin of the best projection area; x obj The horizontal coordinate of the projection origin of the current projection device when the matching degree between the position projection of the current projection device and the optimal projection area reaches the maximum value; y obj The ordinate of the projection origin of the current projection device when the matching degree between the position projection of the current projection device and the optimal projection area reaches the maximum value;

[0153] Step S4-2: judge the calculated matching degree. The judging process is as follows:

[0154] When F=0, the current position of the projection device is determined. By adjusting the translation offset and angle adjustment, the optimal projection area can be fully matched and projection can be performed directly.

[0155] When F≠0, the current position of the projection device is determined to be unable to fully match the optimal projection area by adjusting the translation offset and the angle adjustment, and a position adjustment signal is issued.

[0156] Step S5: acquiring the projection distance of the projection device according to the adjusted position of the projection device, and adjusting the projection offset of the projection device in combination with the projection origin of the optimal projection area.

[0157] Step S5-1, obtaining the two-dimensional coordinate information corresponding to the adjusted position of the projection device, and obtaining the projection distance of the adjusted position of the projection device again through the sensor;

[0158] Step S5-2, controlling the projection origin adjustment of the projection device according to the two-dimensional coordinate information corresponding to the adjusted position of the projection device and the projection origin coordinates of the optimal projection area, so that the projection origin of the projection device after the position adjustment finally coincides with the projection origin of the optimal projection area.

[0159] For example, the projection origin of the best projection area is (0, 0), the projection origin of the current projection device is (2, 1), the horizontal translation offset is 5, and the vertical translation offset is 5. By adjusting the projection offset of the projection device, the projection origin of the current projection device can be made to coincide with the projection origin of the best projection area without adjusting the position of the projection device.

[0160] Embodiment 2, as Figure 2 As shown, the present invention provides an intelligent adjustment system for a projection device, the intelligent adjustment system comprising: a reference image acquisition module, a global image coordinate module, a foreign body recognition module, a projection intelligent selection module and a projection offset adjustment module;

[0161] The reference image acquisition module is used to project a detection pattern on a part of the pre-projection area, and analyze and extract image data in a foreign-matter-free state; the global image coordinate module is used to collect comprehensive image data of the pre-projection area, and construct a two-dimensional coordinate to assign a unique two-dimensional coordinate to each grid; the foreign matter identification module is used to identify foreign matter information in the pre-projection area; the projection intelligent selection module is used to select the best projection area from the pre-projection area; the projection offset adjustment module is used to control the projection offset of the projection device;

[0162] The output end of the reference image acquisition module is electrically connected to the input end of the global image coordinate module; the output end of the global image coordinate module is electrically connected to the input end of the foreign object recognition module; the output end of the foreign object recognition module is electrically connected to the input end of the projection intelligent selection module; the output end of the projection intelligent selection module is electrically connected to the input end of the projection offset adjustment module.

[0163] The reference image acquisition module includes a local image acquisition unit and a reference image generation unit; the local image acquisition unit is used to acquire the detection pattern; the reference image generation unit is used to extract the image in the state without foreign matter and generate the reference image;

[0164] The global image coordinate module includes a global image acquisition unit and a coordinate management unit; the global image acquisition unit is used to acquire an omnidirectional image of the pre-projection area; the coordinate management unit is used to generate a two-dimensional coordinate according to the projected detection pattern;

[0165] The foreign object recognition module includes a foreign object recognition unit and a foreign object coordinate unit; the foreign object recognition unit is used to recognize foreign object data in the pre-projection area; and the foreign object coordinate unit is used to calculate the geometric center coordinates of the foreign object.

[0166] The projection intelligent selection module includes a foreign body impact calculation unit and an optimal projection origin unit; the foreign body position calculation unit is used to calculate the comprehensive projection impact value according to the color, area and position of the foreign body; the optimal projection origin unit is used to manage the projection origin coordinate data of the optimal projection area;

[0167] The projection offset adjustment module includes a projection origin adjustment unit and a projection offset correction unit; the projection origin adjustment unit is used to adjust the projection origin coordinates of the current projection device to align with the optimal area; the projection offset correction unit is used to control the translation offset and angle adjustment amount.

[0168] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent adjustment method for a projection device, characterized in that: The intelligent adjustment method includes the following steps: Step S1, projecting a detection pattern partially on the pre-projection area through a projection device, and using an image sensor to collect data on the detection pattern in the pre-projection area, extracting image data in a foreign body-free state and saving it as a reference image; Step S2, collecting global image data of the pre-projection area to construct global image data, constructing two-dimensional coordinates for the global image data, identifying foreign objects in the global image data according to the reference image, and constructing a projection foreign object set to store the identification results; Step S3, performing a comprehensive projection analysis based on the projection foreign body set, wherein the comprehensive projection analysis includes the influence of foreign body color, area and position, and selecting the best projection area for projection; Step S4, obtaining the projection distance of the projection device, and calculating the matching degree between the projection area of ​​the current position of the projection device and the optimal projection area in combination with the projection distance and the projection offset of the projection device, wherein the projection offset includes a translation offset and an angle adjustment amount, and feeding back the position adjustment range of the projection device; Step S5: acquiring the projection distance of the projection device according to the adjusted position of the projection device, and adjusting the projection offset of the projection device in combination with the projection origin of the optimal projection area.

2. The intelligent adjustment method for a projection device according to claim 1, characterized in that: The specific steps of step S1 are as follows: Step S1-1, selecting any local area of ​​the pre-projection area, wherein the pre-projection area is represented by an area to be projected defined by an image sensor before the projection device performs actual projection; using the projection device to project a detection pattern, wherein the detection pattern is a plurality of n*m grids; Step S1-2, using an image sensor to collect image data of the detection pattern in the pre-projection area to extract image data in a foreign matter-free state, where the foreign matter-free state refers to a projection environment in which no interference affects the projection; marking the n*m ​​grids with the most identical grayscale values, and saving the image data consisting of the marked continuous multiple n*m ​​grids as a reference image.

3. The intelligent adjustment method for projection equipment according to claim 2, characterized in that: The specific steps of step S2 are as follows: Step S2-1, performing omnidirectional image acquisition on the pre-projection area through an image sensor, wherein the omnidirectional image acquisition is performed on the entire area used for projection in the pre-projection area, acquiring detection pattern data projected by the projection device on the pre-projection area, generating complete image data covering the pre-projection area, and constructing global image data, wherein the global image data includes pixel information of all n*m grids in the pre-projection area; Step S2-2, in the global image data, each n*m grid is taken as a unit and a unique two-dimensional coordinate is assigned to it. With one corner of the pre-projection area as the origin, the horizontal coordinate is constructed in the horizontal direction, and the vertical coordinate is constructed in the vertical direction. The coordinate values ​​are assigned in sequence according to the arrangement order of the n*m ​​grids; Step S2-3: construct a projected foreign body set, perform foreign body recognition on the global image data according to the reference image, and store the recognition result in the projected foreign body set.

4. The intelligent adjustment method for projection equipment according to claim 3, characterized in that: The specific steps of step S2-3 are as follows: Step S2-3-1, taking the n*m ​​grid grayscale value of the reference image as a reference, combining the grayscale co-occurrence matrix to extract the foreign body data in the pre-projection area, and obtain the grid two-dimensional coordinate data where the foreign body is located; Step S2-3-2, construct a set of projected foreign bodies, and identify foreign bodies in the pre-projection area through the gray-level co-occurrence matrix, where each gray-level co-occurrence matrix includes multiple n*m ​​grids, and one n*m ​​grid corresponds to one element of the gray-level co-occurrence matrix. The contrast of the n*m ​​grids in the pre-projection area is calculated according to the gray-level co-occurrence matrix. The calculation formula of the contrast is as follows: ； Where D is the contrast of multiple n*m ​​grids in the gray-level co-occurrence matrix; B is the total number of gray levels; P(i, j) is the frequency of occurrence of gray values ​​i and j in the gray-level co-occurrence matrix at a given direction and distance; i and j are the indexes of the row and column in the gray-level co-occurrence matrix, respectively, representing the gray value of a pixel in the image; (ij) 2 It is expressed as the intensity value of the gray value change in the gray-level co-occurrence matrix; The energy of the n*m ​​grid in the pre-projection area is calculated according to the gray-level co-occurrence matrix. The energy calculation formula is as follows: ； Where E represents the complex value of the image texture in the pre-projection area; P(ij) 2 It is expressed as the intensity of the frequency of occurrence of gray values ​​i and j in the gray-level co-occurrence matrix; The entropy value of the n*m ​​grid in the pre-projection area is calculated according to the gray-level co-occurrence matrix. The entropy value calculation formula is as follows: ； Where C represents the randomness intensity of the gray value distribution of the image in the pre-projection area; log2P(i, j) represents the quantization calculation used for the image; The color influence value of the n*m ​​grid in the pre-projection area is calculated using the following formula: ； In the formula, G real It is represented by the color influence value of n*m grids in the pre-projection area on the projection of the projection device; w1, w2 and w3 represent the weight of each characteristic value on the total influence value; Calculate the average value g and standard deviation b of the reference image color influence value based on the reference image, and set the foreign matter judgment threshold G based on the average value g and standard deviation b max , set up using the following formula: G max =Q*b; In the formula, G max It is expressed as the maximum value of the color influence fluctuation in the n*m ​​grid; Q is expressed as the color influence coefficient; When |G real - g| ≥ G max it indicates that the image data within the n*m grid is foreign object image data; When |G real - g| < G max it indicates that the image data in the n*m grid is foreign object-free image data; G=G real -g; Where G represents the color impact value of the n*m ​​grid in the pre-projection area on the projection of the projection device; Step S2-3-3, construct a projection foreign body set, calculate the color influence value G of the n*m ​​grid on the projection device, obtain the corresponding two-dimensional coordinate data, and store the color influence value G and the corresponding two-dimensional coordinate data of each grid into the projection foreign body set, wherein the projection foreign body set includes the color influence value G and the two-dimensional coordinate data of the n*m ​​grid on the projection device.

5. The intelligent adjustment method for projection equipment according to claim 4, characterized in that: The specific steps of step S3 are as follows: Step S3-1, performing a comprehensive projection analysis based on the projection foreign body set, wherein the comprehensive projection analysis includes the influence of foreign body color, area and position, and the value of color influence is a color influence value G; The area impact is calculated as follows: ； In the formula, S color，C It is expressed as the total area of ​​the grid adjacent to the G position whose color influence value is; H C It is represented by the number of grids adjacent to the G position whose color influence value is n; n is the length of the grid; m is the width of the grid; The projected foreign body set is analyzed and calculated to obtain the geometric center coordinates of the foreign body. The position influence of the foreign body is calculated in combination with the projection origin. The specific calculation process is as follows: The abscissa of the geometric center of the foreign body is the color influence value G, and the average value of the abscissas of the adjacent grids; The ordinate of the geometric center of the foreign body is the color impact value G, and the average ordinate of the grids adjacent to it; The calculation of the influence of the foreign body position is combined with the calculation of the projection origin, using the following formula: ； Where A represents the influence value of the n*m ​​grid in the pre-projection area on the projection position of the projection device; R represents the coefficient for controlling the magnitude of the influence value; x0 represents the horizontal coordinate of the projection origin; y0 represents the vertical coordinate of the projection origin; x center Expressed as the horizontal coordinate of the foreign body, y center It is represented as the ordinate of the foreign body; p is represented as the attenuation index of the foreign body position from the projection origin; Step S3-2: According to the color impact value G and the area impact S color，C The area with the least foreign matter influence in the pre-projection area is calculated by the foreign matter position influence A, and is taken as the optimal projection area. The comprehensive foreign matter influence calculation is performed on the pre-projection area, and the calculation is performed using the following formula: ； Where Z represents the impact value of foreign matter in the pre-projection area; w G Expressed as the weight of the color influence value; w S Expressed as the weight of the area impact value; w A The weight expressed as the position influence value; The area with the smallest Z value is selected as the best projection area.

6. The intelligent adjustment method for projection equipment according to claim 5, characterized in that: The specific steps of step S4 are as follows: Step S4-1, obtain the projection distance of the projection device through the sensor, and calculate the matching degree between the projection area of ​​the current position of the projection device and the optimal projection area according to the projection origin and projection offset of the projection device. The matching degree calculation formula is as follows: ； Where F represents the maximum value of the matching degree when the current projection device position is used for projection; x now It is represented as the horizontal coordinate of the origin of the best projection area; y now It is expressed as the ordinate of the origin of the best projection area; x obj The horizontal coordinate of the projection origin of the current projection device when the matching degree between the position projection of the current projection device and the optimal projection area reaches the maximum value; y obj The ordinate of the projection origin of the current projection device when the matching degree between the position projection of the current projection device and the optimal projection area reaches the maximum value; Step S4-2: judge the calculated matching degree. The judging process is as follows: When F=0, the current position of the projection device is determined. If the translation offset and angle adjustment can completely match the optimal projection area, projection is performed directly. When F≠0, the current position of the projection device is determined to be unable to fully match the optimal projection area by adjusting the translation offset and the angle adjustment, and a position adjustment signal is issued.

7. The intelligent adjustment method for projection equipment according to claim 6, characterized in that: The specific steps of step S5 are as follows: Step S5-1, obtaining the two-dimensional coordinate information corresponding to the adjusted position of the projection device, and obtaining the projection distance of the adjusted position of the projection device again through the sensor; Step S5-2, controlling the projection origin adjustment of the projection device according to the two-dimensional coordinate information corresponding to the adjusted position of the projection device and the projection origin coordinates of the optimal projection area, so that the projection origin of the projection device after the position adjustment finally coincides with the projection origin of the optimal projection area.

8. An intelligent adjustment system for a projection device, applied to an intelligent adjustment method for a projection device according to any one of claims 1 to 7, characterized in that: The intelligent adjustment system includes a reference image acquisition module, a global image coordinate module, a foreign body recognition module, a projection intelligent selection module and a projection offset adjustment module; The reference image acquisition module is used to project a detection pattern on a part of the pre-projection area, and analyze and extract image data in a foreign-matter-free state; the global image coordinate module is used to collect comprehensive image data of the pre-projection area, and construct a two-dimensional coordinate to assign a unique two-dimensional coordinate to each grid; the foreign matter identification module is used to identify foreign matter information in the pre-projection area; the projection intelligent selection module is used to select the best projection area from the pre-projection area; the projection offset adjustment module is used to control the projection offset of the projection device; The output end of the reference image acquisition module is electrically connected to the input end of the global image coordinate module; the output end of the global image coordinate module is electrically connected to the input end of the foreign object recognition module; the output end of the foreign object recognition module is electrically connected to the input end of the projection intelligent selection module; the output end of the projection intelligent selection module is electrically connected to the input end of the projection offset adjustment module.

9. The intelligent adjustment system for projection equipment according to claim 8, characterized in that: The reference image acquisition module includes a local image acquisition unit and a reference image generation unit; the local image acquisition unit is used to acquire the detection pattern; the reference image generation unit is used to extract the image in the state without foreign matter and generate the reference image; The global image coordinate module includes a global image acquisition unit and a coordinate management unit; The global image acquisition unit is used to acquire an omnidirectional image of the pre-projection area; the coordinate management unit is used to generate two-dimensional coordinates according to the projected detection pattern; The foreign object recognition module includes a foreign object recognition unit and a foreign object coordinate unit; the foreign object recognition unit is used to recognize foreign object data in the pre-projection area; and the foreign object coordinate unit is used to calculate the geometric center coordinates of the foreign object.

10. The intelligent adjustment system for projection equipment according to claim 8, characterized in that: The projection intelligent selection module includes a foreign body impact calculation unit and an optimal projection origin unit; the foreign body impact calculation unit is used to calculate the comprehensive projection impact value according to the color, area and position of the foreign body; the optimal projection origin unit is used to manage the projection origin coordinate data of the optimal projection area; The projection offset adjustment module includes a projection origin adjustment unit and a projection offset correction unit; the projection origin adjustment unit is used to adjust the projection origin coordinates of the current projection device to align with the optimal area; the projection offset correction unit is used to control the translation offset and angle adjustment amount.

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