Camera installation deviation calibration method and device applied to regional inspection

Abstract

The invention relates to a camera installation deviation calibration method and device applied to regional inspection, and the method comprises the steps: carrying out the parameter extraction of camera equipment, and obtaining a target parameter; determining an equipment installation position of the camera equipment, performing environment acquisition on the equipment installation position to obtain an equipment environment, constructing a panoramic image of the equipment installation position, and performing feature extraction on the panoramic image to obtain a feature image; performing image shooting on the equipment mounting position by using the camera equipment to obtain a shot image, performing image synthesis on the shot image to obtain a synthesized image, and calculating a position deviation coefficient of the camera equipment; and calculating the overlap ratio of the panoramic image and the composite image, determining an angle deviation coefficient of the camera equipment in combination with the overlap ratio and the target parameter, and calibrating the installation position of the camera equipment according to the position deviation coefficient and the angle deviation coefficient. The calibration accuracy of the installation deviation of the camera applied to regional inspection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment installation calibration, and in particular to a calibration method and device for camera installation deviations used in regional inspections. Background Art

[0002] Inspection refers to the regular and systematic inspection and examination of areas (including power grid areas, specific waters, mines, and production workshops, etc.) to complete production safety inspections and equipment operation and maintenance inspections within the area. Through regional inspections, possible safety hazards can be discovered and eliminated in a timely manner, and corresponding measures can be taken to repair or improve them. Currently, the purpose of regional inspections is mainly achieved based on camera shooting.

[0003] After installation, the camera needs to be calibrated at the installation position to ensure that it can accurately shoot the area. However, the existing camera calibration method mainly uses photoelectric detection technology to convert and detect the camera's photoelectric signals to calibrate the camera's installation position. However, this method does not take into account the camera's actual shooting conditions and installation position, resulting in a decrease in the calibration accuracy of the camera's installation position. Therefore, a method is needed to improve the calibration accuracy of camera installation deviations for area inspections. Summary of the Invention

[0004] The present invention provides a method and device for calibrating the installation deviation of a camera used for regional inspection, the main purpose of which is to improve the calibration accuracy of the installation deviation of the camera used for regional inspection.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for calibrating camera installation deviations for regional inspection, comprising:

[0006] Acquire a camera device to be calibrated, extract parameters of the camera device, and obtain target parameters;

[0007] Determining a device installation location of the camera device within the area, performing environmental acquisition on the device installation location to obtain a device environment, constructing a panoramic image of the device installation location based on the device environment, and performing feature extraction on the panoramic image to obtain a feature image;

[0008] Using the camera to capture an image of the device installation position to obtain a captured image, synthesizing the captured image to obtain a synthesized image, and combining the feature image and the synthesized image to calculate a position deviation coefficient of the camera;

[0009] Calculate the degree of overlap between the panoramic image and the composite image, determine the angular deviation coefficient of the camera device in combination with the degree of overlap and the target parameter, and calibrate the installation position of the camera device based on the position deviation coefficient and the angular deviation coefficient.

[0010] At the same time, the present invention also provides a device for calibrating camera installation deviations used for regional inspections, the device comprising:

[0011] A parameter extraction module is used to obtain the camera device to be calibrated, extract parameters of the camera device, and obtain target parameters;

[0012] a feature extraction module, configured to determine the device installation location of the camera device within the area, perform environmental acquisition on the device installation location to obtain a device environment, construct a panoramic image of the device installation location based on the device environment, and perform feature extraction on the panoramic image to obtain a feature image;

[0013] a position deviation calculation module, configured to use the camera to capture an image of the device installation position to obtain a captured image, synthesize the captured image to obtain a synthesized image, and calculate a position deviation coefficient of the camera by combining the feature image and the synthesized image;

[0014] The device calibration module is used to calculate the overlap between the panoramic image and the composite image, determine the angular deviation coefficient of the camera device based on the overlap and the target parameter, and calibrate the installation position of the camera device according to the position deviation coefficient and the angular deviation coefficient.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The present invention obtains the camera device to be calibrated and extracts parameters of the camera device to obtain parameter information of the camera device, so as to better understand the attribute information of the camera device and further determine the device installation position of the camera device in the area. The environment is collected according to the device installation position to obtain the environmental information of the device installation position, which provides a guarantee for the subsequent construction of panoramic images and calculation of the position deviation coefficient of the camera device.

[0017] In addition, the present invention calculates the overlap degree of the panoramic image and the synthetic image to grasp the overlap degree of the panoramic image and the synthetic image, thereby providing a guarantee for subsequently improving the accuracy of the installation position calibration of the camera equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a method for calibrating camera installation deviations for regional inspections provided by one embodiment of the present invention;

[0019] Figure 2 A functional module diagram of a device for calibrating camera installation deviations for regional inspections provided by one embodiment of the present invention;

[0020] Figure 3 A schematic structural diagram of an electronic device for implementing the method for calibrating camera installation deviations for regional inspections provided in one embodiment of the present invention.

[0021] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] The present application provides a method for calibrating the installation deviation of a camera applied to regional inspections, and the execution subject thereof includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the present application. In other words, the calibration method for the installation deviation of a camera applied to regional inspections can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0024] Example 1:

[0025] Reference Figure 1 FIG. 1 is a flow chart of a method for calibrating camera installation deviations for regional inspections according to an embodiment of the present invention. The region includes any scenario requiring equipment inspection, operation and maintenance, or environmental monitoring, such as a power grid installation area, a specific water area, a mine, or a production workshop. The calibration method includes steps S1-S4.

[0026] S1. Obtain a camera device to be calibrated, extract parameters of the camera device, and obtain target parameters.

[0027] The present invention obtains the camera device to be calibrated and extracts parameters of the camera device to obtain parameter information of the camera device, so as to better understand the attribute information of the camera device.

[0028] Among them, the camera device is a device that converts optical image signals into electrical signals for storage or transmission, such as a video camera and a surveillance camera, and the device parameters are the attribute introduction information of the camera device, such as the device name, model and performance introduction.

[0029] Furthermore, the parameter extraction of the camera device to obtain the target parameters includes: extracting the parameter text of the camera device, performing semantic analysis on the parameter text to obtain text semantics, calculating the text weight of the parameter text based on the text semantics, performing text filtering on the parameter text based on the text weight to obtain the target text, performing vector conversion on the target text to obtain a text vector, calculating the vector distance of the text vector, and classifying the parameter text based on the vector distance to obtain the target parameters.

[0030] Among them, the parameter text is the text information corresponding to the parameters of the camera device, the text semantics is the meaning and explanation corresponding to the parameter text, the text weight is the importance corresponding to each text in the parameter text, the target text is the text obtained after filtering the parameter text according to the text weight, the text vector is the vector expression corresponding to the target text, and the vector distance is the distance between each vector in the text vector, which can represent the close relationship between two vectors.

[0031] Furthermore, the parameter text of the camera device can be extracted through OCR recognition technology, the semantic analysis of the parameter text can be achieved through semantic analysis, the text filtering of the parameter text can be achieved through a text filter, the text filter is compiled by Java language, the vector conversion of the target text can be achieved through the word2vec algorithm, the vector distance of the text vector can be calculated through the Euclidean distance algorithm, and the classification of the parameter text can be achieved through a decision tree algorithm.

[0032] As an optional embodiment of the present invention, the calculating the text weight of the parameter text includes:

[0033] The text weight of the parameter text is calculated by the following formula:

[0034]

[0035] Among them, E represents the text weight of the parameter text, C i Represents the feature vector of the i-th text in the parameter text, It represents the covariance of the feature vector of the i-th text in the parameter text, and trace() represents the spatial filtering function.

[0036] S2. Determine the device installation location of the camera device in the area, collect the environment of the device installation location to obtain the device environment, construct a panoramic image of the device installation location based on the device environment, and extract features from the panoramic image to obtain a feature image.

[0037] The present invention determines the device installation position of the camera device in the area, and collects the environment of the camera device based on the device installation position, so as to obtain the environmental information of the device installation position, thereby providing a guarantee for the subsequent construction of a panoramic image of the camera device.

[0038] Among them, the equipment installation location is the place where the camera equipment needs to be installed in the area. For example, if it is installed at the mine entrance, it can prevent unauthorized personnel from entering or leaving the area. If it is installed in the working area, it can monitor whether the workers are working in accordance with the safety operating procedures. If it is installed in a high-risk area, potential dangers and accident hazards can be discovered in time. The equipment environment is the environment around the equipment installation location. Furthermore, the equipment installation location of the camera equipment in the area can be determined by manual selection or human-computer interaction, and the environmental collection of the equipment installation location can be achieved through an environment collector.

[0039] Therefore, by constructing a panoramic image of the device installation location based on the device environment and converting the device environment into a corresponding image, it is convenient for subsequent calibration of the camera device, wherein the panoramic image is an image expression corresponding to the device environment.

[0040] Furthermore, constructing a panoramic image of the device installation location based on the device environment includes: performing attribute analysis on the device environment to obtain environmental attributes, rendering a three-dimensional panoramic environment corresponding to the device environment based on the environmental attributes, constructing a view matrix corresponding to each view of the three-dimensional panoramic environment, performing weighted summation on the view matrix to obtain a target matrix, performing a two-dimensional conversion on the three-dimensional panoramic environment in combination with the target matrix to obtain a two-dimensional panoramic image, and color buffering the two-dimensional panoramic image to obtain a panoramic image of the device installation location.

[0041] Among them, the environmental attributes are basic information corresponding to the device environment, such as information such as objects and colors in the environment; the three-dimensional panoramic environment is the expression form of the three-dimensional model corresponding to the device environment; the view matrix is ​​the image matrix obtained after the three-dimensional panoramic environment is converted under different viewing angles; the target matrix is ​​the matrix obtained by calculating the average value of each matrix in the view matrix; and the two-dimensional panoramic image is the two-dimensional expression form corresponding to the three-dimensional panoramic environment.

[0042] Furthermore, attribute analysis of the device environment can be achieved through an attribute analysis tool, which is compiled by a scripting language. Rendering of the three-dimensional panoramic environment corresponding to the device environment can be achieved through a rendering tool, such as 3D MAX. The view matrix corresponding to each view of the three-dimensional panoramic environment can be constructed by a matrix function, which is compiled by Java language. The two-dimensional conversion of the three-dimensional panoramic environment can be achieved through CAD software, and color buffering of the two-dimensional panoramic image can be achieved through a GPU.

[0043] The present invention can obtain the characteristic part of the panoramic image by extracting features from the panoramic image, which provides a guarantee for the subsequent calculation of the position deviation coefficient of the camera device, wherein the characteristic image is a representative image in the panoramic image.

[0044] Furthermore, the feature extraction of the panoramic image to obtain a feature image includes: entity marking of the panoramic image to obtain an image entity, identifying image textures in the image entity, calculating the complexity of each texture in the image texture to obtain texture complexity, screening the image entity to obtain a target image entity when the texture complexity is greater than a preset threshold, calculating the grayscale value of each pixel in the target image entity to obtain an entity grayscale value, feature screening the target image entity based on the entity grayscale value to obtain a feature image entity, and feature extraction of the panoramic image based on the feature image entity to obtain a feature image.

[0045] Among them, the image entity is the image of the real object in the panoramic image, which can be used as a reference or representative object; the image texture is the image texture of the image entity; the texture complexity represents the complexity of the image texture; the preset threshold is a standard value for judging the texture complexity; the target image entity is an entity whose texture complexity is greater than the preset threshold; the entity grayscale value is the brightness value corresponding to only one sampled color of the target image entity; and the feature image entity is a representative entity among the target image entities.

[0046] Furthermore, entity marking of the panoramic image can be achieved through an entity marking tool, which is compiled by a scripting language, identifying image textures in the image entities can be achieved through an LBP algorithm, screening the image entities can be achieved through a VLOOKUP function, calculating the grayscale value of each pixel in the target image entity can be achieved through a floating-point method, feature screening of the target image entity can be achieved through a genetic algorithm, and feature extraction of the panoramic image can be achieved through a SIFT feature extraction method.

[0047] Calculating the complexity of each texture in the image texture to obtain texture complexity includes:

[0048] The complexity of each texture in the image texture can be calculated by the following formula:

[0049]

[0050] Among them, B represents the complexity of each texture in the image texture, d*d represents the value range of the image texture, a and b represent the texture positions of the image texture in the d*d range, and D(a, b) represents the mean square error corresponding to the image texture at the (a, b) position.

[0051] S3. Use the camera device to capture an image of the device installation position to obtain a captured image, synthesize the captured image to obtain a synthesized image, and calculate the position deviation coefficient of the camera device by combining the feature image and the synthesized image.

[0052] The present invention can obtain a captured image by using the camera device to capture the device installation position, which provides a guarantee for the subsequent calculation of the position deviation coefficient of the camera device, wherein the captured image is an image obtained by capturing the device installation position by the camera device.

[0053] Furthermore, the present invention can obtain a composite image by performing image synthesis on the captured images, so as to synthesize multiple captured images into a complete image, providing convenience for subsequent processing, wherein the composite image is a complete image synthesized by synthesizing several captured images.

[0054] Specifically, the image synthesis of the captured image to obtain a synthesized image includes: marking the edges of the captured image to obtain an edge image, measuring the number of pixels in the edge image, constructing a pixel matrix of the edge image based on the number of pixels, calculating the matrix variance of each matrix in the pixel matrix, performing equalization processing on the edge image based on the matrix variance to obtain a balanced edge image, calculating the image fit of the balanced edge image, and performing image synthesis on the captured image based on the image fit to obtain a synthesized image.

[0055] Among them, the edge image is the image located at the edge position in the captured image, the pixel matrix is ​​a square matrix composed of pixel points, the matrix variance is a measure of the discrete degree of the pixel matrix, the balanced edge image is the image obtained after the edge image is balanced, and the image fit represents the degree of fit of the edge image.

[0056] Furthermore, edge marking of the captured image can be achieved through the CVAT tool, measuring the number of pixels in the edge image can be achieved through a summation function, such as the SUM function, calculating the matrix variance of each matrix in the pixel matrix can be achieved through a variance calculator, the image fit can be obtained by calculating the cosine value of the angle between each image in the balanced edge image, and image synthesis of the captured image can be achieved through an image synthesizer.

[0057] The equalizing processing of the edge image to obtain the equalized edge image includes:

[0058]

[0059] Where G represents the balanced edge image, μ represents the interference coefficient of the edge image, j represents the starting pixel of the edge image, X represents the total pixel value of the pixel points of the edge image, and H j Represents the number of pixels corresponding to the pixel value j, K represents the variance coefficient of the matrix variance, and ω represents the mapping value corresponding to the variable item in the edge image.

[0060] The present invention calculates the position deviation coefficient of the camera device by combining the characteristic image and the composite image. The degree of deviation of the camera device can be understood based on the position deviation coefficient, so as to facilitate position adjustment of the camera device, wherein the position deviation coefficient is the degree of deviation between the camera device and the standard installation position.

[0061] The method of calculating the position deviation coefficient of the camera device by combining the feature image and the composite image includes: constructing coordinate systems corresponding to the feature image and the composite image respectively to obtain a first coordinate system and a second coordinate system, overlapping the first coordinate system and the second coordinate system to obtain an overlapping coordinate system, calculating the angular deviation coefficient of the same coordinate axis in the overlapping coordinate system, and obtaining the position deviation coefficient of the camera device based on the angular deviation coefficient.

[0062] Among them, the first coordinate system and the second coordinate system are the coordinate systems corresponding to the feature image and the synthetic image respectively, the overlapping coordinate system is the coordinate system obtained by merging the first coordinate system and the second coordinate system together, and the angular deviation coefficient is the degree of separation of the same coordinate axis in the overlapping coordinate system, and the same coordinate axis is the degree of separation between the two x axes, or the two y axes in the first coordinate system and the second coordinate system.

[0063] Furthermore, constructing the coordinate systems corresponding to the feature image and the composite image respectively can be achieved through solidwork software, and the first coordinate system and the second coordinate system can be overlapped by overlapping the origins of the two coordinate systems. The angular deviation coefficient can be obtained by calculating the angle value of the same coordinate axis in the overlapping coordinate system.

[0064] S4. Calculate the degree of overlap between the panoramic image and the composite image, determine the angle deviation coefficient of the camera device based on the degree of overlap and the target parameter, and calibrate the installation position of the camera device according to the position deviation coefficient and the angle deviation coefficient.

[0065] The present invention calculates the degree of overlap between the panoramic image and the composite image, and the degree of overlap between the panoramic image and the composite image can be understood through the degree of overlap, thereby improving the accuracy of subsequent calibration of the installation position of the camera equipment, wherein the degree of overlap represents the degree of overlap between the panoramic image and the composite image.

[0066] Wherein, the calculation of the degree of overlap between the panoramic image and the composite image includes: respectively locating the image centers of the panoramic image and the composite image, respectively measuring the horizontal angles of the panoramic image and the composite image to obtain a first horizontal angle and a second horizontal angle; if the first horizontal angle and the second horizontal angle are inconsistent, adjusting the angles of the panoramic image and the composite image until the first horizontal angle and the second horizontal angle are consistent; and calculating the degree of overlap between the panoramic image and the composite image based on the image centers.

[0067] The image center is the center of the panoramic image and the composite image, and the first horizontal angle and the second horizontal angle are the horizontal angles corresponding to entities in the panoramic image and the composite image, respectively.

[0068] Furthermore, locating the image centers of the panoramic image and the composite image can be achieved by using the ruler tool in the PS software, measuring the horizontal angles of the panoramic image and the composite image can be achieved by using an angle measuring instrument, and the degree of overlap can be obtained by calculating the similarity between the panoramic image and the composite image.

[0069] The present invention determines the angle deviation coefficient of the camera device by combining the overlap and the target parameter. The angle deviation coefficient can be used to understand whether the angle of the camera device is standard, wherein the angle deviation coefficient is the degree of deviation of the shooting angle of the camera device. Furthermore, the actual shooting angle of the camera device can be understood through the target parameter, and the angle deviation coefficient can be determined in combination with the overlap.

[0070] The present invention calibrates the installation position of the camera device according to the position deviation coefficient and the angle deviation coefficient, thereby improving the accuracy of the calibration of the camera device, so as to improve the subsequent shooting accuracy of the camera device.

[0071] Therefore, the present invention can obtain parameter information of the camera device to better understand the attribute information of the camera device, further determine the device installation position of the camera device in the area, and perform environmental collection based on the device installation position to obtain the environmental information of the device installation position, thereby providing a guarantee for the subsequent construction of a panoramic image and calculation of the position deviation coefficient of the camera device;

[0072] In addition, the present invention calculates the overlap degree of the panoramic image and the synthetic image to grasp the overlap degree of the panoramic image and the synthetic image, thereby providing a guarantee for subsequently improving the accuracy of the installation position calibration of the camera equipment.

[0073] Example 2:

[0074] like Figure 2 Figure 1 shows the functional modules of a device 100 for calibrating camera installation deviations for regional inspections, according to one embodiment of the present invention. The device includes a parameter extraction module 101, a feature extraction module 102, a position deviation calculation module 103, and a device calibration module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by a processor of an electronic device and perform a fixed function. The module is stored in the memory of the electronic device.

[0075] In this embodiment, the functions of each module / unit are as follows:

[0076] The parameter extraction module 101 is used to obtain a camera device to be calibrated, extract parameters of the camera device, and obtain target parameters;

[0077] The feature extraction module 102 is configured to determine the device installation location of the camera device within the area, perform environmental collection on the device installation location to obtain a device environment, construct a panoramic image of the device installation location based on the device environment, and perform feature extraction on the panoramic image to obtain a feature image;

[0078] The position deviation calculation module 103 is configured to use the camera to capture an image of the device installation position to obtain a captured image, synthesize the captured image to obtain a synthesized image, and calculate the position deviation coefficient of the camera by combining the feature image and the synthesized image;

[0079] The device calibration module 104 is used to calculate the overlap between the panoramic image and the composite image, determine the angular deviation coefficient of the camera device based on the overlap and the target parameter, and calibrate the installation position of the camera device based on the position deviation coefficient and the angular deviation coefficient.

[0080] In detail, each module described in the device 100 for calibrating the camera installation deviation for regional inspection in the embodiment of the present application adopts the same method as above when in use. Figure 1 The same technical means as the calibration method of camera installation deviation applied to regional inspection described in and can produce the same technical effects are not described here.

[0081] Example 3:

[0082] like Figure 3 1 is a schematic structural diagram of an electronic device 1 for implementing a method for calibrating camera installation deviations for regional inspections, provided by an embodiment of the present invention.

[0083] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and run on the processor 10, such as the calibration method program for camera installation deviation applied to regional inspection as described in Example 1.

[0084] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0085] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0086] Among them, the calibration method program for camera installation deviation applied to regional inspection stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, the calibration method program for camera installation deviation applied to regional inspection described in Example 1 can be implemented.

[0087] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can implement the calibration method program for camera installation deviation applied to regional inspections described in Example 1.

[0088] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0089] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for calibrating camera installation deviations for regional inspections, characterized in that: The method comprises: Acquire a camera device to be calibrated, extract parameters of the camera device, and obtain target parameters; Determining a device installation location of the camera device within the area, performing environmental acquisition on the device installation location to obtain a device environment, constructing a panoramic image of the device installation location based on the device environment, and performing feature extraction on the panoramic image to obtain a feature image; Using the camera to capture an image of the device installation position to obtain a captured image, synthesizing the captured image to obtain a synthesized image, and combining the feature image and the synthesized image to calculate a position deviation coefficient of the camera; Calculate the degree of overlap between the panoramic image and the composite image, determine the angular deviation coefficient of the camera device in combination with the degree of overlap and the target parameter, and calibrate the installation position of the camera device based on the position deviation coefficient and the angular deviation coefficient.

2. The calibration method according to claim 1, wherein: The extracting parameters of the camera device to obtain target parameters includes: Extracting parameter text of the camera device, performing semantic analysis on the parameter text to obtain text semantics; According to the text semantics, the text weight of the parameter text is calculated by the following formula; Among them, E represents the text weight of the parameter text, C i Represents the feature vector of the i-th text in the parameter text, Represents the covariance of the feature vector of the i-th text in the parameter text, trace() represents the spatial filtering function; Performing text filtering on the parameter text according to the text weight to obtain the target text; Performing vector conversion on the target text to obtain a text vector, and calculating the vector distance of the text vector; The parameter text is classified according to the vector distance to obtain the target parameter.

3. The calibration method according to claim 1, wherein: The step of constructing a panoramic image of the device installation location according to the device environment includes: Performing attribute analysis on the device environment to obtain environment attributes, and rendering a three-dimensional panoramic environment corresponding to the device environment according to the environment attributes; Constructing a view matrix corresponding to each view of the three-dimensional panoramic environment, and performing weighted summation on the view matrices to obtain a target matrix; Combining the target matrix, performing a two-dimensional conversion on the three-dimensional panoramic environment to obtain a two-dimensional panoramic image; Color buffering is performed on the two-dimensional panoramic image to obtain a panoramic image of the device installation location.

4. The calibration method according to claim 1, wherein: The step of extracting features from the panoramic image to obtain a feature image includes: Performing entity marking on the panoramic image to obtain an image entity, and identifying image texture in the image entity; Calculating the complexity of each texture in the image texture to obtain texture complexity; When the texture complexity is greater than a preset threshold, screening the image entity to obtain a target image entity; Calculating the grayscale value of each pixel in the target image entity to obtain the entity grayscale value; Performing feature screening on the target image entity according to the entity grayscale value to obtain a feature image entity; According to the feature image entity, feature extraction is performed on the panoramic image to obtain a feature image.

5. The calibration method according to claim 4, wherein: Calculating the complexity of each texture in the image texture to obtain texture complexity includes: The complexity of each texture in the image texture can be calculated by the following formula: Among them, B represents the complexity of each texture in the image texture, d*d represents the value range of the image texture, a and b represent the texture positions of the image texture in the d*d range, and D(a, b) represents the mean square error corresponding to the image texture at the (a, b) position.

6. The calibration method according to claim 1, wherein: The performing image synthesis on the captured images to obtain a synthesized image includes: Marking edges of the captured image to obtain an edge image, and measuring the number of pixels in the edge image; Constructing a pixel matrix of the edge image according to the number of pixels, and calculating the matrix variance of each matrix in the pixel matrix; performing equalization processing on the edge image according to the matrix variance to obtain a balanced edge image; The image consistency of the equalized edge image is calculated, and the captured images are synthesized according to the image consistency to obtain a synthesized image.

7. The calibration method according to claim 6, wherein: The equalizing processing of the edge image to obtain the equalized edge image includes: Where G represents the balanced edge image, μ represents the interference coefficient of the edge image, j represents the starting pixel of the edge image, X represents the total pixel value of the pixel points of the edge image, and H j Represents the number of pixels corresponding to the pixel value j, K represents the variance coefficient of the matrix variance, and ω represents the mapping value corresponding to the variable item in the edge image.

8. The calibration method according to claim 1, wherein: The calculating the position deviation coefficient of the camera device by combining the feature image and the composite image includes: Constructing coordinate systems corresponding to the feature image and the composite image respectively to obtain a first coordinate system and a second coordinate system; Overlapping the first coordinate system and the second coordinate system to obtain an overlapped coordinate system; Calculating the angular deviation coefficient of the same coordinate axis in the coincident coordinate system; A position deviation coefficient of the camera device is obtained according to the angular deviation coefficient.

9. The calibration method according to claim 1, wherein: The calculating the degree of overlap between the panoramic image and the synthesized image includes: respectively locating image centers of the panoramic image and the composite image; Measuring the horizontal angles of the panoramic image and the composite image respectively to obtain a first horizontal angle and a second horizontal angle; If the first horizontal angle and the second horizontal angle are inconsistent, adjusting the angles of the panoramic image and the composite image until the first horizontal angle and the second horizontal angle are consistent; The degree of overlap between the panoramic image and the synthesized image is calculated based on the image center.

10. A device for calibrating camera installation deviations used for regional inspections, characterized in that: The device comprises: A parameter extraction module is used to obtain the camera device to be calibrated, extract parameters of the camera device, and obtain target parameters; a feature extraction module, configured to determine the device installation location of the camera device within the area, perform environmental acquisition on the device installation location to obtain a device environment, construct a panoramic image of the device installation location based on the device environment, and perform feature extraction on the panoramic image to obtain a feature image; a position deviation calculation module, configured to use the camera to capture an image of the device installation position to obtain a captured image, synthesize the captured image to obtain a synthesized image, and calculate a position deviation coefficient of the camera by combining the feature image and the synthesized image; The device calibration module is used to calculate the overlap between the panoramic image and the composite image, determine the angular deviation coefficient of the camera device based on the overlap and the target parameter, and calibrate the installation position of the camera device according to the position deviation coefficient and the angular deviation coefficient.

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