Multi-spectral fusion security camera monitoring device and data processing method thereof

Abstract

The present invention discloses a multi-spectral fusion security camera monitoring device and a data processing method thereof, which belongs to the field of image processing and security monitoring technology. The device comprises a plurality of multi-spectral cameras, and also comprises obtaining position data of common features of a plurality of spectral images, and generating a common feature position evaluation index; correcting the spectral image according to the common feature position evaluation index; establishing an image fusion model, and fusing the corrected spectral image according to the image fusion model; obtaining environmental data corresponding to the spectral image, and generating an environmental state index; establishing a dynamic adjustment model according to the environmental state index, generating model parameter adjustment values, and dynamically adjusting the model parameters of the image fusion model; the present invention can not only improve the fusion accuracy under different environmental conditions and enhance the target recognition capability of the security camera in complex scenes, but also reduce the cost of manual intervention through fully automated processing, and provide an efficient and reliable solution for multi-spectral fusion monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing and security monitoring, and in particular relates to a multi-spectral fusion security camera monitoring device and a data processing method thereof. Background Art

[0002] With the increasing demand for security monitoring, multispectral fusion technology has demonstrated significant advantages in target recognition and monitoring in complex environments. However, multispectral images often suffer from positional offsets and feature inconsistencies due to imaging differences between sensors in different wavelength bands. Environmental factors (such as lighting and haze) can easily degrade the information in single-spectral images, affecting the fusion effect.

[0003] In existing technologies, multispectral image fusion mainly relies on fixed weight distribution models or manual intervention adjustments, such as using weighted averaging to fuse visible light and infrared images, or manually calibrating and aligning feature points.

[0004] However, traditional technologies have significant shortcomings: first, there is a lack of an automated correction mechanism for the positional deviation of multispectral images, resulting in blurred image details or target misalignment after fusion; second, the fusion weight distribution fails to dynamically adjust in combination with real-time environmental conditions, and information loss is prone to occur under sudden changes in lighting or inclement weather; third, existing systems lack the ability to collaboratively process multi-source data, making it difficult to meet the high-precision monitoring needs of complex security scenarios. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a multi-spectral fusion security camera monitoring device and a data processing method thereof, which solves the above problems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-spectral fusion security camera monitoring device, the device comprising:

[0007] Multiple multispectral cameras are used to collect image information in different spectral bands, such as visible light, near infrared, short-wave infrared, etc., to obtain the characteristics of the target under different spectra;

[0008] A feature data acquisition module is used to obtain the position data of the common features of multiple spectral images; wherein the position data refers to the position coordinates of the common features in the spectral images; the common features are the different forms of a common object in multiple spectral images displayed in different spectral images;

[0009] a feature position analysis unit, configured to generate a common feature position evaluation index based on the position coordinates of the common features in the spectral image;

[0010] A spectral image correction module is used to correct the spectral image according to the common feature position evaluation index;

[0011] Image fusion model building module, used to build image fusion model;

[0012] An environmental analysis unit, configured to obtain environmental data corresponding to the spectral image and generate an environmental status index; wherein the environmental data corresponding to the spectral image refers to the environmental data required for the environment when the spectral image is generated;

[0013] Model parameter analysis module, used to establish a dynamic adjustment model based on the environmental state index and generate model parameter adjustment values;

[0014] A model parameter adjustment module, used for dynamically adjusting the model parameters of the image fusion model according to the model parameter adjustment value;

[0015] The image fusion module is used to perform image fusion processing on the corrected spectral image using an image fusion model and output a spectral fusion image.

[0016] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0017] A data processing method for a multi-spectral fusion security camera monitoring device is applied to the multi-spectral fusion security camera monitoring device described above, comprising the following steps:

[0018] Obtaining position data of a common feature of the multiple spectral images; wherein the position data refers to the position coordinates of the common feature in the spectral image; the common feature is the different forms of a common object in the multiple spectral images displayed in different spectral images;

[0019] Generate a common feature position evaluation index according to the position coordinates of the common features in the spectral image;

[0020] The spectral image is corrected according to the common feature position evaluation index;

[0021] Establish an image fusion model;

[0022] Obtaining environmental data corresponding to the spectral image and generating an environmental status index; wherein the environmental data corresponding to the spectral image refers to the environmental data required for the environment when the spectral image is generated;

[0023] Establish a dynamic adjustment model based on the environmental state index and generate model parameter adjustment values;

[0024] Dynamically adjust the model parameters of the image fusion model according to the model parameter adjustment value;

[0025] The image fusion model is used to perform image fusion processing on the corrected spectral image and output a spectral fusion image.

[0026] Further technical solution: The method for acquiring the position data of the common features of the plurality of spectral images is specifically as follows:

[0027] The fixed features within the selected monitoring area are the common features of multiple spectral images;

[0028] Get the position coordinates (X i , Y i ); where i is a different spectral image.

[0029] Further technical solution: The method for generating the common feature position evaluation index specifically includes the following steps:

[0030] Generate common feature coordinate deviation values ​​according to the position coordinates of the common features in the spectral image;

[0031] The common feature coordinate deviation value is used to generate the common feature position evaluation index.

[0032] Further technical solution: The common feature coordinate deviation value is generated in the following manner:

[0033] By formula:

[0034] ;

[0035] Generate common feature coordinate deviation values ;

[0036] In the formula, It represents the X-axis coordinate value of the position coordinate of a single common feature in the spectral image. It represents the X-axis coordinate value of the position coordinate of other single common features in the spectral image. It represents the Y-axis coordinate value of the position coordinate of a single common feature in the spectral image. It represents the Y-axis coordinate value of the position coordinate of other single common features in the spectral image.

[0037] Further technical solution: The common feature position evaluation index is generated in the following manner:

[0038] Performing difference processing on the common feature coordinate deviation value and the common feature coordinate deviation threshold to generate a coordinate deviation evaluation value;

[0039] The coordinate deviation evaluation value is compared with the common feature coordinate deviation threshold to generate a common feature position evaluation index.

[0040] Further technical solution: The method of correcting the spectral image is specifically as follows:

[0041] According to the common feature position evaluation index, determine whether the spectral image needs to be corrected;

[0042] The specific judgment method is:

[0043] comparing the common feature position evaluation index with a common feature position evaluation index threshold;

[0044] If the common feature position evaluation index is greater than the common feature position evaluation index threshold, it is determined that the spectral image needs to be adjusted;

[0045] When the determination result is that the spectral image needs to be corrected, the spectral image is corrected;

[0046] The specific correction method of the spectral image is as follows:

[0047] A single spectral image is set as a reference spectral image, and other spectral images are adjusted according to the reference spectral image.

[0048] Further technical solution: The expression of the image fusion model is:

[0049] ;

[0050] In the expression, F represents the fused spectral image. 、 、 All are corrected spectral images. 、 、 are model parameters, and + +....+ =1.

[0051] Further technical solution: The method for generating the environmental status index specifically includes the following steps:

[0052] Obtain environmental data on the environment requirements for generating spectral images;

[0053] Generate an environmental status index based on the environmental data required for the environment when generating the spectral image;

[0054] The environmental status index is generated in the following manner:

[0055] By formula:

[0056] ;

[0057] Generate environmental state index Q;

[0058] In the formula, 、 、 All are current environmental data. 、 、 All of them are corresponding standard environmental data. 、 、 are weight coefficients, and + + =1.

[0059] Further technical solution: The expression of the dynamic adjustment model is:

[0060] ;

[0061] In the expression, It represents the adjustment value of the model parameters. is the model parameter of the current image fusion model, and Q represents the environmental state index.

[0062] The present invention provides a multi-spectral fusion security camera monitoring device and a data processing method thereof, which has the following advantages compared with the prior art:

[0063] The present invention realizes dynamic correction and alignment between spectral images by automatically acquiring the position data of common features of multispectral images and generating an evaluation index, effectively solving the problem of poor consistency of multispectral images and effectively avoiding the problems of blurred image details or target misalignment after fusion; at the same time, a dynamic adjustment model is established in combination with the environmental state index to optimize the image fusion weight in real time to avoid information loss, significantly improving the fusion accuracy under different environmental conditions and the ability to collaboratively process multi-source data, enhancing the target recognition ability of security cameras in complex scenarios, and reducing the cost of manual intervention through fully automated processing, providing an efficient and reliable solution for multispectral fusion monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flowchart of a data processing method for a multi-spectral fusion security camera monitoring device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. 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.

[0066] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0067] The present invention provides a multi-spectral fusion security camera monitoring device, which includes:

[0068] Multiple multispectral cameras are used to collect image information in different spectral bands, such as visible light, near infrared, short-wave infrared, etc., to obtain the characteristics of the target under different spectra;

[0069] A feature data acquisition module is used to obtain the position data of the common features of multiple spectral images; wherein the position data refers to the position coordinates of the common features in the spectral images; the common features are the different forms of a common object in multiple spectral images displayed in different spectral images;

[0070] a feature position analysis unit, configured to generate a common feature position evaluation index based on the position coordinates of the common features in the spectral image;

[0071] A spectral image correction module is used to correct the spectral image according to the common feature position evaluation index;

[0072] Image fusion model building module, used to build image fusion model;

[0073] An environmental analysis unit, configured to obtain environmental data corresponding to the spectral image and generate an environmental status index; wherein the environmental data corresponding to the spectral image refers to the environmental data required for the environment when the spectral image is generated;

[0074] Model parameter analysis module, used to establish a dynamic adjustment model based on the environmental state index and generate model parameter adjustment values;

[0075] A model parameter adjustment module, used for dynamically adjusting the model parameters of the image fusion model according to the model parameter adjustment value;

[0076] The image fusion module is used to perform image fusion processing on the corrected spectral image using an image fusion model and output a spectral fusion image.

[0077] See also Figure 1 , a data processing method for a multi-spectral fusion security camera monitoring device provided by an embodiment of the present invention, the method is applied to the above-mentioned multi-spectral fusion security camera monitoring device, comprising the following steps:

[0078] Step S10: Acquire position data of a common feature of a plurality of spectral images; wherein the position data refers to the position coordinates of the common feature in the spectral image; the common feature is the different forms of a common object in the plurality of spectral images displayed in different spectral images;

[0079] Step S20: generating a common feature position evaluation index according to the position coordinates of the common feature in the spectral image;

[0080] Step S30: Correcting the spectral image according to the common feature position evaluation index;

[0081] Step S40: establishing an image fusion model;

[0082] Step S50: Obtaining environmental data corresponding to the spectral image and generating an environmental status index; wherein the environmental data corresponding to the spectral image refers to the environmental data required for the environment when the spectral image is generated;

[0083] Step S60: establishing a dynamic adjustment model according to the environmental state index and generating model parameter adjustment values;

[0084] Step S70: dynamically adjusting the model parameters of the image fusion model according to the model parameter adjustment value;

[0085] Step S80: performing image fusion processing on the corrected spectral image using an image fusion model, and outputting a spectral fusion image.

[0086] As a preferred embodiment of the present invention, step S10 specifically includes:

[0087] The fixed features within the selected monitoring area are the common features of multiple spectral images;

[0088] Get the position coordinates (X i , Y i ); where i is a different spectral image; for example, X i Refers to the X-axis coordinate value of the position coordinate of the common feature of multiple spectral images in the spectral image;

[0089] In this embodiment, the multiple spectral images include but are not limited to infrared images, low-light images, thermal imaging images, etc.;

[0090] In addition, the coordinates are generated by establishing a two-dimensional coordinate system on the spectral image and generating the position coordinates of the common features in the spectral image according to the positions of the common features on the two-dimensional coordinate system.

[0091] As a preferred embodiment of the present invention, step S20 specifically includes the following steps:

[0092] S21: generating a common feature coordinate deviation value according to the position coordinates of the common feature in the spectral image;

[0093] S22: Generate a common feature position evaluation index based on the common feature coordinate deviation value.

[0094] As a preferred embodiment of the present invention, the common feature coordinate deviation value is generated in the following manner:

[0095] By formula:

[0096] ;

[0097] Generate common feature coordinate deviation values ;

[0098] In the formula, It represents the X-axis coordinate value of the position coordinate of a single common feature in the spectral image. It represents the X-axis coordinate value of the position coordinate of other single common features in the spectral image. It represents the Y-axis coordinate value of the position coordinate of a single common feature in the spectral image. It represents the Y-axis coordinate value of the position coordinate of other single common features in the spectral image;

[0099] It should be explained that the position coordinate of a single common feature in the spectral image refers to one of the position coordinates of multiple common features in the spectral image, and 、 is the position coordinate of the same common feature in the spectral image; the position coordinates of other single common features in the spectral image refer to 、 The position coordinates of a certain common feature in the spectral image other than the position coordinates of the common feature represented by 、 are the position coordinates of the same common feature in the spectral image.

[0100] As a preferred embodiment of the present invention, the common feature position evaluation index is generated in the following manner:

[0101] Performing difference processing on the common feature coordinate deviation value and the common feature coordinate deviation threshold to generate a coordinate deviation evaluation value;

[0102] The coordinate deviation evaluation value is compared with the common feature coordinate deviation threshold to generate a common feature position evaluation index;

[0103] It should be explained that if the coordinate deviation evaluation value is less than the common feature coordinate deviation threshold, it means that the deviation between the position coordinates of the common features of the current two spectral images in the spectral image is low, which has little impact on the quality of the image fusion and does not require spectral image correction.

[0104] As a preferred embodiment of the present invention, step S30 specifically includes:

[0105] According to the common feature position evaluation index, determine whether the spectral image needs to be corrected;

[0106] The specific judgment method is:

[0107] comparing the common feature position evaluation index with a common feature position evaluation index threshold;

[0108] If the common feature position evaluation index is less than or equal to the common feature position evaluation index threshold, it is determined that the spectral image does not need to be adjusted; when the common feature position evaluation index is smaller, the need for spectral image adjustment is smaller;

[0109] If the common feature position evaluation index is greater than the common feature position evaluation index threshold, it is determined that the spectral image needs to be adjusted; when the common feature position evaluation index is larger, the need for spectral image adjustment is greater;

[0110] When the determination result is that the spectral image needs to be corrected, the spectral image is corrected;

[0111] The specific correction method of the spectral image is as follows:

[0112] Setting a single spectral image as a reference spectral image, and adjusting other spectral images according to the reference spectral image;

[0113] In this embodiment, the adjustment methods include cropping, image re-acquisition, etc.; for example, based on the deviation between the other spectral images and the reference spectral image, the acquisition angle of the other spectral images is adjusted to obtain a new spectral image; wherein the reference spectral image can be any image. Specifically, image fusion generally involves fusion processing of images in the same area. First, the reference coordinate position (e.g., directly above, lower right corner, plane coordinate position, etc.) of a fixed feature (e.g., a stone) in a certain position in the area is determined in the reference image, and then the position coordinates of the feature in the other images are adjusted based on the reference coordinate position. The method of adjusting the spectral image acquisition angle is a conventional technical means used by those skilled in the art and will not be elaborated here.

[0114] The method for obtaining the adjustment value of the acquisition angle can be specifically determined based on the azimuth angle of the straight line formed between the position coordinates of the common features in the spectrum image;

[0115] In addition, the spectral image acquisition device can be accurately adjusted through the azimuth angle of the straight line formed between the position coordinates of the common features in the spectral image and the common feature coordinate deviation value.

[0116] As a preferred embodiment of the present invention, the expression of the image fusion model is:

[0117] ;

[0118] In the expression, F represents the fused spectral image. 、 、 All are corrected spectral images. 、 、 are model parameters, and + +....+ =1;

[0119] It needs to be explained that 、 、 are different spectral images, where The i in refers to the number of types of spectral images;

[0120] also, 、 、 is with 、 、 The corresponding model parameters.

[0121] As a preferred embodiment of the present invention, step 50 specifically includes the following steps:

[0122] S51: Acquire environmental data required for the environment when generating the spectral image;

[0123] It should be noted that different spectral images require different environmental data when generating them. For example, visible light spectral images rely on ambient lighting, and their information is severely degraded in low light and haze. Therefore, environmental data includes light intensity and air dust content.

[0124] S52: Generate an environmental status index according to the environmental data required for the environment when the spectral image is generated.

[0125] As a preferred embodiment of the present invention, the environmental status index is generated in the following manner:

[0126] By formula:

[0127] ;

[0128] Generate environmental state index Q;

[0129] In the formula, 、 、 All are current environmental data. 、 、 All of them are corresponding standard environmental data. 、 、 are weight coefficients, and + + =1;

[0130] It should be explained that the corresponding standard environmental data refers to the standard value of the environmental data corresponding to the current environmental data; for example, the spectral image of visible light includes the light intensity in the environmental requirements, and the current environmental data is the light intensity, that is, , then the corresponding standard environmental data is the light intensity under standard working conditions, that is , the standard working condition refers to the light intensity requirement when generating the visible light spectrum image (for example, if the light intensity is too low, that is, when the environment is dim, the recognition of the generated visible light spectrum image will be too low);

[0131] also, 、 、 It is a set value and is set by relevant personnel in this field.

[0132] As a preferred embodiment of the present invention, the expression of the dynamic adjustment model is:

[0133] ;

[0134] In the expression, It represents the adjustment value of the model parameters. is the model parameter of the current image fusion model, and Q represents the environmental state index;

[0135] It needs to be explained that is the model parameter in the image fusion model (i.e. 、 、 one of the).

[0136] As a preferred embodiment of the present invention, the method of dynamically adjusting the model parameters of the image fusion model is specifically as follows:

[0137] According to the model parameter adjustment value, the model parameters of the corresponding current image fusion model are adjusted;

[0138] During the adjustment, other model parameters of the image fusion model can be adaptively adjusted according to the values ​​of the model parameters of the adjusted image fusion model to ensure that the sum of the model parameters of the image fusion model remains unchanged, that is, + +....+ =1.

[0139] As a preferred embodiment of the present invention, the feature position analysis unit specifically includes:

[0140] A coordinate deviation analysis module generates common feature coordinate deviation values ​​according to the position coordinates of the common features in the spectral image;

[0141] The common feature position evaluation module is used to generate a common feature position evaluation index based on the common feature coordinate deviation value.

[0142] As a preferred embodiment of the present invention, the environment analysis unit specifically includes:

[0143] An environmental data acquisition module is used to obtain environmental data required for the environment when generating spectral images;

[0144] The environmental analysis module is used to generate an environmental status index based on the environmental data required for the environment when generating the spectral image.

[0145] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Multi-spectral fusion security camera monitoring device, characterized by: The device includes: Multiple multispectral cameras are used to collect image information in different spectral bands, including visible light image information, near-infrared image information, and short-wave infrared image information, to obtain the characteristics of the target under different spectra; A feature data acquisition module is used to obtain position data of common features of multiple spectral images; the position data is the position coordinates of the common features in the spectral images; the common features are characteristic information of different forms displayed by a common object in multiple spectral images in different spectral images; a feature position analysis unit, configured to generate a common feature position evaluation index based on the position coordinates of the common features in the spectral image; A spectral image correction module is used to correct the spectral image according to the common feature position evaluation index; Image fusion model building module, used to build image fusion model; An environmental analysis unit is used to obtain environmental data corresponding to the spectral image and generate an environmental status index; the environmental data corresponding to the spectral image is the environmental data required for the environment when the spectral image is generated; Model parameter analysis module, used to establish a dynamic adjustment model based on the environmental state index and generate model parameter adjustment values; A model parameter adjustment module, used for dynamically adjusting the model parameters of the image fusion model according to the model parameter adjustment value; An image fusion module is used to perform image fusion processing on the corrected spectral image using an image fusion model and output a spectral fusion image; Specifically, the feature position analysis unit includes: A coordinate deviation analysis module generates common feature coordinate deviation values ​​according to the position coordinates of the common features in the spectral image; A common feature position evaluation module is used to generate a common feature position evaluation index based on the common feature coordinate deviation value; The common feature position evaluation index is generated by performing difference processing on the common feature coordinate deviation value and the common feature coordinate deviation threshold value to generate a coordinate deviation evaluation value; performing ratio processing on the coordinate deviation evaluation value and the common feature coordinate deviation threshold value to generate a common feature position evaluation index; Specifically, the environment analysis unit includes: An environmental data acquisition module is used to obtain environmental data required for the environment when generating spectral images; An environmental analysis module is used to generate an environmental state index Q based on the environmental data required for the environment when generating the spectral image; The environmental state index Q is generated as follows: ； in, 、 、 All are current environmental data. 、 、 All of them are corresponding standard environmental data. 、 、 are weight coefficients, and + + =1.

2. A data processing method for a multi-spectral fusion security camera monitoring device, characterized in that: The method is applied to the multi-spectral fusion security camera monitoring device according to claim 1, comprising the following steps: Acquire location data of a common feature of the plurality of spectral images; the location data is the location coordinates of the common feature in the spectral image; the common feature is characteristic information of different forms displayed in different spectral images of a common object in the plurality of spectral images; Generate a common feature position evaluation index according to the position coordinates of the common features in the spectral image; The spectral image is corrected according to the common feature position evaluation index; Establish an image fusion model; Acquire environmental data corresponding to the spectral image and generate an environmental status index; the environmental data corresponding to the spectral image is the environmental data required for the environment when the spectral image is generated; Establish a dynamic adjustment model based on the environmental state index and generate model parameter adjustment values; Dynamically adjust the model parameters of the image fusion model according to the model parameter adjustment value; The image fusion model is used to perform image fusion processing on the corrected spectral image and output a spectral fusion image; The common feature position evaluation index is generated in the following manner: Generate common feature coordinate deviation values ​​according to the position coordinates of the common features in the spectral image; The common feature coordinate deviation value is used to generate a common feature position evaluation index; specifically, the common feature coordinate deviation value is subjected to difference processing with the common feature coordinate deviation threshold value to generate a coordinate deviation evaluation value; the coordinate deviation evaluation value is subjected to ratio processing with the common feature coordinate deviation threshold value to generate a common feature position evaluation index; The environmental status index is generated in the following manner: Obtain environmental data on the environment requirements for generating spectral images; According to the environmental data required for the environment when generating the spectral image, the environmental state index Q is generated: The environmental state index Q is generated as follows: ； in, 、 、 All are current environmental data. 、 、 All of them are corresponding standard environmental data. 、 、 are weight coefficients, and + + =1.

3. The data processing method of the multi-spectral fusion security camera monitoring device according to claim 2 is characterized in that: The method for acquiring the position data of the common features of the multiple spectral images is: The fixed features within the selected monitoring area are the common features of multiple spectral images; Get the position coordinates (X i , Y i ); where i is a different spectral image.

4. The data processing method of the multi-spectral fusion security camera monitoring device according to claim 2 is characterized in that: The calculation formula of the common characteristic coordinate deviation value is: ； in, It represents the X-axis coordinate value of the position coordinate of a single common feature in the spectral image. It represents the X-axis coordinate value of the position coordinate of other single common features in the spectral image. It represents the Y-axis coordinate value of the position coordinate of a single common feature in the spectral image. It represents the Y-axis coordinate value of the position coordinate of other single common features in the spectral image.

5. The data processing method of the multi-spectral fusion security camera monitoring device according to claim 2 is characterized in that: The method for correcting the spectral image is as follows: According to the common feature position evaluation index, it is judged whether the spectral image needs to be corrected. The judgment method is as follows: comparing the common feature position evaluation index with a common feature position evaluation index threshold; If the common feature position evaluation index is greater than the common feature position evaluation index threshold, it is determined that the spectral image needs to be adjusted; When the determination result is that the spectral image needs to be corrected, the spectral image is corrected; The correction method of the spectral image is: A single spectral image is set as a reference spectral image, and other spectral images are adjusted according to the reference spectral image.

6. The data processing method of the multi-spectral fusion security camera monitoring device according to claim 2, characterized in that: The expression of the image fusion model is: ； Among them, F represents the fused spectral image, 、 、 All are corrected spectral images. 、 、 are model parameters, and + +....+ =1.

7. The data processing method of the multi-spectral fusion security camera monitoring device according to claim 2, characterized in that: The calculation expression for dynamically adjusting the model parameters of the image fusion model is: ； in, It represents the adjustment value of the model parameters. is the model parameter of the current image fusion model, and Q represents the environmental state index.

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