Operating lamp brightness energy-saving analysis system and method based on big data processing

Abstract

The invention discloses an operating lamp brightness energy-saving analysis system and method based on big data processing, and belongs to the field of scene recognition. According to the operating lamp brightness energy-saving analysis system and method based on big data processing, accurate scene analysis is carried out on required operating lamp brightness by comprehensively evaluating the definition of an operating position and the distinguishing condition of the operating position and a non-operating position; and meanwhile, the appropriate brightness of the operating lamp is comprehensively analyzed by integrating the scene condition of the operating position and the abnormal light reflection of the operating instrument in the operating process, so that the suitability of the brightness of the operating lamp is further improved.

Description

Technical Field

[0001] This application belongs to the field of scene recognition, specifically a surgical lamp brightness energy-saving analysis system and method based on big data processing. Background Art

[0002] The brightness of a surgical lamp is a crucial factor during a surgical procedure. The main purpose of a surgical lamp is to provide sufficient and uniform illumination for the surgery, enabling the doctor to clearly see the surgical site and ensuring the safety and accuracy of the surgery. The brightness of a surgical lamp is usually measured in lux. Generally speaking, the illuminance of a surgical lamp should be between 5000 and 10000 lux to ensure clear vision during the surgery and reduce eye fatigue and discomfort. Within this range, the doctor can make more accurate judgments and operations, reducing errors caused by insufficient light;

[0003] When controlling the brightness of a surgical lamp in the prior art, it is usually simply to make the surgical image clear, without considering the distinction between the surgical position and the non-surgical position, and the abnormal reflection of surgical instruments during the surgical process, resulting in a mismatch between the brightness during the surgical process and the required brightness, thus affecting surgical safety. Most of the prior art has the above problems;

[0004] To solve the problems raised in this background art, this application designs a surgical lamp brightness energy-saving analysis system and method based on big data processing. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, this application proposes a surgical lamp brightness energy-saving analysis system and method based on big data processing. This application accurately analyzes the required surgical lamp brightness through comprehensive evaluation of the clarity of the surgical position and the distinction between the surgical position and the non-surgical position, improving the adaptability of the surgical lamp brightness. At the same time, by comprehensively analyzing the scene situation of the surgical position and the abnormal reflection of surgical instruments during the surgical process, the adaptability of the surgical lamp brightness is further improved.

[0006] To achieve the above object, this application provides the following technical solutions: In the first aspect, this application provides a surgical lamp brightness energy-saving analysis method based on big data processing, which includes the following specific steps:

[0007] S1. Real-time obtain the image data of the surgical site, and at the same time obtain the information of the surgical instruments to be used;

[0008] S2. Import the obtained image data of the surgical site into an image brightness analysis model for surgical image brightness analysis;

[0009] S3. Based on the information of the surgical instruments to be used, import them into an image reflection analysis model for surgical instrument reflection abnormality analysis;

[0010] S4. Perform the required surgical lamp brightness analysis on the obtained surgical image brightness analysis results and the surgical instrument reflection anomaly analysis results.

[0011] S5. The control component adjusts the surgical lamp to the analyzed surgical lamp brightness.

[0012] As a preferred technical solution of the surgical lamp brightness energy-saving analysis method based on big data processing, the specific content of obtaining the image data of the surgical site in real time and simultaneously obtaining the information of the surgical instruments to be used is as follows:

[0013] S11. Collect the image of the patient's surgical site under the room light environment of the operating room through the image acquisition terminal and store it in the storage component.

[0014] S12. Obtain the surgical instrument data during the historical process of performing this type of surgery, obtain the reflection area data, material reflectivity data, and usage frequency data of the surgical instruments, and store them in the storage component.

[0015] As a preferred technical solution of the surgical lamp brightness energy-saving analysis method based on big data processing, the importing the obtained image data of the surgical site into the image brightness analysis model for surgical image brightness analysis includes the following specific steps:

[0016] S21. Obtain the image data of the corresponding surgical site, and import the image data of the surgical site into the image sharpness analysis strategy for image sharpness analysis.

[0017] Among them, the image sharpness analysis strategy includes the following specific steps:

[0018] S211. Obtain the gray values of each pixel point of the collected image, substitute them into the average value calculation formula to calculate the gray average value of the collected image. Among them, the average value calculation formula is: Among them, N is the number of pixel points in the vertical direction of the image, M is the number of pixel points in the horizontal direction of the image, and f(i, j) is the gray value of the pixel point with the abscissa i and the ordinate j on the image.

[0019] S212. Obtain the gray average value and the gray values of each pixel point of the collected image, substitute them into the image clarity coefficient calculation formula to calculate the image clarity coefficient. Among them, the image clarity coefficient calculation formula is:

[0020]

[0021] S22. Obtain the image data of the corresponding surgical site, and import the image data of the surgical site into the surgical position discrimination analysis strategy for surgical position discrimination analysis.

[0022] Among them, the specific steps of the surgical position discrimination analysis strategy are as follows:

[0023] Obtain the surgical center position and the surgical position, expand outward by twice the distance from each point on the boundary of the surgical position to the surgical center position to obtain an image with the surgical position enlarged by twice, remove the surgical position from the obtained image to get the non-surgical position, and at the same time obtain the boundary regions of the surgical position and the non-surgical position that need to be processed. Import the calculated clarity coefficients of the surgical position and the non-surgical position into the surgical position discrimination calculation formula to calculate the surgical position discrimination. Among them, the surgical position discrimination calculation formula is: Among them, sw is the clarity coefficient of the surgical position, and sz is the clarity coefficient of the non-surgical position. In this way, the clarity coefficients of the surgical position and the non-surgical position are comprehensively analyzed respectively to obtain the discrimination situation between the surgical position and the non-surgical position;

[0024] S23. Import the obtained image clarity analysis result and surgical position discrimination analysis result into the brightness analysis strategy for brightness analysis;

[0025] Among them, the brightness analysis strategy includes the following specific contents:

[0026] Obtain the image clarity coefficient and surgical position discrimination of the surgical position, and import them into the brightness adjustment analysis value calculation formula to calculate the brightness adjustment analysis value. Among them, the brightness adjustment analysis value calculation formula is: Among them, exp() is the power of the natural constant e, a1 is the clarity ratio coefficient, a2 is the discrimination ratio coefficient, swm is the clarity coefficient threshold, and Qm is the discrimination threshold.

[0027] As a preferred technical solution of the surgical lamp brightness energy-saving analysis method based on big data processing, the surgical instrument reflection anomaly analysis by importing the information of the surgical instruments to be used into the image reflection analysis model includes the following specific steps:

[0028] S31. Obtain the reflection area data, material reflectivity data, and usage frequency data of the surgical instruments;

[0029] S32. Import the obtained reflection area data, material reflectivity data, and usage frequency data of the surgical instruments into the instrument reflection anomaly value calculation formula to calculate the instrument reflection anomaly value. Among them, the instrument reflection anomaly value calculation formula is: Among them, b is the material ratio coefficient, Sx is the reflection area data of the surgical instrument, Mx is the material reflectivity of the surgical instrument, Sm is the average value of the reflection areas of all surgical instruments, Mm is the average value of the material reflectivities of the surgical instruments, Xc is the average displacement when the surgical instrument is used for the cth time, Xm is the average distance of all instrument uses, and R is the average number of uses of the surgical instrument;

[0030] S33. Obtain the instrument reflection anomaly values of all surgical instruments required for the corresponding surgery, and add up the instrument reflection anomaly values of all surgical instruments to obtain the overall reflection anomaly value.

[0031] As a preferred technical solution of the surgical lamp brightness energy-saving analysis method based on big data processing, the surgical lamp brightness analysis that needs to be performed on the obtained surgical image brightness analysis result and surgical instrument reflection anomaly analysis result includes the following specific contents:

[0032] Substitute the obtained surgical standard brightness value, brightness adjustment analysis value, and overall reflection anomaly value into the surgical lamp brightness calculation formula to calculate the required brightness of the surgical lamp. Among them, the surgical lamp brightness calculation formula is: Among them, Fm is the surgical standard brightness value, NF is the overall reflection anomaly value, and λ is the brightness adjustment analysis proportion coefficient.

[0033] In the second aspect, the present application provides a surgical lamp brightness energy-saving analysis system based on big data processing, which is implemented based on the above-mentioned surgical lamp brightness energy-saving analysis method based on big data processing, and specifically includes a data acquisition module, an image brightness analysis module, a reflection anomaly analysis module, a surgical lamp brightness analysis module, and a brightness adjustment module;

[0034] Among them, the data acquisition module is used to obtain the image data of the surgical site in real time and obtain the information of the surgical instruments that need to be used at the same time;

[0035] The image brightness analysis module is used to import the obtained image data of the surgical site into the image brightness analysis model for surgical image brightness analysis;

[0036] The reflection anomaly analysis module is used to import the information of the surgical instruments that need to be used into the image reflection analysis model for surgical instrument reflection anomaly analysis;

[0037] The surgical lamp brightness analysis module is used to perform the required surgical lamp brightness analysis on the obtained surgical image brightness analysis result and surgical instrument reflection anomaly analysis result;

[0038] The brightness adjustment module is used to control the component to adjust the surgical lamp to the analyzed surgical lamp brightness.

[0039] In the third aspect, the present application provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory;

[0040] The processor executes the above-mentioned surgical lamp brightness energy-saving analysis method based on big data processing by calling the computer program stored in the memory.

[0041] Fourthly, the present application provides a computer-readable storage medium storing instructions, which, when run on a computer, cause the computer to execute the method for analyzing the brightness energy saving of an operating lamp based on big data processing as described above.

[0042] Compared with the prior art, the beneficial effects of the present application are as follows:

[0043] The present application accurately analyzes the required brightness of the operating lamp through comprehensive evaluation of the clarity of the surgical position and the distinction between the surgical position and the non-surgical position, improving the adaptability of the brightness of the operating lamp;

[0044] The present application further improves the adaptability of the brightness of the operating lamp by comprehensively analyzing the appropriate brightness of the operating lamp based on the scene situation of the surgical position and the abnormal reflection of surgical instruments during the surgical process. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more apparent;

[0046] Figure 1 It is a schematic diagram of the overall process of the method for analyzing the brightness energy saving of an operating lamp based on big data processing of the present application;

[0047] Figure 2 It is a schematic diagram of step S2 of the method for analyzing the brightness energy saving of an operating lamp based on big data processing of the present application;

[0048] Figure 3 It is a schematic diagram of step S3 of the system for analyzing the brightness energy saving of an operating lamp based on big data processing of the present application;

[0049] Figure 4 It is a schematic diagram of the overall framework of the system for analyzing the brightness energy saving of an operating lamp based on big data processing of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present application and its application or use.

[0051] To solve the technical problems raised in the background art, the present application provides a preferred embodiment: as Figures 1-3 shown, the present embodiment provides a method for analyzing the brightness energy saving of an operating lamp based on big data processing, which includes the following specific steps:

[0052] S1. Obtain the image data of the surgical site in real time, and at the same time obtain the information of the surgical instruments to be used;

[0053] In one specific embodiment, the specific content of obtaining the image data of the surgical site in real time and obtaining the information of the surgical instruments to be used is as follows:

[0054] S11. Collect the image of the patient's surgical site under the room light environment of the operating room through an image acquisition terminal, and store it in the storage component;

[0055] S12. Obtain the surgical instrument data during the historical process of performing this type of surgery, obtain the reflected light area data, material reflectance data and usage frequency data of the surgical instruments, and store them in the storage component. Here, it should be noted that for the convenience of calculation, the reflected light area is set to half of the surface area of the instrument. The material reflectance is the efficiency of reflecting light, which is obtained through experiments. The usage frequency data is the number of times and the average moving distance of the surgical instrument used during the historical same type of surgery. Among them, surgical instruments are essential tools in surgical operations, with a wide variety of types and different uses. For example, knife and scissors types: including surgical knives, tissue scissors, suture scissors, etc., used for cutting and trimming tissues; clamp types: such as hemostatic forceps, tissue forceps, appendix forceps, etc., used for clamping, traction and fixing tissues; needle holders and sewing needles: used for suturing wounds and tissues; suction tips: used for sucking blood, body fluids and flushing fluids during surgery to keep the surgical field clear. In addition, there are some instruments specifically used for specific surgeries, such as laparoscopic instruments, hysteroscopic instruments, etc.;

[0056] S2. Import the obtained image data of the surgical site into the image brightness analysis model for surgical image brightness analysis;

[0057] In one specific embodiment, the specific content of obtaining the image data of the surgical site in real time and obtaining the information of the surgical instruments to be used is as follows:

[0058] S11. Collect the image of the patient's surgical site under the room light environment of the operating room through an image acquisition terminal, and store it in the storage component;

[0059] S12. Obtain the surgical instrument data during the historical process of performing this type of surgery, obtain the reflected light area data, material reflectance data and usage frequency data of the surgical instruments, and store them in the storage component.

[0060] As a preferred technical solution of the surgical lamp brightness energy-saving analysis method based on big data processing, importing the obtained image data of the surgical site into the image brightness analysis model for surgical image brightness analysis includes the following specific steps:

[0061] S21. Obtain the image data of the corresponding surgical site, and import the image data of the surgical site into the image sharpness analysis strategy for image sharpness analysis;

[0062] Among them, the image sharpness analysis strategy includes the following specific steps:

[0063] S211. Obtain the gray values of each pixel point of the acquired image, and substitute them into the average value calculation formula to calculate the gray average value of the acquired image. Among them, the average value calculation formula is: Among them, N is the number of pixel points in the vertical direction of the image, M is the number of pixel points in the horizontal direction of the image, and f(i, j) is the gray value of the pixel point with the abscissa i and the ordinate j on the image;

[0064] S212. Obtain the gray average value and the gray values of each pixel point of the acquired image, and substitute them into the image sharpness coefficient calculation formula to calculate the image sharpness coefficient. Among them, the image sharpness coefficient calculation formula is: In this formula, since the clearer the image, the more high-frequency components there are in the image. Taking the gray average value of all pixels of the image as a reference, the square sum of the differences of the gray values of each pixel point is calculated, and then it is normalized by the total number of pixels. It represents the average degree of gray change in the image. The greater the average degree of gray change, the clearer the image, and the smaller the average degree of gray change, the more blurred the image;

[0065] S22. Obtain the image data of the corresponding surgical site, and import the image data of the surgical site into the surgical position discrimination analysis strategy for surgical position discrimination analysis;

[0066] Among them, the specific steps of the surgical position discrimination analysis strategy are:

[0067] Obtain the surgical center position and the surgical position, expand outward by twice the distance from each point on the boundary of the surgical position to the surgical center position to obtain an image with the surgical position magnified twice. Remove the surgical position from the obtained image to get the non-surgical position. At the same time, obtain the boundary regions of the surgical position and the non-surgical position that need to be processed. Import the calculated sharpness coefficients of the surgical position and the non-surgical position into the surgical position discrimination calculation formula to calculate the surgical position discrimination. Among them, the surgical position discrimination calculation formula is: Among them, sw is the sharpness coefficient of the surgical position, and sz is the sharpness coefficient of the non-surgical position. In this way, the sharpness coefficients of the surgical position and the non-surgical position are comprehensively analyzed respectively to obtain the discrimination situation between the surgical position and the non-surgical position;

[0068] S23. Import the obtained image sharpness analysis result and surgical position discrimination analysis result into the brightness analysis strategy for brightness analysis;

[0069] Among them, the brightness analysis strategy includes the following specific contents:

[0070] Obtain the image clarity coefficient and surgical position discrimination degree of the surgical position, and import them into the brightness adjustment analysis value calculation formula to calculate the brightness adjustment analysis value. Among them, the brightness adjustment analysis value calculation formula is: Among them, exp() is the exponential power of the natural constant e, a1 is the clarity ratio coefficient, a2 is the discrimination ratio coefficient, swm is the clarity coefficient threshold, and Qm is the discrimination degree threshold;

[0071] S3. Import the information of the surgical instruments to be used into the image reflection analysis model to perform analysis on abnormal reflection of surgical instruments;

[0072] In one specific embodiment, importing the information of the surgical instruments to be used into the image reflection analysis model to perform analysis on abnormal reflection of surgical instruments includes the following specific steps:

[0073] S31. Obtain the reflection area data, material reflection rate data, and usage frequency data of the surgical instruments;

[0074] S32. Import the obtained reflection area data, material reflection rate data, and usage frequency data of the surgical instruments into the instrument reflection abnormal value calculation formula to calculate the instrument reflection abnormal value. Among them, the instrument reflection abnormal value calculation formula is: Among them, b is the material ratio coefficient, Sx is the reflection area data of the surgical instrument, Mx is the material reflection rate of the surgical instrument, Sm is the average value of the reflection areas of all surgical instruments, Mm is the average value of the material reflection rates of the surgical instruments, Xc is the average displacement of the c-th use of the surgical instrument, Xm is the average distance of all instrument uses, and R is the average number of uses of the surgical instrument;

[0075] S33. Obtain the instrument reflection abnormal values of all surgical instruments to be used in the corresponding surgery, and add up the instrument reflection abnormal values of all surgical instruments to obtain the overall reflection abnormal value;

[0076] It should be noted here that by comprehensively analyzing the scene situation of the surgical position and the abnormal reflection of surgical instruments during the surgery, the appropriate surgical lamp brightness is further improved, and the adaptability of the surgical lamp brightness is further enhanced;

[0077] S4. Perform the required surgical lamp brightness analysis on the obtained surgical image brightness analysis result and surgical instrument reflection abnormal analysis result;

[0078] In one specific embodiment, performing the required surgical lamp brightness analysis on the obtained surgical image brightness analysis result and surgical instrument reflection abnormal analysis result includes the following specific contents:

[0079] Substitute the obtained surgical standard brightness value, brightness adjustment analysis value, and overall reflection anomaly value into the surgical lamp brightness calculation formula to calculate the required brightness of the surgical lamp. The surgical lamp brightness calculation formula is as follows: Among them, Fm is the surgical standard brightness value, NF is the overall reflection anomaly value, and λ is the brightness adjustment analysis proportion coefficient;

[0080] In this embodiment, it should also be specifically noted that the value-taking methods of the unknown parameters in this application, such as the brightness adjustment analysis proportion coefficient, clarity proportion coefficient, and discrimination proportion coefficient, are as follows: Obtain the image data of the historical surgical site and the information of the surgical instruments to be used, substitute them into the surgical lamp brightness calculation formula to calculate the required brightness of the surgical lamp, obtain the scoring judgment results of experts on the required brightness of the surgical lamp in these scenarios, and import the required brightness of the surgical lamp and the scoring judgment results of the required brightness of the surgical lamp into the fitting software to output the values of unknown parameters such as the brightness adjustment analysis proportion coefficient, clarity proportion coefficient, and discrimination proportion coefficient that meet the maximum judgment accuracy rate;

[0081] S5. The control component adjusts the surgical lamp to the analyzed brightness of the surgical lamp. Here, it should be noted that the control component can be any controller capable of adjusting the brightness of the surgical lamp.

[0082] The advantages of setting the surgical lamp brightness control analysis method in this way compared with the prior art are as follows: By comprehensively evaluating the clarity of the surgical position and the distinction between the surgical position and the non-surgical position, accurate scene analysis of the required surgical lamp brightness is carried out, improving the adaptability of the surgical lamp brightness. At the same time, by comprehensively analyzing the scene situation of the surgical position and the reflection anomaly of the surgical instruments during the surgical process, the appropriate surgical lamp brightness is further analyzed, further improving the adaptability of the surgical lamp brightness.

[0083] As Figure 4 shown, this embodiment also provides a surgical lamp brightness energy-saving analysis system based on big data processing, which is implemented based on the above-mentioned surgical lamp brightness energy-saving analysis method based on big data processing, and specifically includes a data acquisition module, an image brightness analysis module, a reflection anomaly analysis module, a surgical lamp brightness analysis module, and a brightness adjustment module;

[0084] Among them, the data acquisition module is used to obtain the image data of the surgical site in real time and obtain the information of the surgical instruments to be used at the same time;

[0085] The image brightness analysis module is used to import the obtained image data of the surgical site into the image brightness analysis model for surgical image brightness analysis;

[0086] The reflection anomaly analysis module is used to import the information of the surgical instruments to be used into the image reflection analysis model for surgical instrument reflection anomaly analysis;

[0087] The surgical lamp brightness analysis module is used to perform the required surgical lamp brightness analysis on the obtained surgical image brightness analysis results and the analysis results of abnormal reflection of surgical instruments;

[0088] The brightness adjustment module is used to control the component to adjust the surgical lamp to the analyzed surgical lamp brightness; Figure 4 The arrow in the figure indicates the data transmission direction of this system. At the same time, all the process steps of this system have been covered in the above method and will not be elaborated here.

[0089] This embodiment also provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory;

[0090] The processor executes the above-mentioned surgical lamp brightness energy-saving analysis method based on big data processing by calling the computer program stored in the memory.

[0091] This electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the surgical lamp brightness energy-saving analysis method provided by the above method embodiment. This electronic device can also include other components for realizing the functions of the device. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.

[0092] This embodiment also proposes a computer-readable storage medium, on which a rewritable computer program is stored;

[0093] When the computer program runs on a computer device, it causes the computer device to execute the above-mentioned surgical lamp brightness energy-saving analysis method based on big data processing.

[0094] For example, the computer-readable storage medium can be a read-only memory, a random access memory, a read-only optical disc, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0095] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0096] The term "comprising", "including", or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or apparatus.

[0097] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, the technical solutions formed by mutually replacing the above features with (but not limited to) technical features having similar functions in the present application.

Claims

1. A method for analyzing the brightness of surgical lamps based on big data processing, characterized in that: It includes the following specific steps: Acquire image data of the surgical site in real time, and simultaneously obtain information on the surgical instruments that need to be used; Importing the acquired image data of the surgical site into the image brightness analysis model to perform brightness analysis of the surgical image; Based on the information of the surgical instruments to be used, the image reflection analysis model is imported to perform reflection anomaly analysis of the surgical instruments; Perform the required surgical light brightness analysis based on the acquired surgical image brightness analysis results and surgical instrument reflection abnormality analysis results; The control component adjusts the operating light to the brightness of the operating light obtained by analysis.

2. The method for analyzing the brightness of surgical lamps based on big data processing according to claim 1, characterized in that: The step of importing the acquired image data of the surgical site into the image brightness analysis model to perform surgical image brightness analysis comprises the following specific steps: Acquire image data corresponding to the surgical site, and import the image data of the surgical site into the image clarity analysis strategy for image clarity analysis; Acquire image data corresponding to the surgical site, and import the image data of the surgical site into the surgical site discrimination analysis strategy to perform surgical site discrimination analysis; The obtained image clarity analysis results and surgical position differentiation analysis results are introduced into the brightness analysis strategy for brightness analysis.

3. The method for analyzing the brightness of surgical lamps based on big data processing according to claim 2, characterized in that: The image clarity analysis strategy includes the following specific steps: Obtain the grayscale value of each pixel of the collected image, substitute it into the average value calculation formula to calculate the grayscale average value of the collected image, where the average value calculation formula is: Where N is the number of pixels in the vertical direction of the image, M is the number of pixels in the horizontal direction of the image, and f(i,j) is the grayscale value of the pixel with coordinate i and vertical coordinate j in the image; Obtain the grayscale average value and the grayscale value of each pixel of the collected image and substitute them into the image clarity coefficient calculation formula to calculate the image clarity coefficient, where the image clarity coefficient calculation formula is:

4. The method for analyzing the brightness of surgical lamps based on big data processing as claimed in claim 3, characterized in that: The specific steps of the surgical location discrimination analysis strategy are: The surgical center and surgical position are obtained, and the surgical position is diffused outward at twice the distance from each point on the surgical position boundary to the surgical center to obtain an image of the surgical position magnified twice. The surgical position is removed from the obtained image to obtain the non-surgical position. At the same time, the boundary area between the surgical position and the non-surgical position is obtained, and the calculated clarity coefficient of the surgical position and the clarity coefficient of the non-surgical position are obtained and imported into the surgical position discrimination calculation formula to calculate the surgical position discrimination. The surgical position discrimination calculation formula is: Among them, sw is the clarity coefficient of the surgical position, and sz is the clarity coefficient of the non-surgical position.

5. The method for analyzing the brightness of surgical lamps based on big data processing according to claim 4, characterized in that: The brightness analysis strategy includes the following specific contents: The image clarity coefficient and the surgical position distinction of the surgical position are obtained, and imported into the brightness adjustment analysis value calculation formula to calculate the brightness adjustment analysis value, wherein the brightness adjustment analysis value calculation formula is: Among them, exp() is the power of the natural constant e, a1 is the clarity ratio coefficient, a2 is the discrimination ratio coefficient, swm is the clarity coefficient threshold, and Qm is the discrimination threshold.

6. The method for analyzing the brightness of surgical lamps based on big data processing according to claim 5, characterized in that: The method of importing the information of the surgical instrument to be used into the image reflection analysis model to perform the abnormal reflection analysis of the surgical instrument includes the following specific steps: Obtain reflective area data, material reflectivity data, and usage frequency data of surgical instruments; The acquired reflective area data, material reflectivity data and usage frequency data of the surgical instrument are imported into the instrument reflective abnormality value calculation formula to calculate the instrument reflective abnormality value, wherein the instrument reflective abnormality value calculation formula is: Wherein, b is the material ratio coefficient, Sx is the reflective area data of the surgical instrument, Mx is the material reflectivity of the surgical instrument, Sm is the average reflective area of ​​all surgical instruments, Mm is the average reflectivity of the material of the surgical instrument, Xc is the average displacement of the surgical instrument when used for the cth time, Xm is the average distance used by all instruments, and R is the average number of times the surgical instrument is used; The instrument reflection abnormality values ​​of all surgical instruments required to be used in the corresponding surgery are obtained, and the instrument reflection abnormality values ​​of all surgical instruments are added together to obtain the overall reflection abnormality value.

7. The method for analyzing the brightness of surgical lamps based on big data processing according to claim 6, characterized in that: The step of performing the required surgical light brightness analysis on the acquired surgical image brightness analysis results and surgical instrument reflection abnormality analysis results includes the following specific contents: Substitute the obtained surgical standard brightness value, brightness adjustment analysis value and overall reflection abnormality value into the surgical light brightness calculation formula to calculate the required brightness of the surgical light. The surgical light brightness calculation formula is: Among them, Fm is the standard brightness value for surgery, NF is the overall reflection abnormality value, and λ is the brightness adjustment analysis ratio coefficient.

8. A surgical lamp brightness energy-saving analysis system based on big data processing, which is implemented based on the surgical lamp brightness energy-saving analysis method based on big data processing as claimed in any one of claims 1 to 7, characterized in that: It specifically includes a data acquisition module, an image brightness analysis module, a reflection abnormality analysis module, an operating light brightness analysis module and a brightness adjustment module; The data acquisition module is used to acquire image data of the surgical site in real time and simultaneously acquire information of the surgical instruments to be used; The image brightness analysis module is used to import the acquired image data of the surgical site into the image brightness analysis model to perform brightness analysis of the surgical image; The reflection anomaly analysis module is used to import the information of the surgical instrument to be used into the image reflection analysis model to perform reflection anomaly analysis on the surgical instrument; The surgical light brightness analysis module is used to perform required surgical light brightness analysis on the acquired surgical image brightness analysis results and surgical instrument reflection abnormality analysis results; The brightness adjustment module is used to control the components to adjust the operating light to the brightness of the operating light obtained through analysis.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the surgical lamp brightness energy-saving analysis method based on big data processing as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the operating lamp brightness energy-saving analysis method based on big data processing as described in any one of claims 1 to 7.

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