Method for Assisting in Identifying Postoperative Pain of Patients under Anesthesia Based on Near-Infrared Technology

Abstract

The present invention relates to the field of image recognition technology, and particularly to a method for assisting in the recognition of postoperative pain of patients based on near-infrared technology. The method includes: obtaining near-infrared images of the surgical site of the patient to be detected after anesthesia at a historical moment and the current moment; determining the temperature increase coefficient, temperature fluctuation factor, and postoperative pain factor corresponding to each pixel point in the near-infrared image; determining the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the differences between the temperature increase coefficient, postoperative pain factor, and the temperature increase coefficients of the pixel points at the same position; and determining the target pain level corresponding to each pixel point in the current near-infrared image according to the pain occurrence factor and postoperative pain factor corresponding to each pixel point in the current near-infrared image. By analyzing the near-infrared image, the present invention relatively objectively quantifies the pain levels at different positions of the patient's surgical site, thereby improving the accuracy of the recognition of postoperative pain of patients.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and particularly to a method for assisting in recognizing postoperative pain of patients based on near-infrared technology. Background Art

[0002] After a patient's anesthesia surgery is completed, it is often necessary to judge the patient's pain level in order for the doctor to evaluate whether analgesic drugs need to be given to the patient. Currently, when judging the patient's pain level, the commonly used method is: evaluating the patient's pain level through the pain self-assessment scale filled out by the patient. However, due to the conceptual differences in the understanding of pain among different patients, it may be difficult for patients to accurately fill out the pain self-assessment scale, resulting in the doctor being unable to obtain the patient's true pain level.

[0003] Currently, another method for judging the patient's pain level is: based on the patient's facial image, through a neural network, judging whether the patient's facial expression is painful to judge the patient's pain level.

[0004] However, when judging whether the patient's facial expression is painful based on the facial image after the patient's anesthesia surgery through a neural network to judge the patient's pain level, the following technical problems often exist:

[0005] When judging the patient's pain level by judging whether the patient's facial expression is painful, it is often considered that the higher the pain level of the patient, the more painful the patient's facial expression. However, different patients have different pain tolerances. For example, some patients may show a higher tolerance to pain when they are reluctant to show a painful expression due to subjective psychological factors. As a result, the patient's facial expression cannot objectively and truly represent the patient's pain level. Therefore, when judging the patient's pain level only by judging whether the patient's facial expression is painful, it may lead to misjudgment of the pain level, resulting in poor accuracy in recognizing postoperative pain of patients. Summary of the Invention

[0006] In order to solve the technical problem of poor accuracy in recognizing postoperative pain of patients, the present invention proposes a method for assisting in recognizing postoperative pain of patients based on near-infrared technology.

[0007] In a first aspect, the present invention provides a method for assisting in recognizing postoperative pain of patients based on near-infrared technology, the method comprising:

[0008] Obtaining near-infrared images of the surgical site of the patient to be detected after anesthesia at a historical moment and a current moment, respectively, as a historical near-infrared image and a current near-infrared image;

[0009] Determine the heating coefficient corresponding to each pixel point in the near-infrared image according to the gray value corresponding to each pixel point in the near-infrared image and the difference between the corresponding gray value and the gray value of the pixel point at the same position in the previous frame of the near-infrared image;

[0010] Determine the temperature fluctuation factor corresponding to each pixel point in the near-infrared image according to the distribution of the maximum pixel points in the preset neighborhood within the preset surrounding area corresponding to each pixel point in the near-infrared image;

[0011] Determine the postoperative pain factor corresponding to each pixel point in the near-infrared image according to the heating coefficient and the temperature fluctuation factor within the preset surrounding area corresponding to each pixel point in the near-infrared image;

[0012] Determine the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the heating coefficient and the postoperative pain factor in the current near-infrared image and the difference between the heating coefficient corresponding to each pixel point in the current near-infrared image and the heating coefficient of the pixel point at the same position in the previous preset number of frames of historical near-infrared images;

[0013] Determine the target pain level corresponding to each pixel point in the current near-infrared image according to the pain occurrence factor and the postoperative pain factor corresponding to each pixel point in the current near-infrared image.

[0014] Combined with the first aspect above, in a possible implementation manner, the step of determining the heating coefficient corresponding to each pixel point in the near-infrared image according to the gray value corresponding to each pixel point in the near-infrared image and the difference between the corresponding gray value and the gray value of the pixel point at the same position in the previous frame of the near-infrared image includes:

[0015] Determine any pixel point in any frame of the near-infrared image as a marked pixel point, and screen out the pixel point with the same position as the marked pixel point from the previous frame of the near-infrared image to which the marked pixel point belongs as a reference pixel point;

[0016] Normalize the difference between the gray value corresponding to the marked pixel point and the gray value corresponding to the reference pixel point to obtain the gray difference index corresponding to the marked pixel point;

[0017] Normalize the product of the gray value corresponding to the marked pixel point and the gray difference index to obtain the heating coefficient corresponding to the marked pixel point.

[0018] Combined with the first aspect above, in a possible implementation manner, the step of determining the temperature fluctuation factor corresponding to each pixel point in the near-infrared image according to the distribution of the maximum pixel points in the preset neighborhood within the preset surrounding area corresponding to each pixel point in the near-infrared image includes:

[0019] Select the pixel point with the maximum gray value from the preset neighborhood corresponding to each pixel point as the maximum pixel point of the preset neighborhood;

[0020] According to the distance between different maximum pixel points of the preset neighborhood and their nearest maximum pixel points of the preset neighborhood in the preset surrounding area corresponding to each pixel point in the near-infrared image, and the gray distribution between the maximum pixel points of the preset neighborhood and their nearest maximum pixel points of the preset neighborhood in the preset surrounding area corresponding to each pixel point in the near-infrared image, determine the temperature fluctuation factor corresponding to each pixel point in the near-infrared image.

[0021] Combined with the above first aspect, in a possible implementation, the formula corresponding to the temperature fluctuation factor of the pixel points in the near-infrared image is:

[0022] ; where is the temperature fluctuation factor corresponding to the th pixel point in the th frame of the near-infrared image; is the serial number of the near-infrared image; is the serial number of the pixel points in each frame of the near-infrared image; is the hyperbolic tangent function; is the th frame of the near-infrared image, and the number of maximum pixel points of the preset neighborhood corresponding to the th pixel point in the preset surrounding area; is the th frame of the near-infrared image, and the serial number of the maximum pixel point of the preset neighborhood corresponding to the th pixel point in the preset surrounding area; is the th frame of the near-infrared image, and the gray value corresponding to the th pixel point in the preset surrounding area, and the th maximum pixel point of the preset neighborhood; is the th frame of the near-infrared image, and the gray value corresponding to the target maximum pixel point of the preset neighborhood corresponding to the th pixel point; the target maximum pixel point of the preset neighborhood is the maximum pixel point of the preset neighborhood closest to the th maximum pixel point of the preset neighborhood; is the th frame of the near-infrared image, and the th pixel point in the preset surrounding area, and the th maximum pixel point of the preset neighborhood and the mean value of the gray values corresponding to all pixel points on the line connecting the target maximum pixel point of the preset neighborhood; is a factor greater than 0 set in advance; is the In the preset surrounding area corresponding to the th pixel point in the frame near-infrared image, the distance between the th preset neighborhood maximum pixel point and the target neighborhood maximum pixel point.

[0023] Combined with the above first aspect, in a possible implementation manner, the determining the postoperative pain factor corresponding to each pixel point in the near-infrared image according to the temperature increase coefficient and the temperature fluctuation factor in the preset surrounding area corresponding to each pixel point in the near-infrared image includes:

[0024] Determining the average value of the temperature fluctuation factors corresponding to all pixel points in the preset surrounding area corresponding to each pixel point as the temperature fluctuation representative index corresponding to each pixel point;

[0025] Normalizing the variance of the temperature increase coefficients corresponding to all pixel points in the preset surrounding area corresponding to each pixel point to obtain the temperature increase distribution factor corresponding to each pixel point;

[0026] Normalizing the absolute value of the difference between the temperature fluctuation representative index and the temperature increase distribution factor corresponding to each pixel point to obtain the postoperative pain factor corresponding to each pixel point.

[0027] Combined with the above first aspect, in a possible implementation manner, the determining the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the temperature increase coefficient and the postoperative pain factor in the current near-infrared image, and the difference between the temperature increase coefficient corresponding to each pixel point in the current near-infrared image and the temperature increase coefficient corresponding to the pixel point at the same position in the previous preset number of frames of historical near-infrared images includes:

[0028] Determining the overall temperature increase coefficient corresponding to the current near-infrared image according to the temperature increase coefficients and the postoperative pain factors corresponding to all pixel points in the current near-infrared image;

[0029] Determining each frame of historical near-infrared image in the previous preset number of frames of historical near-infrared images of the current near-infrared image as a reference infrared image;

[0030] Determining the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the overall temperature increase coefficient, the gray value corresponding to each pixel point in the current near-infrared image and the gray value corresponding to the pixel point at the same position in the preset number of frames of reference infrared images, and the difference between the temperature increase coefficient corresponding to each pixel point in the current near-infrared image and the temperature increase coefficient corresponding to the pixel point at the same position in the preset number of frames of reference infrared images.

[0031] Combined with the above first aspect, in a possible implementation manner, the formula corresponding to the overall temperature increase coefficient of the current near-infrared image is:

[0032] ; where, is the overall temperature rise coefficient corresponding to the current near-infrared image; is the hyperbolic tangent function; is the number of pixel points in each frame of the near-infrared image; is the serial number of the pixel point in each frame of the near-infrared image; is the th temperature rise coefficient corresponding to the pixel point in the current near-infrared image; is the natural exponential function; is the postoperative pain factor corresponding to the th pixel point in the current near-infrared image.

[0033] Combined with the above first aspect, in a possible implementation manner, the formula for the pain occurrence factor corresponding to the pixel point in the current near-infrared image is:

[0034] ;

[0035] ;

[0036] ;

[0037] ; where, is the th pain occurrence factor corresponding to the pixel point in the current near-infrared image; is the serial number of the pixel point in each frame of the near-infrared image; is the normalization function; is the cumulative value of the gray value corresponding to the th pixel point in the current near-infrared image and the gray value corresponding to the th pixel point in the preset number of frames of reference infrared images; is the overall temperature rise coefficient corresponding to the current near-infrared image; is the natural exponential function; represents the temperature change situation at the position where the th pixel point is located; is the preset number; is the serial number of the reference infrared image; is the th frame of reference infrared image, and is the difference between the temperature rise coefficient corresponding to the th pixel point and the temperature rise coefficient corresponding to the pixel point at the same position in the subsequent frame of infrared image; is the th frame of reference infrared image, and is the The difference between the heating coefficient corresponding to a pixel and the heating coefficient corresponding to the pixel at the same position in the subsequent frame of the infrared image. is the heating coefficient corresponding to the th pixel in the th frame of the reference infrared image; is the heating coefficient corresponding to the th pixel in the infrared image subsequent to the th frame of the reference infrared image; is the heating coefficient corresponding to the th pixel in the th frame of the reference infrared image; is the heating coefficient corresponding to the th pixel in the infrared image subsequent to the th frame of the reference infrared image.

[0038] Combined with the first aspect above, in a possible implementation, the determining the target pain level corresponding to each pixel in the current near-infrared image according to the pain occurrence factor and the postoperative pain factor corresponding to each pixel in the current near-infrared image includes:

[0039] Normalize the product of the pain occurrence factor and the postoperative pain factor corresponding to each pixel in the current near-infrared image to obtain the target pain index corresponding to each pixel in the current near-infrared image;

[0040] Determine the target pain level corresponding to each pixel in the current near-infrared image according to the target pain index corresponding to each pixel in the current near-infrared image.

[0041] Combined with the first aspect above, in a possible implementation, the determining the target pain level corresponding to each pixel in the current near-infrared image according to the target pain index corresponding to each pixel in the current near-infrared image includes:

[0042] If the target pain index corresponding to a pixel is greater than a preset pain threshold, then determine the pixel as a pain pixel;

[0043] If a pixel is a pain pixel and the proportion of pain pixels in its corresponding preset neighborhood is greater than or equal to a preset proportion, then set the target pain level corresponding to the pixel to a high pain level, otherwise set the target pain level corresponding to the pixel to a low pain level.

[0044] In a second aspect, the present invention provides a patient postoperative pain assisted recognition system based on near-infrared technology, and the system includes:

[0045] A near-infrared image acquisition module, configured to acquire near-infrared images of the surgical site of a patient to be detected after anesthesia at a historical moment and a current moment, respectively serving as a historical near-infrared image and a current near-infrared image;

[0046] A heating coefficient determination module, configured to determine a heating coefficient corresponding to each pixel point in the near-infrared image according to the gray value corresponding to each pixel point in the near-infrared image and the difference between the corresponding gray value and the gray value of the pixel point at the same position in the previous frame of the near-infrared image;

[0047] A temperature fluctuation factor determination module, configured to determine a temperature fluctuation factor corresponding to each pixel point in the near-infrared image according to the distribution of the maximum pixel points in the preset neighborhood within the preset surrounding area corresponding to each pixel point in the near-infrared image;

[0048] A postoperative pain factor determination module, configured to determine a postoperative pain factor corresponding to each pixel point in the near-infrared image according to the heating coefficient and the temperature fluctuation factor within the preset surrounding area corresponding to each pixel point in the near-infrared image;

[0049] A pain occurrence factor determination module, configured to determine a pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the heating coefficient and the postoperative pain factor in the current near-infrared image, and the difference between the heating coefficient corresponding to each pixel point in the current near-infrared image and the heating coefficient of the pixel point at the same position in the previous preset number of frames of historical near-infrared images;

[0050] A target pain level determination module, configured to determine a target pain level corresponding to each pixel point in the current near-infrared image according to the pain occurrence factor and the postoperative pain factor corresponding to each pixel point in the current near-infrared image.

[0051] In a third aspect, a server is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0052] In a fourth aspect, a computer program product is provided, including: computer program code, when the computer program code runs on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0053] In a fifth aspect, a computer-readable storage medium is provided, storing computer program code, when the computer program code runs on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0054] The present invention has the following beneficial effects:

[0055] The method for assisting in identifying the pain of patients after anesthesia based on near-infrared technology of the present invention analyzes near-infrared images, relatively objectively quantifies the pain levels at different positions of the surgical site of the patient, solves the technical problem of poor accuracy in identifying the pain of patients after anesthesia, and thus improves the accuracy of identifying the pain of patients after anesthesia. In actual situations, the surgical site after anesthesia may have abnormal temperature distribution due to inflammation or infection, etc., which may exacerbate the patient's pain level. The present invention analyzes historical near-infrared images and current near-infrared images, quantifies multiple factors related to the temperature distribution of the surgical site, such as the heating coefficient and the temperature fluctuation factor, and thus quantifies factors related to the pain degree, such as the postoperative pain factor and the pain occurrence factor, and further relatively objectively quantifies the target pain level representing the pain levels at different positions of the surgical site, reducing the influence of the patient's subjective psychological factors or subjective pain understanding factors to a certain extent, and thus improving the accuracy of identifying the pain of patients after anesthesia. Description of the Drawings

[0056] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0057] Figure 1 is a flowchart of the method for assisting in identifying the pain of patients after anesthesia based on near-infrared technology of the present invention;

[0058] Figure 2 is a schematic diagram of the composition structure of the system for assisting in identifying the pain of patients after anesthesia based on near-infrared technology of the present invention;

[0059] Figure 3 is a schematic diagram of the structure of a computer device of the present invention. Detailed Embodiments

[0060] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific embodiments, structures, features, and effects of the technical solutions proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0062] Reference Figure 1 , which shows the flow of some embodiments of a method for assisting in the identification of postoperative pain in patients based on near-infrared technology according to the present invention. The method for assisting in the identification of postoperative pain in patients based on near-infrared technology includes the following steps:

[0063] Step S1, obtaining near-infrared images of the surgical site of the patient to be detected after anesthesia at a historical moment and a current moment, respectively, as a historical near-infrared image and a current near-infrared image.

[0064] Among them, the patient to be detected can be a patient undergoing pain identification after anesthesia. The surgical site can be the body part where the surgery is performed. The historical moment can be a moment before the current moment.

[0065] As an example, through a near-infrared imaging device, within the current time period, a near-infrared image of the surgical site of the patient to be detected is collected every 5 seconds, and the near-infrared image collected at the end moment of the current time period is used as the current near-infrared image, and each near-infrared image except the current near-infrared image among all the near-infrared images collected within the current time period is used as the historical near-infrared image. Among them, the current time period can be the time period after the patient to be detected is anesthetized, its start moment can be the end moment of the surgery of the patient to be detected, and its end moment can be the current moment. Each image acquisition moment within the current time period except the end moment is the historical moment. The near-infrared imaging device can be a NIRS (Near Infrared Spectroscopy, using near-infrared spectroscopy technology for imaging) device. Near-infrared technology (NIR) is a technology for analysis and imaging by detecting radiation in the near-infrared spectral range, and is widely used in fields such as medicine, life science, and materials research.

[0066] It should be noted that before monitoring, it is often necessary to calibrate the NIRS device to ensure accurate measurement. According to the device's instruction manual, check whether the light source, probe, and sensor are working properly, and select a suitable NIRS probe according to the patient's postoperative site; clean the imaging site with a sterile cleaner to remove dirt and grease to ensure clear images; align the near-infrared imaging probe with the target site and ensure good contact between the probe and the skin; start the near-infrared imaging device, start image acquisition of the postoperative area, and adjust imaging parameters as needed, such as exposure time and light source intensity, thereby obtaining near-infrared images of the patient's surgical site.

[0067] In actual situations, after surgery, the local temperature of the patient may increase due to inflammation or infection. During the healing process, temperature changes can reflect the state of blood circulation and metabolism. Therefore, in the embodiments of the present invention, pain assessment is performed by analyzing the temperature increase and abnormal temperature changes at the surgical site of the patient in the collected near-infrared images.

[0068] Step S2: Determine the temperature increase coefficient corresponding to each pixel point in the near-infrared image according to the gray value corresponding to each pixel point in the near-infrared image and the difference between its corresponding gray value and the gray value of the pixel point at the same position in the previous frame of the near-infrared image.

[0069] As an example, this step may include the following steps:

[0070] First step: Determine any pixel point in any frame of the near-infrared image as a marked pixel point, and select the pixel point at the same position as the marked pixel point from the previous frame of the near-infrared image to which the marked pixel point belongs as a reference pixel point.

[0071] Second step: Normalize the difference between the gray value corresponding to the marked pixel point and the gray value corresponding to the reference pixel point to obtain the gray difference index corresponding to the marked pixel point.

[0072] Third step: Normalize the product of the gray value corresponding to the marked pixel point and the gray difference index to obtain the temperature increase coefficient corresponding to the marked pixel point.

[0073] For example, the formula for determining the temperature increase coefficient corresponding to a pixel point in the near-infrared image can be:

[0074] ;

[0075] ; where is the temperature increase coefficient corresponding to the th pixel point in the th frame of the near-infrared image. is the serial number of the near-infrared image. is the serial number of the pixel point in each frame of the near-infrared image. is the hyperbolic tangent function. is the th frame of the near-infrared image. is the gray value corresponding to the th pixel point in the th frame of the near-infrared image. is the gray difference index corresponding to the th pixel point in the th frame of the near-infrared image. th frame of the near-infrared image. The gray value corresponding to a pixel point. In different near-infrared images, the pixel points can represent the pixel points at the same position in different near-infrared images.

[0076] It should be noted that the pixel points with higher gray values in the near-infrared image often reflect the positions of the body parts with higher temperatures, while the pixel points with lower gray values often reflect the positions of the body parts with lower temperatures. Secondly, the degree of temperature rise in the surgical site due to inflammation or infection is often higher than that during the normal recovery of the surgical site. Therefore, when there is a high temperature rise at a certain body part position, it often indicates that inflammation or infection is more likely to have occurred at that position, and it often indicates that the pain degree at that position may be greater. When is larger, it often indicates that the gray value corresponding to the pixel point in the frame of near-infrared image is larger, and it often indicates that the temperature of the body part position represented by the pixel point may be higher. When is larger, it often indicates that the gray value of the pixel point in the frame of near-infrared image is more likely to be higher than its gray value in the frame of near-infrared image, and it often indicates that the temperature of the body part position represented by the pixel point is more likely to show an upward trend. Therefore, when is larger, it often indicates that the body part position represented by the pixel point is more likely to show a trend of high temperature rise at the acquisition moment corresponding to the frame of near-infrared image, and it often indicates that the body part position represented by the pixel point is more likely to show a higher pain degree at the acquisition moment corresponding to the

[0077] Step S3, according to the distribution of the preset neighborhood maximum pixel points in the preset surrounding area corresponding to each pixel point in the near-infrared image, determine the temperature fluctuation factor corresponding to each pixel point in the near-infrared image.

[0078] Among them, the preset surrounding area can be a preset rectangular area. For example, the preset surrounding area can be a 7×7 area. The pixel point can be located at the center of its corresponding preset surrounding area.

[0079] As an example, this step may include the following steps:

[0080] The first step is to screen out the pixel point with the largest gray value from the preset neighborhood corresponding to each pixel point as the preset neighborhood maximum pixel point.

[0081] Among them, the preset neighborhood can be a pre-set rectangular neighborhood, and its size is smaller than that of the preset surrounding area. For example, the preset neighborhood can be a 3×3 neighborhood. The pixel point can be located at the center of its corresponding preset neighborhood. A pixel point in the near-infrared image can correspond to a preset neighborhood maximum pixel point. Therefore, the near-infrared image can contain multiple preset neighborhood maximum pixel points.

[0082] In the second step, according to the distance between different preset neighborhood maximum pixel points and their nearest preset neighborhood maximum pixel points within the preset surrounding area corresponding to each pixel point in the near-infrared image, and the gray-scale distribution between the preset neighborhood maximum pixel points and their nearest preset neighborhood maximum pixel points within the preset surrounding area corresponding to each pixel point in the near-infrared image, determine the temperature fluctuation factor corresponding to each pixel point in the near-infrared image.

[0083] For example, the formula for determining the temperature fluctuation factor corresponding to the pixel points in the near-infrared image can be:

[0084] ; where is the temperature fluctuation factor corresponding to the th pixel point in the th frame of the near-infrared image. is the serial number of the near-infrared image. is the serial number of the pixel point in each frame of the near-infrared image. is the hyperbolic tangent function. is the th frame of the near-infrared image, and is the number of preset neighborhood maximum pixel points within the preset surrounding area corresponding to the th pixel point in the th frame of the near-infrared image. is the serial number of the preset neighborhood maximum pixel point within the preset surrounding area corresponding to the th pixel point in the th frame of the near-infrared image. is the gray-scale value corresponding to the th preset neighborhood maximum pixel point within the preset surrounding area corresponding to the th pixel point in the th frame of the near-infrared image. is the gray-scale value corresponding to the target neighborhood maximum pixel point within the preset surrounding area corresponding to the th pixel point in the th frame of the near-infrared image. The target neighborhood maximum pixel point is the preset neighborhood maximum pixel point closest to the th preset neighborhood maximum pixel point. is the The mean of the gray values corresponding to all the pixels on the line connecting a preset neighborhood maximum pixel and a target neighborhood maximum pixel. is a preset factor greater than 0, mainly used to prevent the denominator from being 0. For example, can be 0.001. is the distance between the th preset neighborhood maximum pixel and the target neighborhood maximum pixel within the preset surrounding area corresponding to the th pixel in the

[0085] It should be noted that after the operation, inflammation may occur at the surgical site, and the blood flow velocity may become faster or slower, which may lead to a relatively large difference in the temperature change of the surgical site compared with the normal human body temperature change. Since the human tissue distribution is relatively uniform, the change of the normal body surface temperature is often relatively smooth. However, after the operation, due to the possible suture wounds on the body surface, inflammation and infection may occur, and the blood flow at the postoperative site may increase or decrease, which may lead to a more obvious temperature change at the surgical site after the operation. That is to say, the more obvious the temperature change, the more likely it is the site of inflammation or infection, and the greater the degree of pain. is the weight of When is smaller, it often means that the distance between the th preset neighborhood maximum pixel and its nearest preset neighborhood maximum pixel is smaller. When is larger, it often means that the gray difference between the th preset neighborhood maximum pixel and its nearest preset neighborhood maximum pixel and their surrounding pixels is larger; it often means that the gray change degree between the th preset neighborhood maximum pixel and its nearest preset neighborhood maximum pixel is larger, and it often means that the temperature change degree between the position of the part represented by the th preset neighborhood maximum pixel and the position of the part represented by its nearest preset neighborhood maximum pixel is larger. Therefore, when is larger, it often means that the gray change degree between the relatively close preset neighborhood maximum pixels within the preset surrounding area corresponding to the th pixel in the th frame of near-infrared image is larger, and it often means that the gray change degree within the preset surrounding area corresponding to the th pixel in the th frame of near-infrared image is larger, and it often means that the temperature change degree around the position of the part represented by the th pixel at the acquisition moment corresponding to the At the acquisition moment corresponding to the frame near-infrared image, the pain level around the position of the part represented by the th pixel point may be greater.

[0086] Step S4: Determine the postoperative pain factor corresponding to each pixel point in the near-infrared image according to the temperature increase coefficient and temperature fluctuation factor within the preset surrounding area corresponding to each pixel point in the near-infrared image.

[0087] As an example, this step may include the following steps:

[0088] First step: Determine the mean value of the temperature fluctuation factors corresponding to all pixel points within the preset surrounding area corresponding to each pixel point as the temperature fluctuation representative index corresponding to each pixel point.

[0089] Second step: Normalize the variance of the temperature increase coefficients corresponding to all pixel points within the preset surrounding area corresponding to each pixel point to obtain the temperature increase distribution factor corresponding to each pixel point.

[0090] Third step: Normalize the absolute value of the difference between the temperature fluctuation representative index and the temperature increase distribution factor corresponding to each pixel point to obtain the postoperative pain factor corresponding to each pixel point.

[0091] For example, the formula for determining the postoperative pain factor corresponding to the pixel point in the near-infrared image can be:

[0092] ; where is the postoperative pain factor corresponding to the th pixel point in the th frame of near-infrared image. is the serial number of the near-infrared image. is the serial number of the pixel point in each frame of near-infrared image. is the hyperbolic tangent function. is the absolute value function. is the temperature fluctuation representative index corresponding to the th pixel point in the th frame of near-infrared image, that is, the mean value of the temperature fluctuation factors corresponding to all pixel points within the preset surrounding area corresponding to the th pixel point in the th frame of near-infrared image. is the variance of the temperature increase coefficients corresponding to all pixel points within the preset surrounding area corresponding to the th pixel point in the th frame of near-infrared image. is the temperature increase distribution factor corresponding to the th pixel point in the th frame of near-infrared image.

[0093] It should be noted that since the postoperative site often involves operations such as suturing, which may result in some faults at the postoperative site, leading to a relatively obvious temperature fluctuation distribution at the postoperative site. Therefore, the temperature fluctuation factor at the postoperative site is often relatively large, and the degree of pain is relatively high. Inflammation may occur around the suture site, resulting in a difference between the temperature rise situation and the surrounding normal area. Since the embodiments of the present invention can identify pain within a short time after surgery, even if there is inflammation or infection at the surgical site, it is often relatively mild. Therefore, there are often normal areas around the inflammation or infection area. Due to the existence of the normal area around the inflammation or infection area, it may reduce the overall temperature fluctuation degree, because the temperature fluctuation degree at different positions within the normal area is often relatively small, and it often enlarges the overall temperature rise difference.

[0094] When is larger and is smaller, it often indicates that the temperature fluctuation around the th pixel point in the th frame of the near-infrared image is larger and the temperature rise trend is more uniform. It often indicates that the position represented by the th pixel point in the th frame of the near-infrared image is more likely to be a postoperative surgical position with uneven temperature distribution and relatively consistent temperature rise situation, and the degree of pain is relatively greater. When is smaller and is larger, it often indicates that the temperature fluctuation around the th pixel point in the th frame of the near-infrared image is relatively smaller and the temperature rise trend is relatively more discrete. It often indicates that the position represented by the th pixel point in the th frame of the near-infrared image is more likely to be a position with inflammation or infection. Therefore, when is larger, it often indicates that the position represented by the th pixel point in the th frame of the near-infrared image is more likely to be a surgical site, a position with inflammation or infection. It often indicates that the degree of pain at the position represented by the th pixel point in the th frame of the near-infrared image is relatively greater.

[0095] Step S5: Determine the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the temperature rise coefficient and the postoperative pain factor in the current near-infrared image, and the difference between the temperature rise coefficient corresponding to each pixel point in the current near-infrared image and the temperature rise coefficient corresponding to the pixel point at the same position in the previous preset number of frames of historical near-infrared images.

[0096] Among them, the preset number can be the number of images set in advance. For example, the preset number can be 5.

[0097] As an example, this step may include the following steps:

[0098] First, according to the temperature increase coefficients and postoperative pain factors corresponding to all pixel points in the current near-infrared image, determine the overall temperature increase coefficient corresponding to the current near-infrared image.

[0099] For example, the formula for determining the overall temperature increase coefficient corresponding to the current near-infrared image may be:

[0100] ; where is the overall temperature increase coefficient corresponding to the current near-infrared image. is the hyperbolic tangent function. is the number of pixel points in each frame of the near-infrared image. is the serial number of the pixel point in each frame of the near-infrared image. is the temperature increase coefficient corresponding to the th pixel point in the current near-infrared image. is the natural exponential function. is the postoperative pain factor corresponding to the th pixel point in the current near-infrared image.

[0101] It should be noted that can be used as the weight. When is smaller, it often indicates that the part represented by the th pixel point in the th frame of the near-infrared image is more likely to be a normal part, and its corresponding temperature increase situation can better represent the temperature increase situation in the normal area of the human body, and its temperature increase situation should be retained more. Therefore, can represent the overall temperature increase situation in the normal area of the human body.

[0102] Second, determine each frame of the historical near-infrared images in the previous preset number of frames of the current near-infrared image as the reference infrared images, and a preset number of frames of reference infrared images can be obtained.

[0103] Third, according to the differences between the above-mentioned overall temperature increase coefficient, the gray value corresponding to each pixel point in the current near-infrared image and the gray value corresponding to the pixel point at the same position in the preset number of frames of reference infrared images, and the temperature increase coefficient corresponding to each pixel point in the current near-infrared image and the temperature increase coefficient corresponding to the pixel point at the same position in the preset number of frames of reference infrared images, determine the pain occurrence factor corresponding to each pixel point in the current near-infrared image.

[0104] For example, the formula for determining the pain occurrence factor corresponding to the pixel point in the current near-infrared image may be:

[0105] ;

[0106] ;

[0107] ;

[0108] ; where, is the pain occurrence factor corresponding to the th pixel point in the current near-infrared image. is the serial number of the pixel point in each frame of the near-infrared image. is the normalization function. is the sum of the gray value corresponding to the th pixel point in the current near-infrared image and the gray value corresponding to the th pixel point in the preset number of frames of reference infrared images. is the overall temperature rise coefficient corresponding to the current near-infrared image. is the natural exponential function. characterizes the temperature change situation at the position of the th pixel point. is the preset number. is the serial number of the reference infrared image, which can be the serial number obtained by sorting the reference infrared images in the order of acquisition time. is the difference between the temperature rise coefficient corresponding to the th pixel point in the th frame of reference infrared image and the temperature rise coefficient corresponding to the pixel point at the same position in the subsequent frame of infrared image. is the difference between the temperature rise coefficient corresponding to the th pixel point in the th frame of reference infrared image and the temperature rise coefficient corresponding to the pixel point at the same position in the subsequent frame of infrared image. is the temperature rise coefficient corresponding to the th pixel point in the th frame of reference infrared image. is the temperature rise coefficient corresponding to the th pixel point in the subsequent frame of infrared image of the th frame of reference infrared image. is the temperature rise coefficient corresponding to the th pixel point in the th frame of reference infrared image. is the temperature rise coefficient corresponding to the th pixel point in the subsequent frame of infrared image of the th frame of reference infrared image. The subsequent frame of infrared image of the last frame of reference infrared image can be the current near-infrared image.

[0109] It should be noted that can characterize the overall temperature rise within the normal human body area. When its value is smaller, it often indicates that the degree of temperature rise in the patient is smaller, and it often means that the patient is relatively less likely to feel pain at this time and is relatively less likely to cause further inflammation development. When is larger, it often indicates that the th pixel has a larger gray value in the consecutive-frame infrared images, and it often means that the temperature at the position represented by the th pixel is higher. In actual situations, during the normal postoperative recovery process, the temperature rise of the surgical site often gradually decreases, and the degree of decrease becomes smaller and smaller. During this process, the degree of pain felt by the patient is relatively not very high. and can characterize the degree of decrease in the temperature rise corresponding to the th pixel in different-frame reference infrared images. can characterize the change in the degree of decrease in the temperature rise corresponding to the th pixel. The larger its value, the more it often indicates that the degree of decrease conforms to the law of gradually decreasing, and it often means that the degree of pain at the position represented by the th pixel is relatively not high. Therefore, when is larger, it often indicates that a relatively high degree of pain is more likely to occur at the position represented by the th pixel.

[0110] Step S6: Determine the target pain level corresponding to each pixel in the current near-infrared image according to the pain occurrence factor and the postoperative pain factor corresponding to each pixel in the current near-infrared image.

[0111] As an example, this step may include the following steps:

[0112] First step: Normalize the product of the pain occurrence factor and the postoperative pain factor corresponding to each pixel in the current near-infrared image to obtain the target pain index corresponding to each pixel in the current near-infrared image.

[0113] For example, the formula for determining the target pain index corresponding to the pixel in the current near-infrared image can be:

[0114] ; where is the target pain index corresponding to the th pixel in the current near-infrared image. is the serial number of the pixel in each frame of the near-infrared image. is the normalization function. is the pain occurrence factor corresponding to the th pixel in the current near-infrared image. is the postoperative pain factor corresponding to the th pixel point in the current near-infrared image.

[0115] It should be noted that when is larger, it often indicates that the higher the pain level is more likely to occur at the position of the part represented by the th pixel point. When is larger, it often indicates that the part represented by the pixel point in the current near-infrared image is more likely to be the surgical site, the site with inflammation or infection, and it often indicates that the pain level of the part represented by the th pixel point in the current near-infrared image is relatively larger. Therefore, when is larger, it often indicates that the pain level at the position of the part represented by the th pixel point is relatively larger.

[0116] The second step, according to the target pain index corresponding to each pixel point in the current near-infrared image, determining the target pain level corresponding to each pixel point in the current near-infrared image may include the following sub-steps:

[0117] The first sub-step, if the target pain index corresponding to the pixel point is greater than the preset pain threshold, then determine the pixel point as a pain pixel point.

[0118] Among them, the preset pain threshold can be a threshold set in advance for judging the pain level. For example, the preset pain threshold can be 0.7.

[0119] The second sub-step, if the pixel point is a pain pixel point and the proportion of pain pixel points in its corresponding preset neighborhood is greater than or equal to the preset proportion, then set the target pain level corresponding to the pixel point as a high pain level, otherwise set the target pain level corresponding to the pixel point as a low pain level.

[0120] Among them, the preset proportion can be the proportion of pixel points set in advance. For example, the preset proportion can be 0.5.

[0121] It should be noted that when there are more pixel points with a high pain level in the current near-infrared image, it often indicates that the pain level of the patient to be detected is more severe at the current moment. And if there are many pixel points with a high pain level in multiple historical near-infrared images consecutive to the current near-infrared image, it often means that the patient to be detected has been in a state of high pain for some time and often needs to be given appropriate analgesics. Among them, common analgesics can include, but are not limited to: non-steroidal anti-inflammatory drugs (NSAIDs) and opioid drugs. Non-steroidal anti-inflammatory drugs can include, but are not limited to: ibuprofen and paracetamol. Opioid drugs can include, but are not limited to: morphine and fentanyl. Therefore, based on the target pain level of the pixel points, the pain condition of the patient to be detected at the current moment can be judged to assist the doctor in identifying the postoperative pain of the patient. Specifically, judging the pain condition of the patient to be detected at the current moment based on the target pain level of the pixel points can include the following steps:

[0122] First, if the proportion of pixel points with a high pain level in the near-infrared image is greater than 40%, the near-infrared image is determined as a high-pain image.

[0123] Next, if the current near-infrared image and its first 9 frames of historical near-infrared images are all high-pain images, it is determined that the pain condition of the patient to be detected at the current moment is severe pain, and this information is sent to the doctor to assist the doctor in further pain assessment and judge whether it is necessary to give the patient appropriate analgesics based on the assessment result.

[0124] Reference Figure 2 , based on the same inventive concept as the above method embodiment, the present invention provides a system for assisting in the identification of postoperative pain of patients based on near-infrared technology. The system includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the above computer program is executed by the processor, it implements the steps of the method for assisting in the identification of postoperative pain of patients based on near-infrared technology, which can specifically include:

[0125] A near-infrared image acquisition module 201, configured to acquire near-infrared images of the surgical site of the patient to be detected after anesthesia at historical moments and the current moment, respectively, as historical near-infrared images and current near-infrared images;

[0126] A heating coefficient determination module 202, configured to determine the heating coefficient corresponding to each pixel point in the near-infrared image according to the gray value corresponding to each pixel point in the near-infrared image and the difference between its corresponding gray value and the gray value of the pixel point at the same position in the previous frame of the near-infrared image;

[0127] A temperature fluctuation factor determination module 203, configured to determine a temperature fluctuation factor corresponding to each pixel point in the near-infrared image according to the distribution of the preset neighborhood maximum pixel points in the preset surrounding area corresponding to each pixel point in the near-infrared image;

[0128] A postoperative pain factor determination module 204, configured to determine a postoperative pain factor corresponding to each pixel point in the near-infrared image according to the heating coefficient and the temperature fluctuation factor in the preset surrounding area corresponding to each pixel point in the near-infrared image;

[0129] A pain occurrence factor determination module 205, configured to determine a pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the heating coefficient and the postoperative pain factor in the current near-infrared image, and the difference between the heating coefficient corresponding to each pixel point in the current near-infrared image and the heating coefficient corresponding to the pixel points at the same position in the previous preset number of frames of historical near-infrared images;

[0130] A target pain level determination module 206, configured to determine a target pain level corresponding to each pixel point in the current near-infrared image according to the pain occurrence factor and the postoperative pain factor corresponding to each pixel point in the current near-infrared image.

[0131] Figure 3 It is a schematic structural diagram of a computer device provided by an embodiment of the present invention. Exemplarily, as Figure 3 shown, the computer device 300 includes: a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. Wherein, when the processor 302 executes the computer program 303, the computer device can execute any one of the foregoing near-infrared technology-based patient anesthesia postoperative pain auxiliary recognition methods.

[0132] Based on the same inventive concept as the above method embodiment, the present invention provides a server, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes any one of the foregoing near-infrared technology-based patient anesthesia postoperative pain auxiliary recognition methods.

[0133] Based on the same inventive concept as the above method embodiment, the present invention provides a computer program product, which includes: computer program code. When the computer program code runs on a computer, the computer executes any one of the foregoing near-infrared technology-based patient anesthesia postoperative pain auxiliary recognition methods.

[0134] Based on the same inventive concept as the above method embodiments, the present invention provides a computer-readable storage medium storing computer program code, which, when running on a computer, causes the computer to execute any of the above patient anesthesia postoperative pain auxiliary recognition methods based on near-infrared technology.

[0135] In summary, the present invention analyzes historical near-infrared images and current near-infrared images, quantifies a plurality of factors related to the temperature distribution of the surgical site, such as the heating coefficient and the temperature fluctuation factor, thereby quantifying factors related to the pain level, such as the postoperative pain factor and the pain onset factor, and further relatively objectively quantifying the target pain level representing the pain levels at different positions of the surgical site, reducing the influence of the patient's subjective psychological factors or subjective pain understanding factors to a certain extent, and thus improving the accuracy of patient anesthesia postoperative pain recognition.

[0136] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for assisting in the identification of postoperative pain in patients under anesthesia based on near-infrared technology, characterized in that, Including the following steps: Obtain near-infrared images of the surgical site of the patient to be detected after anesthesia at a historical moment and the current moment, respectively, as the historical near-infrared image and the current near-infrared image; Determine the heating coefficient corresponding to each pixel point in the near-infrared image according to the gray value corresponding to each pixel point in the near-infrared image and the difference between its corresponding gray value and the gray value of the pixel point at the same position in the previous frame of the near-infrared image; Determine the temperature fluctuation factor corresponding to each pixel point in the near-infrared image according to the distribution of the maximum pixel points in the preset neighborhood within the preset surrounding area corresponding to each pixel point in the near-infrared image, including: screening out the pixel point with the largest gray value from the preset neighborhood corresponding to each pixel point as the maximum pixel point in the preset neighborhood; determining the temperature fluctuation factor corresponding to each pixel point in the near-infrared image according to the distance between different maximum pixel points in the preset neighborhood and its nearest maximum pixel point within the preset surrounding area corresponding to each pixel point in the near-infrared image, and the gray distribution between the maximum pixel point in the preset neighborhood and its nearest maximum pixel point within the preset surrounding area corresponding to each pixel point in the near-infrared image; the formula corresponding to the temperature fluctuation factor of the pixel point in the near-infrared image is: ; wherein, is the temperature fluctuation factor corresponding to the th pixel point in the th near-infrared image; is the serial number of the near-infrared image; is the hyperbolic tangent function; is the th near-infrared image, and is the number of the maximum pixel points in the preset neighborhood within the preset surrounding area corresponding to the th pixel point; is the th near-infrared image, and is the serial number of the maximum pixel point in the preset neighborhood within the preset surrounding area corresponding to the th pixel point; is the th near-infrared image, and is the gray value corresponding to the th maximum pixel point in the preset neighborhood within the preset surrounding area corresponding to the th pixel point; is the th near-infrared image, and is the gray value corresponding to the target maximum pixel point in the preset neighborhood within the preset surrounding area corresponding to the th pixel point; the target maximum pixel point is the maximum pixel point in the preset neighborhood closest to the th maximum pixel point in the preset neighborhood; is the th near-infrared image, and is the average value of the gray values corresponding to all pixel points on the line connecting the th maximum pixel point and the target maximum pixel point in the preset neighborhood within the preset surrounding area corresponding to the th pixel point; is a factor greater than 0 set in advance; is the th near-infrared image, and is the distance between the th maximum pixel point and the target maximum pixel point in the preset neighborhood within the preset surrounding area corresponding to the th pixel point; Determine the postoperative pain factor corresponding to each pixel point in the near-infrared image according to the heating coefficient and the temperature fluctuation factor within the preset surrounding area corresponding to each pixel point in the near-infrared image; Determine the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the heating coefficient and the postoperative pain factor in the current near-infrared image, and the difference between the heating coefficient corresponding to each pixel point in the current near-infrared image and the heating coefficient of the pixel point at the same position in the previous preset number of frames of historical near-infrared images; Determine the target pain level corresponding to each pixel point in the current near-infrared image according to the pain occurrence factor and the postoperative pain factor corresponding to each pixel point in the current near-infrared image.

2. The method for assisting in identifying postoperative pain of patients based on near-infrared technology according to claim 1, wherein The step of determining the heating coefficient corresponding to each pixel point in the near-infrared image according to the gray value corresponding to each pixel point in the near-infrared image and the difference between its corresponding gray value and the gray value of the pixel point at the same position in the previous frame of the near-infrared image includes: Determine any pixel point in any frame of the near-infrared image as a marked pixel point, and screen out the pixel point with the same position as the marked pixel point from the previous frame of the near-infrared image to which the marked pixel point belongs as a reference pixel point; Normalize the difference between the gray value corresponding to the marked pixel point and the gray value corresponding to the reference pixel point to obtain the gray difference index corresponding to the marked pixel point; Normalize the product of the gray value corresponding to the marked pixel point and the gray difference index to obtain the heating coefficient corresponding to the marked pixel point.

3. The method for assisting in identifying the pain of a patient after anesthesia based on near-infrared technology according to claim 1, wherein The step of determining the postoperative pain factor corresponding to each pixel point in the near-infrared image according to the heating coefficient and the temperature fluctuation factor within the preset surrounding area corresponding to each pixel point in the near-infrared image includes: Determine the mean of the temperature fluctuation factors corresponding to all pixel points within the preset surrounding area corresponding to each pixel point as the temperature fluctuation representative index corresponding to each pixel point; Normalize the variance of the heating coefficients corresponding to all pixel points within the preset surrounding area corresponding to each pixel point to obtain the heating distribution factor corresponding to each pixel point; Normalize the absolute value of the difference between the temperature fluctuation representative index and the heating distribution factor corresponding to each pixel point to obtain the postoperative pain factor corresponding to each pixel point.

4. The method for assisting in identifying postoperative pain of patients based on near-infrared technology according to claim 1, wherein, The determining of the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the heating coefficient and the postoperative pain factor in the current near-infrared image, and the difference between the heating coefficient corresponding to each pixel point in the current near-infrared image and the heating coefficient corresponding to the pixel point at the same position in the previous preset number of frames of historical near-infrared images includes: Determine the overall heating coefficient corresponding to the current near-infrared image according to the heating coefficients and the postoperative pain factors corresponding to all pixel points in the current near-infrared image; Determine each frame of historical near-infrared image in the previous preset number of frames of historical near-infrared images of the current near-infrared image as a reference infrared image; Determine the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the overall heating coefficient, the gray value corresponding to each pixel point in the current near-infrared image and the gray value corresponding to the pixel point at the same position in the preset number of frames of reference infrared images, and the difference between the heating coefficient corresponding to each pixel point in the current near-infrared image and the heating coefficient corresponding to the pixel point at the same position in the preset number of frames of reference infrared images.

5. The method for assisting in identifying postoperative pain of patients based on near-infrared technology according to claim 4, wherein The formula for the overall heating coefficient corresponding to the current near-infrared image is: ; wherein, is the overall temperature increase coefficient corresponding to the current near-infrared image; is the hyperbolic tangent function; is the number of pixel points in each frame of the near-infrared image; is the serial number of the pixel points in each frame of the near-infrared image; is the th temperature increase coefficient corresponding to the pixel point in the current near-infrared image; is the natural exponential function; is the postoperative pain factor corresponding to the th pixel point in the current near-infrared image.

6. The method for assisting in identifying postoperative pain of patients based on near-infrared technology according to claim 4, wherein, The formula for the pain occurrence factor corresponding to the pixel point in the current near-infrared image is: ； ； ； ; wherein, is the pain occurrence factor corresponding to the -th pixel point in the current near-infrared image; is the serial number of the pixel point in each frame of near-infrared image; is the normalization function; is the cumulative value of the gray value corresponding to the -th pixel point in the current near-infrared image and the gray value corresponding to the -th pixel point in the preset number of frames of reference infrared images; is the overall temperature rise coefficient corresponding to the current near-infrared image; is the natural exponential function; characterizes the temperature change situation at the position where the -th pixel point is located; is the preset number; is the serial number of the reference infrared image; is the -th frame of reference infrared image, and is the difference between the temperature rise coefficient corresponding to the -th pixel point and the temperature rise coefficient corresponding to the pixel point at the same position in the subsequent frame of infrared image; is the -th frame of reference infrared image, and is the difference between the temperature rise coefficient corresponding to the -th pixel point and the temperature rise coefficient corresponding to the pixel point at the same position in the subsequent frame of infrared image; is the temperature rise coefficient corresponding to the -th frame of reference infrared image and the -th pixel point; is the temperature rise coefficient corresponding to the -th pixel point in the subsequent frame of infrared image after the -th frame of reference infrared image; is the temperature rise coefficient corresponding to the -th frame of reference infrared image and the -th pixel point; is the temperature rise coefficient corresponding to the -th pixel point in the subsequent frame of infrared image after the -th frame of reference infrared image.

7. A method for assisting in the identification of postoperative pain in patients based on near-infrared technology according to claim 1, characterized in that, The determining of the target pain level corresponding to each pixel point in the current near-infrared image according to the pain occurrence factor and the postoperative pain factor corresponding to each pixel point in the current near-infrared image includes: Normalize the product of the pain occurrence factor and the postoperative pain factor corresponding to each pixel point in the current near-infrared image to obtain the target pain index corresponding to each pixel point in the current near-infrared image; Determine the target pain level corresponding to each pixel point in the current near-infrared image according to the target pain index corresponding to each pixel point in the current near-infrared image.

8. A method for assisting in identifying postoperative pain of patients based on near-infrared technology according to claim 7, characterized in that, The determining of the target pain level corresponding to each pixel point in the current near-infrared image according to the target pain index corresponding to each pixel point in the current near-infrared image includes: If the target pain index corresponding to the pixel point is greater than the preset pain threshold, then determine the pixel point as a pain pixel point; If the pixel point is a pain pixel point and the proportion of pain pixel points within its corresponding preset neighborhood is greater than or equal to the preset proportion, then set the target pain level corresponding to the pixel point to the high pain level, otherwise set the target pain level corresponding to the pixel point to the low pain level.

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