A method for monitoring α and β human contamination by combining infrared imaging and particle counting

Abstract

This invention discloses a method for monitoring α and β human body contamination by combining infrared imaging and particle counting, belonging to the field of particle monitoring technology. It addresses the problem of reduced efficiency in human body radiation safety monitoring. The method involves scanning the detection area using an infrared imaging device to divide the human body surface area into an environmental area, extracting the temperature radiation state of the human body surface, and classifying physiological hot zones. An initial contamination weight is calculated based on the radiation characteristics of these hot zones, ensuring that particle counting analysis reflects the true heat distribution of the human body. During the detection process, the ambient temperature is monitored in real time, and a temperature benchmark threshold is constructed. A contamination correction coefficient is generated to correct the initial contamination weight. α and β particles are collected from each physiological hot zone using a particle counting device, and their numbers are statistically analyzed. The statistical results are combined with the initial contamination weights to analyze the contamination concentration characteristics of the physiological hot zones and generate human body contamination distribution results, thereby improving the efficiency of human body radiation safety monitoring in the field of medical protection.

Description

Technical Field

[0001] This invention relates to the field of particle monitoring technology, and more specifically, to a method for monitoring α and β human body contamination by combining infrared imaging and particle counting. Background Technology

[0002] With the widespread application of nuclear medicine, radiotherapy, and nuclear energy-related industries, the risk of human exposure to radioactive materials in nuclear radiation work environments has increased significantly. Traditional radioactive contamination detection mainly relies on contact detectors or surface contamination measuring instruments, which detect the radiation levels of alpha and beta particles by directly contacting the probe with the skin surface.

[0003] The existing technology has the following shortcomings:

[0004] Currently, traditional surface contamination meters require direct contact with human skin, which can easily cause secondary contamination. They are also unable to comprehensively detect complex shapes or uneven areas, and are easily affected by environmental factors such as air temperature, humidity, dust, and background radiation. Furthermore, they lack a coupling analysis mechanism for human thermal radiation characteristics and radioactive particle distribution, resulting in reduced efficiency in human radiation safety monitoring and an inability to accurately reflect the spatial distribution characteristics of contamination. Therefore, a method for monitoring α and β human contamination by combining infrared imaging and particle counting is proposed.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an α and β human body pollution monitoring method that combines infrared imaging and particle counting. This method solves the problems mentioned in the background art by employing infrared radiation feature modeling and particle counting weight adaptive fusion algorithm.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring α and β human body contamination by combining infrared imaging and particle counting, comprising the following steps:

[0008] Step S1: When identifying contamination in the human body to be tested in the detection area, infrared imaging data of the detection area is obtained using an infrared imaging device. Based on the infrared imaging data, the detection area is divided into the human body surface area and the environment area.

[0009] Step S2: Extract the temperature radiation state of the human body surface area and divide it into physiological thermal zones, set the initial pollution weight, detect the real-time temperature of the environmental area and set the temperature reference threshold, determine whether to enter the weight correction stage based on the temperature reference threshold and generate the pollution correction coefficient.

[0010] Step S3: In the weight correction stage, the real-time temperature of each physiological thermal zone is detected and abnormal physiological thermal zones are screened. The initial contamination weight of the abnormal physiological thermal zones is updated according to the contamination correction coefficient.

[0011] Step S4: After collecting α particles and β particles in each physiological hot zone using a particle counting device, the number of particles is counted. The pollution concentration characteristics of the physiological hot zone are analyzed by combining the statistical results with the initial pollution weight, and the human body pollution distribution results are generated based on the pollution concentration characteristics.

[0012] In a preferred embodiment, in step S1, the detection area is scanned using an infrared imaging device, and the radiation energy at each location in the detection area is projected onto the focal plane of the detector to output infrared imaging data containing the surface of the human body to be tested and its surrounding environment.

[0013] Infrared imaging data is a two-dimensional temperature radiation matrix, which is composed of individual pixels. The gray value corresponding to each pixel is the temperature radiation intensity.

[0014] All grayscale values ​​in the infrared imaging data are statistically analyzed, and an adaptive threshold segmentation method is used to calculate the grayscale threshold, dividing the detection area into the human body surface area and the environment area.

[0015] In a preferred embodiment, in step S2, after obtaining the human body surface area, the gray values ​​in the human body surface area are analyzed to calculate the temperature radiation state of the human body surface area, including the local gray mean and gray gradient values.

[0016] After standardizing the gray values ​​of all pixels within the human body surface area, the gray value coefficient is obtained. Taking each pixel as the center pixel, the neighboring pixels of the center pixel are selected, and the local gray value mean and gray value gradient are calculated based on the gray value.

[0017] In a preferred embodiment, in step S2, each pixel is categorized:

[0018] If the local grayscale mean is greater than the preset local grayscale threshold and the grayscale gradient value is greater than the preset gradient threshold, then the center pixel is marked as a hot pixel.

[0019] If the local grayscale mean is greater than the preset local grayscale threshold, and the grayscale gradient value is less than or equal to the preset gradient threshold, then the center pixel is marked as a stable hot zone pixel.

[0020] If the local grayscale mean is less than the preset local grayscale threshold and the grayscale gradient value is less than or equal to the preset gradient threshold, then the center pixel is marked as a cold area pixel.

[0021] Otherwise, the center pixel is not marked;

[0022] After classifying each pixel in the human body surface region, pixels with the same class of markings are aggregated based on the spatial connectivity of the pixels to form continuously distributed physiological thermal zones.

[0023] In a preferred embodiment, in step S2, the physiological hot zones of different categories of labeled pixels are statistically analyzed, and the average radiation value and average radiation change value are calculated based on the local gray-level mean and gray-level gradient value.

[0024] The initial contamination weights of each physiological thermozone were calculated using the average radiation value and the average radiation change value.

[0025] The real-time temperature of the environment is monitored by an infrared thermal imaging acquisition component, and the temperature reference threshold is obtained by combining the maximum and minimum values ​​of multiple consecutive monitoring results.

[0026] In a preferred embodiment, during step S2, when entering the particle counting and particle contamination identification phase, the real-time temperature of the environmental area is continuously read, and the real-time temperature is compared with a temperature reference threshold.

[0027] If the real-time temperature is within the temperature reference threshold, there is no need to correct the initial contamination weight.

[0028] Conversely, it enters the weight adjustment phase;

[0029] The average of the maximum and minimum values ​​in the temperature reference threshold is used as the midpoint of the temperature threshold. The pollution correction coefficient is calculated based on the real-time temperature and the midpoint of the temperature threshold.

[0030] In a preferred embodiment, in step S3, during the weight correction stage, a continuous thermal image of the human body surface area is acquired by an infrared imaging device.

[0031] The average radiation temperature of all pixels in the physiological region is calculated based on the radiation temperature of each pixel in the heat map, and this average temperature is used as the current average temperature of the physiological thermal zone.

[0032] The difference between the current average temperature of the physiological thermal zone and the midpoint of the temperature threshold is defined as the temperature change of the physiological thermal zone.

[0033] If the temperature change exceeds the preset abnormal temperature rise threshold, the physiological thermal zone is determined to be an abnormal physiological thermal zone.

[0034] Conversely, if the temperature change is less than or equal to the preset abnormal temperature rise threshold, the physiological thermal zone is determined to be a normal physiological thermal zone.

[0035] In a preferred embodiment, in step S3, all physiological regions that meet the condition that the temperature change is greater than a preset abnormal temperature rise threshold are screened to obtain abnormal physiological hot zones.

[0036] The pollution correction coefficient of the abnormal physiological region is multiplied by the initial pollution weight to obtain the updated initial pollution weight of the abnormal physiological region.

[0037] In a preferred embodiment, in step S4, a particle counting device is used to continuously detect the radioactive particle flow on the surface of the physiological hot zone, and the electrical signal generated by each particle impact is electronically counted to obtain the α particle count value and β particle count value of each physiological hot zone.

[0038] The particle count results of each physiological hot zone are combined with the initial contamination weight to obtain the contamination concentration characteristics of the physiological hot zone: ;

[0039] in, Characteristics of pollution concentration, As the initial pollution weight, This represents the alpha particle count. This represents the β particle count.

[0040] In a preferred embodiment, in step S4, the contamination concentration characteristics of each physiological hot zone are mapped using a regional indicator function to generate a radioactive contamination distribution map of the human body surface as the result of human body contamination distribution:

[0041] ;

[0042] in, This is a map showing the distribution of radioactive contamination. Characteristics of pollution concentration, These are the spatial coordinates of various points on the human body surface. The x-axis is... The vertical coordinate is... For the i-th physiological thermal zone, This is the index value of the physiological thermal zone. This represents the total number of physiological thermal zones. For the region indicator function, when point Located in the physiological heat zone At that time, ,otherwise .

[0043] The technical effects and advantages of this invention are as follows:

[0044] This invention uses an infrared imaging device to scan the detection area, dividing it into a human body surface area and an environmental area. It extracts the temperature radiation state of the human body surface and delineates physiological hot zones. Based on the radiation characteristics of these hot zones, an initial contamination weight is calculated to ensure that particle counting analysis conforms to the actual heat distribution of the human body. During the detection process, the ambient temperature is monitored in real time, and a temperature benchmark threshold is constructed. A contamination correction coefficient is generated to correct the initial contamination weight. Alpha and beta particles in each physiological hot zone are collected using a particle counting device, and their numbers are statistically analyzed. The statistical results are combined with the initial contamination weight to analyze the contamination concentration characteristics of the physiological hot zones and generate the human body contamination distribution results. This invention achieves non-contact dynamic detection of human radioactive contamination, possessing advantages such as environmental adaptability, rapid response, and high safety, thus improving the efficiency of human radiation safety monitoring in the field of medical protection. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the implementation of an α and β human body pollution monitoring method combining infrared imaging and particle counting according to the present invention.

[0046] Figure 2 This is a hierarchical system architecture diagram of an α and β human body pollution monitoring method that combines infrared imaging and particle counting according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] This invention uses an infrared imaging device to scan the detection area, dividing it into a human body surface area and an environmental area. It extracts the temperature radiation state of the human body surface and delineates physiological hot zones. Based on the radiation characteristics of these hot zones, an initial contamination weight is calculated to ensure that particle counting analysis conforms to the actual heat distribution of the human body. During the detection process, the ambient temperature is monitored in real time, and a temperature benchmark threshold is constructed. A contamination correction coefficient is generated to correct the initial contamination weight. Alpha and β particles in each physiological hot zone are collected using a particle counting device, and their numbers are statistically analyzed. The combined statistical results and the initial contamination weights are used to analyze the contamination concentration characteristics of the physiological hot zones and generate the human body contamination distribution results. This invention achieves non-contact dynamic detection of radioactive contamination in the human body, offering advantages such as environmental adaptability, rapid response, and high safety.

[0049] Example 1, as Figures 1 to 2 As shown, a method for monitoring α and β human contamination by combining infrared imaging and particle counting includes the following steps:

[0050] Step S1: When identifying contamination in the human body to be tested in the detection area, infrared imaging data of the detection area is obtained using an infrared imaging device. Based on the infrared imaging data, the detection area is divided into the human body surface area and the environment area.

[0051] Step S2: Extract the temperature radiation state of the human body surface area and divide it into physiological thermal zones, set the initial pollution weight, detect the real-time temperature of the environmental area and set the temperature reference threshold, determine whether to enter the weight correction stage based on the temperature reference threshold and generate the pollution correction coefficient.

[0052] Step S3: In the weight correction stage, the real-time temperature of each physiological thermal zone is detected and abnormal physiological thermal zones are screened. The initial contamination weight of the abnormal physiological thermal zones is updated according to the contamination correction coefficient.

[0053] Step S4: After collecting α particles and β particles in each physiological hot zone using a particle counting device, the number of particles is counted. The pollution concentration characteristics of the physiological hot zone are analyzed by combining the statistical results with the initial pollution weight, and the human body pollution distribution results are generated based on the pollution concentration characteristics.

[0054] The specific implementation is as follows:

[0055] In step S1, in a medical protection scenario, when identifying contamination in the human body to be tested in the detection area, an infrared imaging device is used to scan the detection area. The infrared imaging device projects the radiation energy at each location in the detection area onto the focal plane of the detector through an optical lens and a detector array, and outputs infrared imaging data containing the surface of the human body to be tested and its surrounding environment.

[0056] Infrared imaging data refers to a two-dimensional temperature radiation matrix formed by sampling the infrared radiation intensity of each pixel in the detection area. Each pixel in the two-dimensional temperature radiation matrix corresponds to a spatial location in the detection area, and its gray value reflects the temperature radiation intensity at that location.

[0057] Infrared imaging data reflects differences in thermal radiation and abnormal local temperature changes on the surface of human skin. Non-contact infrared thermal imaging equipment avoids cross-contamination that may be caused by physical contact with the human body being tested.

[0058] In this embodiment, because human skin has a high infrared emissivity, its grayscale value is higher than that of non-biological surfaces in the surrounding environment at the same wavelength. In infrared imaging data, the temperature radiation intensity is higher and continuously distributed, while the grayscale value in the environment is lower and discretely distributed. Utilizing the difference in grayscale value distribution, the detection area is divided into a human surface area and an environmental area.

[0059] All grayscale values ​​in the infrared imaging data are statistically analyzed, and an adaptive thresholding method is used to calculate the grayscale threshold. The specific process is as follows:

[0060] Different gray values ​​were randomly selected as comparison gray values, and the gray values ​​in the infrared imaging data were divided into two categories: pixels with lower gray values ​​were classified as environmental pixels, and pixels with higher gray values ​​were classified as human body surface pixels.

[0061] For each grayscale reference, the average grayscale values ​​of the environment pixels and the human body surface pixels are calculated as the average grayscale of the environment and the average grayscale of the human body, respectively.

[0062] When the grayscale reference maximizes the difference between the average grayscale of the environment and the average grayscale of the human body, the grayscale reference is used as the grayscale threshold.

[0063] The adaptive threshold segmentation method automatically adjusts the threshold under different ambient brightness and background conditions, making the segmentation of the human body surface area and the environment area more stable and reliable, and ensuring the adaptability and accuracy of the infrared imaging recognition process.

[0064] If the gray value is greater than the gray value threshold, the pixel is determined to be a human body surface pixel.

[0065] Conversely, the pixel is determined to be an environment pixel.

[0066] The human body surface pixels are spatially aggregated and combined into a human body surface region by a connected component aggregation algorithm. Morphological closing operations are then performed on the human body surface region to eliminate isolated pixels and generate a human body surface region with a smooth contour.

[0067] After obtaining the human body surface area, the remaining pixels that do not belong to the human body surface area are combined into the environment area based on the pixels in the infrared imaging data.

[0068] It should be explained that an infrared imaging device is an imaging device that performs non-contact temperature measurement based on the principle of thermal radiation. It consists of an infrared detector array, an optical imaging lens, a signal amplification and conversion circuit, and an image processing unit, and collects infrared imaging data from various locations within the detection area in real time. The adaptive threshold segmentation method is an image segmentation algorithm that automatically determines the classification threshold based on the image grayscale distribution characteristics. The connected component aggregation algorithm is a region recognition method based on the spatial adjacency relationship of pixels. It is used to merge pixels on the human body surface in infrared imaging data into the same connected region to form an overall contour corresponding to the human body. Morphological closing operation is an image processing method based on structuring elements. It is used to smooth the boundaries and fill holes in the human body surface region after region recognition.

[0069] This step utilizes infrared thermal imaging technology to achieve non-contact temperature zoning of the human body detection area. By combining regional temperature threshold zoning with connected component aggregation algorithms, it distinguishes the human body surface from the environmental background, improving the accuracy of contamination identification. The formation of the human body surface area and the environmental area is based on the temperature gradient changes in the infrared imaging data, which facilitates verification and traceability in medical scenarios. It also provides input for subsequent initial contamination weight calculation and contamination source location, thereby achieving safe and accurate detection results in human radioactive contamination monitoring.

[0070] In step S2, after obtaining the human body surface area, the gray values ​​in the human body surface area are analyzed to calculate the temperature radiation state of the human body surface area, including the local gray mean and gray gradient values. The temperature radiation state reflects the changes in the spatial distribution of infrared radiation intensity at each location in the human body surface area, reflecting the differences in radiation energy and local change trends of the skin surface of the tested human body.

[0071] After standardizing the gray values ​​of all pixels in the human body surface area, the gray coefficient is obtained. Taking each pixel as the center pixel, the local gray mean and gray gradient value are calculated by selecting the neighboring pixels of the center pixel.

[0072] The local grayscale mean is obtained by averaging the grayscale coefficients of the adjacent pixels of the center pixel, which reflects the level of temperature radiation intensity around the center pixel. The grayscale gradient value is obtained by averaging the absolute values ​​of all the differences between the center pixel and each adjacent pixel, which reflects the rate of change of temperature radiation intensity in space.

[0073] A larger grayscale gradient value indicates a significant difference in grayscale among the central pixels and a drastic change in temperature radiation energy; a smaller grayscale gradient value indicates a more uniform distribution of temperature radiation intensity among the central pixels and a more stable change in temperature radiation.

[0074] A preset local grayscale threshold and a preset gradient threshold are compared with the local grayscale mean and grayscale gradient value, respectively, to classify each pixel.

[0075] If the local grayscale mean is greater than the preset local grayscale threshold and the grayscale gradient value is greater than the preset gradient threshold, then the center pixel is marked as a hot pixel.

[0076] If the local grayscale mean is greater than the preset local grayscale threshold, and the grayscale gradient value is less than or equal to the preset gradient threshold, then the center pixel is marked as a stable hot zone pixel.

[0077] If the local grayscale mean is less than the preset local grayscale threshold and the grayscale gradient value is less than or equal to the preset gradient threshold, then the center pixel is marked as a cold area pixel.

[0078] Otherwise, the center pixel is not marked.

[0079] When the local grayscale mean is greater than the preset local grayscale threshold and the grayscale gradient value is less than or equal to the preset gradient threshold, it indicates that the temperature radiation intensity of the central pixel is high but the distribution is stable. It can be classified as a stable high-temperature hot zone, corresponding to the normal high-radiation part of the human body. When the local grayscale mean is greater than the preset local grayscale grading threshold and the grayscale gradient value is greater than the preset gradient grading threshold, the radiation energy level of the central pixel is high and the local changes are obvious, indicating the presence of radioactive particles attached.

[0080] After classifying each pixel in the human body surface area, pixels with the same class of markings are aggregated based on the spatial connectivity of the pixels to form a continuously distributed physiological thermal zone.

[0081] The connected component aggregation algorithm is used to merge adjacent pixels with the same category label into a physiological hot zone. Hot zone pixels are aggregated into the same physiological hot zone, stable hot zone pixels are aggregated into the same physiological hot zone, and cold zone pixels are aggregated into the same physiological hot zone.

[0082] By using a connected component aggregation algorithm to spatially aggregate pixels within a human body surface area, regional identification of the temperature radiation intensity of the human skin surface is achieved. This improves the spatial resolution of infrared detection and the ability to identify thermal anomalies, providing a safe thermal feature monitoring environment for the human body under test and providing a reliable basis for monitoring human radioactive pollution and assessing health risks.

[0083] The initial contamination weights of different physiological hot zones are calculated separately. Physiological hot zones aggregated by different categories of labeled pixels are statistically analyzed. The average local gray-level mean and gray-level gradient values ​​of each labeled pixel of the same category are averaged to obtain the average radiation value and average radiation change value of the corresponding physiological hot zone.

[0084] Calculate the initial contamination weight for each physiological thermozone based on the average radiation value and the average radiation change value: ,in, To preset the basic weights, and The preset adjustment coefficient, The average radiation value. This represents the average change in radiation. This represents the initial pollution weight.

[0085] It should be explained that the standardization processing methods include, but are not limited to, standard linear transformation based on interval scaling, Z-Score standardization based on statistics, or normalization based on nonlinear mapping functions. The application methods of standardization processing will not be elaborated here. The preset adjustment coefficient can be set according to the human body detection scenario and the monitoring sensitivity requirements, and the value range is from 0 to 1. The preset basic weight can be set according to the detection sensitivity requirements and the response characteristics of the equipment.

[0086] By setting initial contamination weights, particle counting results can better reflect the actual thermal distribution on the human body surface, thereby improving the accuracy and medical usability of human contamination detection.

[0087] In this embodiment, in order to avoid the interference of external ambient temperature fluctuations on the identification results of human radioactive contamination, after the human body surface area is divided, the real-time temperature of the environmental area is monitored by an infrared thermal imaging acquisition component, and the temperature reference threshold is obtained by combining the maximum and minimum values ​​of multiple consecutive monitoring results.

[0088] When entering the particle counting identification of particle pollution, the real-time temperature of the environmental area is continuously read and compared with the temperature reference threshold. When the real-time temperature is within the temperature reference threshold, it means that there is no significant thermal change in the external environment and there is no need to correct the initial pollution weight.

[0089] Conversely, it enters the weight adjustment phase;

[0090] The average of the maximum and minimum values ​​in the temperature baseline threshold is used as the midpoint of the temperature threshold. The pollution correction coefficient is then calculated based on the real-time temperature and the midpoint of the temperature threshold. ,in, For real-time temperature, The midpoint of the temperature threshold. As a preset correction factor, This is the pollution correction factor.

[0091] It should be explained that the preset correction factor can be set according to the temperature fluctuation characteristics of the detection area and the monitoring sensitivity requirements.

[0092] By calculating the pollution correction coefficient, the influence of environmental thermal disturbances is filtered out during the particle counting identification process, ensuring that the real local thermal effects caused by α and β particles are accurately identified.

[0093] It should be explained that the infrared thermal imaging acquisition component is a device that realizes the visual measurement of temperature field based on the principle of infrared radiation detection.

[0094] By extracting the temperature radiation state of the human body surface area, the heat distribution pattern and local energy change trend of the skin surface are reflected. Combined with real-time temperature monitoring of the environmental area, a temperature benchmark threshold is set to determine whether the detection environment is stable. When the environmental temperature deviates from the threshold, the initial contamination weight is corrected, so that the human body detection process has adaptive temperature compensation capability. It can effectively distinguish between the local thermal effect caused by the attachment of α and β particles and the overall radiation change caused by changes in external temperature, thereby improving the accuracy and environmental adaptability of radioactive contamination identification.

[0095] In step S3, during the weight correction phase, the temperature of each physiological thermal zone is monitored in real time to obtain the current average temperature of each physiological thermal zone.

[0096] Specifically, infrared imaging equipment is used to collect thermal images of the human body surface area. The average radiation temperature of all pixels in the physiological area is calculated based on the radiation temperature of each pixel in the thermal image of the human body surface area, and this average temperature is used as the current average temperature of the physiological thermal area.

[0097] Subsequently, the temperature change of each physiological hot zone is calculated, which is defined as the difference between the current average temperature of the physiological hot zone and the midpoint of the temperature threshold. The temperature change is used to determine whether the temperature rise of the physiological hot zone is an abnormal state. An abnormal state refers to the local temperature rise caused by the attachment of radioactive particles, rather than the thermal effect caused by the overall temperature fluctuation of the environment.

[0098] The temperature change is compared with a preset temperature rise anomaly threshold:

[0099] If the temperature change exceeds the preset abnormal temperature rise threshold, it indicates that the particle adhesion has caused a disturbance in the infrared radiation distribution, and the physiological thermal zone is determined to be an abnormal physiological thermal zone.

[0100] Conversely, if the temperature change is less than or equal to the preset abnormal temperature rise threshold, it indicates that the temperature rise in the physiological area originates from the overall temperature change in the environment, and the physiological hot zone is determined to be a normal physiological hot zone.

[0101] Abnormal physiological thermal zones are obtained by screening all physiological regions where the temperature change exceeds the preset abnormal temperature rise threshold.

[0102] It should be noted that the preset abnormal temperature rise threshold is used to distinguish the source of temperature rise in physiological hot zones, that is, to determine whether the temperature rise is caused by abnormal thermal effects due to the attachment of local radioactive particles or by normal thermal fluctuations caused by changes in the overall environmental temperature. This is set by statistically analyzing historical temperature data of the detection environment and the thermal distribution of normal human bodies. Specifically, under stable environmental temperature conditions, continuous infrared measurements are performed on each physiological hot zone of the standard human body model to obtain the temperature change distribution of each hot zone, and the mean and standard deviation of the temperature change of each hot zone are calculated. The abnormal temperature rise threshold is set to the mean plus a certain number of times the standard deviation (e.g., the mean plus two times the standard deviation) to ensure that the temperature rise is not misjudged under normal environmental conditions, while also being able to detect local temperature rises caused by the attachment of radioactive particles.

[0103] For abnormal physiological regions, their initial pollution weights are updated based on the pollution correction coefficient. Specifically, the pollution correction coefficient is multiplied by the initial pollution weight to obtain the updated initial pollution weights for the abnormal physiological regions.

[0104] By screening abnormal physiological hot zones and dynamically updating the initial contamination weights, local contamination caused by the attachment of α and β radioactive particles on the human body surface can be accurately identified under fluctuating environmental temperatures. This enables stable monitoring of the contamination status of the human body, thereby improving the reliability and accuracy of radioactive contamination identification and avoiding misjudgments caused by environmental factors.

[0105] In step S4, a particle counting device is used to collect radioactive particles from each physiological hot zone, including independent detection of alpha and beta particles. Specifically, the detection area of ​​the particle counting device is aligned with the pre-defined physiological hot zones to ensure that each physiological hot zone is within the effective sampling range. Subsequently, within a set sampling time interval, the radioactive particle flow on the surface of the physiological hot zone is continuously detected, and the number of alpha and beta particles falling on the detector is recorded in real time. During the acquisition process, the particle counting device performs electronic counting and time accumulation on the electrical signal generated by each particle impact to obtain the alpha particle count value and beta particle count value for each physiological hot zone.

[0106] It should be noted that a particle counting device is a detection device used to measure and record the number of radioactive particles. It consists of a particle detector, a signal amplification module, and an electronic counter. When an alpha or beta particle passes through or collides with the sensitive area of ​​the particle detector, the particle detector generates a charge pulse. The signal amplification module amplifies the charge pulse and transmits it to the electronic counter. The electronic counter counts and accumulates the pulses according to a preset sampling time to form a particle count value.

[0107] Subsequently, the particle count results of each physiological hot zone are combined with the initial pollution weight to obtain the pollution concentration characteristics of the physiological hot zone. The specific calculation formula is as follows:

[0108] ;

[0109] in, Characteristics of pollution concentration, As the initial pollution weight, This represents the alpha particle count. This represents the β particle count.

[0110] The higher the value of the pollution concentration characteristic, the greater the degree of radioactive pollution on the surface of the corresponding physiological hot zone, and vice versa.

[0111] Spatial mapping of contamination concentration characteristics in all physiological hot zones is performed. A radioactive contamination distribution map is generated based on the location and area of ​​each physiological hot zone on the human body surface, serving as the overall contamination distribution result. Specifically, a regional indicator function is used to map the contamination concentration characteristics of each physiological hot zone, as shown in the following expression:

[0112] ;

[0113] in, This is a map showing the distribution of radioactive contamination. Characteristics of pollution concentration, These are the spatial coordinates of various points on the human body surface. The x-axis is... The vertical coordinate is... For the i-th physiological thermal zone, This is the index value of the physiological thermal zone. This represents the total number of physiological thermal zones. For the region indicator function, when point Located in the physiological heat zone At that time, ,otherwise .

[0114] By mapping regional indicator functions, the radioactive contamination levels of various physiological regions on the human body surface can be reflected in two dimensions, clarifying the spatial distribution of each contaminated region. This can provide important auxiliary functions for monitoring radiopharmaceuticals, monitoring the side effects of radiotherapy, and differentiating between infection and inflammation.

[0115] By combining raw alpha and beta particle data collected by particle counting equipment with temperature correction weights, radioactive contamination on the human body surface can be accurately identified under different ambient temperature conditions. This enables the quantification and visualization of the human body's contamination status, protecting the human body from radiation damage and providing a reliable technical basis for subsequent health risk assessment.

[0116] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0117] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The 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, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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 wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0118] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0119] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0120] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0121] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0126] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the above specification.

Claims

1. A method of alpha, beta human contamination monitoring combining infrared imaging with particle counting, characterized by: The method comprises the following steps: Step S1: When identifying the pollution of the human body to be detected in the detection area, the infrared imaging device is used to obtain the infrared imaging data of the detection area, and the detection area is divided into a human body surface area and an environment area according to the infrared imaging data; Step S2: The temperature radiation state of the human body surface area is extracted and physiological heat zoning is performed, an initial pollution weight is set, the real-time temperature of the environment area is detected and a temperature reference threshold is set, whether to enter the weight correction stage is judged based on the temperature reference threshold, and a pollution correction coefficient is generated; In step S2, when entering the particle count to identify particle pollution, the real-time temperature of the environment area is continuously read, and the real-time temperature is compared with the temperature reference threshold: If the real-time temperature is within the temperature reference threshold, the initial pollution weight does not need to be corrected; Otherwise, the weight correction stage is entered; The average of the maximum and minimum values in the temperature reference threshold is taken as the temperature threshold midpoint, and the pollution correction coefficient is calculated based on the real-time temperature and the temperature threshold midpoint; Step S3: In the weight correction stage, the real-time temperature of each physiological heat zone is detected and an abnormal physiological heat zone is screened, and the initial pollution weight of the abnormal physiological heat zone is updated according to the pollution correction coefficient; In step S3, in the weight correction stage, the continuous heat map of the human body surface area is collected by the infrared imaging device; The average radiation temperature of all pixel points in the physiological area is calculated as the current average temperature of the physiological heat zone according to the radiation temperature of each pixel point in the heat map; The difference between the current average temperature of the physiological heat zone and the temperature threshold midpoint is defined as the temperature change of the physiological heat zone; If the temperature change is greater than a preset temperature rise abnormal threshold, the physiological heat zone is determined to be an abnormal physiological heat zone; Otherwise, if the temperature change is less than or equal to the preset temperature rise abnormal threshold, the physiological heat zone is determined to be a normal physiological heat zone; In step S3, all physiological areas that satisfy the temperature change greater than the preset temperature rise abnormal threshold are screened to obtain the abnormal physiological heat zone; The pollution correction coefficient and the initial pollution weight of the abnormal physiological area are multiplied to obtain the updated initial pollution weight of the abnormal physiological area; Step S4: The number of alpha particles and beta particles in each physiological heat zone is counted after being collected by the particle counting device, the pollution concentration characteristics of the physiological heat zone are analyzed based on the comprehensive statistical results and the initial pollution weight, and the human body pollution distribution result is generated based on the pollution concentration characteristics.

2. The alpha and beta human body pollution monitoring method combining infrared imaging and particle counting according to claim 1, characterized in that: In step S1, the infrared imaging device is used to scan the detection area, project the radiation energy of each position in the detection area onto the detector focal plane, and output infrared imaging data containing the surface of the human body to be detected and the surrounding environment; The infrared imaging data is a two-dimensional temperature radiation matrix composed of pixel points, and the gray value corresponding to each pixel point is the temperature radiation intensity; All gray values in the infrared imaging data are counted, an adaptive threshold segmentation method is used to calculate the gray threshold, and the detection area is divided into a human body surface area and an environment area. 3.The method of claim 2, wherein: in step S2, the temperature radiation state of the human body surface region is calculated based on the analysis of the gray values in the human body surface region, including local gray mean value and gray gradient value; and the gray values of all pixel points in the human body surface region are standardized to obtain a gray coefficient, and the local gray mean value and the gray gradient value are calculated based on the gray values of the neighboring pixel points selected with each pixel point as a center pixel. 4.The method of claim 3, wherein: in step S2, each pixel point is marked with a category: if the local gray mean value is greater than a preset local gray threshold value and the gray gradient value is greater than a preset gradient threshold value, the center pixel is marked as a hot zone pixel; if the local gray mean value is greater than the preset local gray threshold value and the gray gradient value is less than or equal to the preset gradient threshold value, the center pixel is marked as a stable hot zone pixel; if the local gray mean value is less than the preset local gray threshold value and the gray gradient value is less than or equal to the preset gradient threshold value, the center pixel is marked as a cold zone pixel; otherwise, the center pixel is not marked; and after the category of each pixel point in the human body surface region is marked, the same category of pixel points are aggregated based on the spatial connectivity of the pixel points to form a continuously distributed physiological hot zone. 5.The method of claim 4, wherein: in step S2, the physiological hot zones aggregated by the pixel points of different categories are counted, and the average radiation value and the average radiation change value are calculated based on the local gray mean value and the gray gradient value; the initial contamination weight of each physiological hot zone is calculated using the average radiation value and the average radiation change value; and the temperature reference threshold value is obtained by combining the maximum value and the minimum value of the temperature of the environment region monitored by the infrared thermal imaging acquisition assembly. 6.The method of claim 1, wherein: in step S4, the radioactive particle flow on the surface of the physiological hot zone is continuously detected by the particle counting device, and the electrical signal generated by each particle impact is counted to obtain the α particle count value and the β particle count value of each physiological hot zone. 7.The method of claim 6, wherein: in step S4, the pollution concentration characteristics of each physiological hot zone are mapped by a regional indicator function to generate a radioactive pollution distribution map of the human body surface as a human body pollution distribution result. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ The particle count results of each physiological hot zone are combined with the initial pollution weight to obtain the pollution concentration characteristics of the physiological hot zone: ; wherein, is a pollution concentration characteristic, is an initial pollution weight, is an alpha particle count value, is a beta particle count value. ​ ​ ； wherein, is a radioactivity contamination distribution map, is a contamination concentration profile, are spatial coordinates of points on the human body surface, is the horizontal coordinate, is the vertical coordinate, is the i-th physiological hot zone, is an index value of the physiological hot zone, is the total number of physiological hot zones, is an indicator function of the region, when the point is located in the physiological hot zone , , otherwise .

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