Adaptive Adjustment Method for Multi-site Measurement Parameters of Bluetooth Infrared Thermometer

Abstract

This application relates to the technical field of body temperature measurement and provides a method for adaptively adjusting multi-site measurement parameters of a Bluetooth infrared thermometer. The method includes: performing human key point detection and generating a measurement path; starting an infrared temperature measurement matrix with the measurement path to obtain body temperature path data; obtaining measurement environment information and performing adaptive compensation to obtain body temperature environment compensation data; constructing a target body temperature fusion model; synchronizing the compensation data to the fusion model to obtain real-time core body temperature; obtaining body temperature correlation data and combining it with the real-time core body temperature for prediction to obtain time-series temperature change information; storing the core body temperature and prediction information to generate a monitoring baseline. This application solves the technical problem that the measurement accuracy of existing infrared thermometers is greatly affected by the measurement environment and user individual differences, making it difficult to ensure the authenticity of body temperature measurement, and achieves the technical effect of improving the accuracy of body temperature measurement through the collaborative work of multiple Bluetooth infrared thermometers, combined with human key point detection and adaptive compensation.

Description

Technical Field

[0001] This application relates to the technical field of body temperature measurement, and particularly to a method for adaptively adjusting multi-site measurement parameters of a Bluetooth infrared thermometer. Background Art

[0002] In recent years, with the continuous growth of the demand for health monitoring, infrared thermometers have been widely used in medical and home health management due to their non-contact, fast, and convenient characteristics. However, most traditional infrared thermometers mainly rely on single-device and single-point measurement, with limited measurement ranges and a lack of intelligent adjustment capabilities, resulting in many deficiencies in actual use. In particular, the existing technology cannot achieve collaborative measurement between multiple devices, making it difficult to comprehensively obtain the body temperature data of multiple parts of a user in complex scenarios. With the development of Bluetooth communication technology, it provides new technical support for the collaborative measurement and data interaction of multiple infrared thermometers. Connecting multiple devices via Bluetooth can achieve efficient data transmission and synchronization, overcoming the limitations of single-device measurement. However, simply relying on Bluetooth communication to connect multiple devices is still not sufficient to solve all problems. Infrared temperature measurement devices are highly sensitive to environmental factors (such as temperature, humidity, wind speed, etc.), and traditional devices lack environmental adaptive compensation capabilities, resulting in poor consistency of measurement results in different scenarios. In addition, the physical characteristics of individual users (such as age, gender, skin characteristics) will significantly affect body temperature distribution, and existing thermometers cannot dynamically adjust measurement parameters according to individual characteristics, resulting in low accuracy and personalization of measurement results. Summary of the Invention

[0003] This application provides a method for adaptively adjusting multi-site measurement parameters of a Bluetooth infrared thermometer, aiming to solve the technical problem that the measurement accuracy of existing infrared thermometers is greatly affected by the measurement environment and individual differences of users, and it is difficult to ensure the authenticity of body temperature measurement.

[0004] In view of the above problems, this application provides a method for adaptively adjusting multi-site measurement parameters of a Bluetooth infrared thermometer.

[0005] The present application provides a method for adaptively adjusting multi-site measurement parameters of a Bluetooth infrared thermometer, and the method includes: detecting human key points of a target user, and generating a body temperature measurement path based on the detection result; starting an infrared temperature measurement matrix with the body temperature measurement path as a constraint to perform multi-site body temperature measurement on the target user to obtain distributed body temperature path data, where the infrared temperature measurement matrix is composed of M Bluetooth infrared thermometers; interacting to obtain measurement environment information, and performing adaptive environment compensation on the distributed body temperature path data according to the measurement environment information to obtain body temperature environment compensation data; calling associated data according to the user number of the target user, and constructing a target body temperature fusion model based on the call result; performing adaptive adjustment of the core body temperature by synchronizing the body temperature environment compensation data to the target body temperature fusion model to obtain the real-time core body temperature; interacting to obtain the body temperature associated data of the target user, and predicting the body temperature change according to the body temperature associated data and the real-time core body temperature to obtain time-series temperature change prediction information; integrating and storing the real-time core body temperature and the time-series temperature change prediction information to generate a long-term and short-term monitoring baseline of the target user.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] The above-mentioned method for adaptively adjusting multi-site measurement parameters of a Bluetooth infrared thermometer. For a target user, this method first performs human key point detection to accurately identify the user's main temperature measurement sites, and generates a body temperature measurement path based on the detection results. This path is used as a measurement constraint to activate an infrared temperature measurement matrix composed of M Bluetooth infrared thermometers to measure the body temperatures of multiple parts of the user, thereby obtaining distributed body temperature path data covering the whole body. This distributed measurement method effectively improves the comprehensiveness and efficiency of multi-site body temperature collection, and avoids the problem of one-sidedness of single-point temperature measurement data. Subsequently, measurement environment information is obtained through Bluetooth interaction, and the distributed body temperature path data is adaptively compensated for the environment in combination with this information to eliminate the interference of external factors such as environmental temperature and humidity on the temperature measurement results, thereby generating more accurate body temperature environment compensation data. This process ensures the stability and consistency of measurement data under different environmental conditions. After that, the associated data of the target user is called according to the user number of the target user, and a target body temperature fusion model is constructed based on this. By inputting the compensated body temperature data into this model, the adaptive adjustment of the core body temperature is realized, thereby obtaining the real-time core body temperature. This dynamic adjustment method can better fit the individual characteristics of the user and significantly improve the accuracy of core body temperature measurement. At the same time, the body temperature associated data of the user is obtained through interaction, and combined with the real-time core body temperature, the prediction of the body temperature change trend is carried out, and the time-series temperature change prediction information is output. This prediction ability can help users understand the body temperature change trend in advance. Finally, the real-time core body temperature and the time-series temperature change prediction information are integrated and stored to generate the long-term and short-term monitoring baseline of the user. This baseline provides a reliable data basis for the continuous monitoring of the user's health status and provides a scientific basis for the formulation of future health management plans. The whole method has achieved significant improvements in the comprehensiveness of multi-site measurement, the accuracy of data compensation, and the intelligence of core body temperature prediction, providing users with a more scientific and comprehensive health monitoring service.

[0008] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the specific embodiments of the present application. Brief Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. 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 according to these drawings.

[0010] Figure 1Schematic flowchart of the multi-site measurement parameter adaptive adjustment method for a Bluetooth infrared thermometer in an embodiment;

[0011] Figure 2 Schematic flowchart of generating a body temperature measurement path for the multi-site measurement parameter adaptive adjustment method of a Bluetooth infrared thermometer in an embodiment. Detailed implementation manners

[0012] In an embodiment of the present application, by providing a multi-site measurement parameter adaptive adjustment method for a Bluetooth infrared thermometer, the technical problem that the measurement accuracy of the existing infrared thermometer is greatly affected by the measurement environment and individual differences of users, and it is difficult to ensure the authenticity of body temperature measurement is solved.

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0014] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment, as Figure 1 shown, the present application provides a multi-site measurement parameter adaptive adjustment method for a Bluetooth infrared thermometer, and the method includes:

[0016] Detect the human key points of the target user, and generate a body temperature measurement path based on the detection result.

[0017] In the embodiments of the present application, the process of performing human key point detection on the target user mainly involves identifying specific key points on the user's body through an atlas or sensor data. For example, common body temperature measurement parts such as the forehead, armpit, and neck. The positioning of these key points can accurately reflect the body temperature distribution of different parts of the user. Based on these detected key points, a body temperature measurement path is analyzed and generated to ensure efficient coverage of each temperature measurement key point. The generation of this path is obtained by comprehensively considering the human body structure, the measurement range of the device, and the spatial layout of the Bluetooth infrared thermometer. Through the Bluetooth of the Bluetooth infrared thermometer, the detection and positioning data between multiple devices can be synchronized, and the path can also be updated to ensure the coherence and accuracy of the measurement process. Through this method of human key point detection and path generation, it can provide an efficient and accurate basis for subsequent multi-site body temperature measurement, while reducing the possibility of omission and repeated measurement, and improving the overall quality of body temperature data collection.

[0018] Further, as Figure 2 shown, the present application provides a method for performing human key point detection on a target user and generating a body temperature measurement path based on the detection results, including:

[0019] Accessing an associated database according to the user number of the target user for information collection to obtain user physical sign data, where the user physical sign data includes static physical sign data and dynamic physical sign data; extracting measurement part composition information from a temperature measurement part information database according to the static physical sign data; performing human key point detection on the target user with the measurement part composition information as a constraint to determine multiple actual measurement parts; and performing measurement trajectory fitting based on the multiple actual measurement parts and the spatial distribution of the infrared temperature measurement matrix, and outputting the body temperature measurement path.

[0020] Preferably, access the associated database through the user ID of the target user to extract the physical sign data related to the user. These physical sign data are divided into static physical sign data and dynamic physical sign data. The static physical sign data includes characteristics such as the user's gender, age, height, body type, etc., which do not change significantly over time. The dynamic physical sign data refers to the user's current activity status, heart rate, etc., which can reflect the dynamic characteristics of the user. Subsequently, according to the static physical sign data of the user, extract the measurement site composition information from the temperature measurement site information database. The temperature measurement site information database stores the typical measurement site distributions corresponding to different physical sign types. For example, children are preferably measured on the forehead and armpits, while adults may be more inclined to the forehead and wrist areas. By analyzing the static physical sign data of the user, the measurement site combination information suitable for the user can be screened out, providing a basis for subsequent measurement path planning. Then, with the measurement site composition information as a constraint, perform human key point detection on the target user. The key point detection collects multi-view images of the user through a camera or other sensors, and uses a measurement identification model to identify and label the actual measurement sites of the target user, such as the specific coordinates of areas such as the head, wrist, and neck. Multi-view data can improve the accuracy of key point detection, especially in cases where the user is occluded or has a special posture. Then, according to the detected actual measurement sites and the spatial distribution of the infrared temperature measurement matrix, perform temperature measurement scheduling simulation for multiple actual measurement sites on multiple alternative call device groups, and then complete the measurement trajectory fitting of multiple actual measurement sites and the infrared temperature measurement matrix through multi-level screening to generate an optimal body temperature measurement path, such as from the left ear canal to the right ear canal to the forehead to the armpit to the mouth. The trajectory fitting considers the scanning range of the device, the temperature measurement efficiency, and the key point distribution of the user to ensure that the path is comprehensively covered and the measurement process is efficient. During the measurement trajectory fitting process, the infrared temperature measurement device shares the measurement range and position information in real time through Bluetooth to ensure that the fitting result covers each measurement site and improves the efficiency of multi-device collaboration. This process realizes the intelligent planning of personalized body temperature measurement paths, improves the comprehensiveness and accuracy of measurement, and at the same time improves the measurement efficiency through device collaboration optimization.

[0021] Further, the present application provides extracting the measurement site composition information from the temperature measurement site information database according to the static physical sign data, including:

[0022] Interactively obtain multiple sample measurement distribution coordinates, where the multiple sample measurement distribution coordinates have multiple sample physical sign data identifiers; associatively store the multiple sample physical sign data and the multiple sample measurement distribution coordinates to complete the construction of the temperature measurement site information database; traverse and calculate the multiple physical sign similarities between the static physical sign data and the multiple sample physical sign data in the temperature measurement site information database; after serializing the multiple physical sign similarities, call the measurement site composition information from the multiple sample measurement distribution coordinates according to the sorting extreme values.

[0023] Optionally, a set of sample measurement distribution coordinates is obtained through a multi-sample data acquisition method. These coordinates represent the positions of body parts related to temperature measurement in different samples, such as the distribution information of common temperature measurement parts like ears, foreheads, armpits, etc. The acquisition of sample data depends on the collaborative work of Bluetooth infrared thermometers. These Bluetooth infrared thermometers achieve real-time data transmission through Bluetooth and synchronize the distribution coordinates of each temperature measurement part and the corresponding physical signs data (such as age, gender, height, weight) to the historical measurement database. Through Bluetooth communication, an interactive relationship with the historical measurement database can be quickly established, thereby obtaining multiple sample measurement distribution coordinates and their corresponding multiple sample physical signs data identifiers. Subsequently, the physical signs data (such as age, gender, height, weight) of each sample is associated and stored with the corresponding measurement distribution coordinate identifier to form a complete sample database, that is, a temperature measurement part information database. This temperature measurement part information database not only contains the spatial positions of various body measurement parts but also contains static information closely related to the individual characteristics of the samples. Then, based on the static physical signs data (such as age, gender, height, weight) of the target user, the sample physical signs data in the temperature measurement part information database is traversed one by one, and the similarity between the user's physical signs and the sample physical signs is calculated to obtain multiple physical signs similarities. The calculation of similarity can be based on the Euclidean distance or other similarity measurement methods. The higher the physical signs similarity, the higher the similarity degree in terms of body shape, etc. with the target user. Then, the calculated similarity results are sorted from high to low. By selecting the sample with the highest similarity, the corresponding measurement distribution coordinates are extracted. These coordinates usually represent the information on the composition of temperature measurement parts that best matches the characteristics of the target user. For example, if the physical signs data of the target user is close to the characteristics of a certain group of samples in the sample database (such as height 170 cm, age 30 years old, etc.), the coordinate information of the temperature measurement parts of this group of samples will be preferentially selected as the information on the composition of measurement parts. The extracted information on the composition of measurement parts is used for subsequent measurement path generation and temperature measurement operations, providing a scientific basis for the fitting of multi-part measurement paths.

[0024] Furthermore, the present application provides a method for performing human key point detection on the target user with the information on the composition of measurement parts as a constraint to determine multiple actual measurement parts, including:

[0025] Performing multi-view image acquisition on the target user to obtain K user images; performing region of interest bounding on the K user images with the information on the composition of measurement parts as a constraint to obtain K groups of measurement part images; inputting the K groups of measurement part images into a measurement identification model for automatic identification to obtain K groups of measurement part coordinate intervals; and performing coordinate unification on the K groups of measurement part coordinate intervals with the information on the composition of measurement parts as a constraint to obtain the multiple actual measurement parts.

[0026] Optionally, multiple image acquisition devices (such as cameras) are used to collect multi-view images of the target user, generating K user images. These devices communicate via Bluetooth, enabling the system terminal to obtain real-time images of the user taken from different angles and ensuring that the key temperature measurement parts (such as the forehead, armpit, ear, etc.) are comprehensively captured. The multi-view acquisition method improves the integrity and accuracy of the measurement part recognition, providing high-quality input for subsequent processing. Subsequently, constrained by the measurement part composition information, the measurement part bounding model automatically bounds and filters out the regions of interest (ROIs) related to temperature measurement in the K user images, and then these selected regions of interest are used as K groups of measurement part images, effectively eliminating the interference of irrelevant regions and ensuring a clear measurement target. Then, the K groups of measurement part images are input into the measurement identification model to identify and label the specific coordinate positions of the measurement parts (such as the center point of the ear or the boundary region of the forehead), generating K groups of measurement part coordinate intervals. For example, for the ear measurement part, the model can accurately identify the outer boundary of the auricle and its center point coordinates, providing high-precision position information. These recognition results are synchronously transmitted to ensure the rapid integration of all identification data, laying a foundation for subsequent operations. Then, based on the measurement part composition information, the K groups of measurement part coordinate intervals are unified. In this process, a unified coordinate reference system needs to be selected. The reference system can be defined based on the perspective of the user's frontal image or the space center point can be chosen as the origin. The purpose of this step is to provide a unified spatial reference for all measurement parts and eliminate the deviation caused by different perspectives. For each group of measurement part coordinate intervals, the perspective transformation algorithm is used to transform them into the unified coordinate reference system, thereby obtaining multiple actual measurement parts. For example, through a three-dimensional space mapping formula (such as a homogeneous coordinate transformation matrix), the coordinate direction of the side view is rotated and adjusted to align with the coordinate axes of the front view, and then translation correction is performed according to the distance and position offset between the side view device and the front view device, so that the measurement part coordinates can be accurately represented in the unified coordinate system, ensuring the consistency and accuracy of multi-view data. Through the above process, the actual measurement parts of the target user can be accurately identified, and the coordinate deviation caused by different perspectives is eliminated through multi-view data fusion and coordinate unification. This not only improves the accuracy and robustness of the measurement part recognition but also provides reliable data support for the subsequent multi-part body temperature measurement path planning.

[0027] Further, the present application provides a method for bounding the regions of interest of the K user images constrained by the measurement part composition information to obtain K groups of measurement part images, including:

[0028] Interactively obtain a set of K sample perspective images, and perform region-of-interest (ROI) bounding on the set of K sample perspective images according to standard measurement parts to obtain a set of K sample identification images; use the set of K sample perspective images and the set of K sample identification images to construct K measurement part bounding units; complete the construction of a measurement part bounding model by paralleling the K measurement part bounding units; automatically perform ROI bounding on the K user images by inputting them into the measurement part bounding model to obtain K groups of associated parts; and screen the K groups of measurement part images from the K groups of associated parts by taking the measurement part composition information as a constraint.

[0029] Optionally, through Bluetooth communication established with multiple image acquisition devices, the system terminal interacts with these image acquisition devices to obtain K sets of sample perspective images. These image sets cover the content of different samples taken from multiple perspectives, providing a diverse data basis for subsequent region of interest analysis. Then, according to the definition of standard measurement parts (such as forehead, ear, armpit, etc.), the K sets of sample perspective images are analyzed to frame the regions of interest related to measurement in each image, generating K sets of sample identification images. Subsequently, the K sets of sample perspective images and the corresponding sample identification images are divided into K training sets and K validation sets, and the constructed initial measurement part framing unit is trained using the training sets. After the training is completed, the validation sets are used for verification to evaluate the performance of the measurement part framing unit. Among them, the measurement part framing unit can be constructed based on a convolutional neural network (CNN). Taking CNN as an example, an initial measurement part framing unit structure is constructed, including an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer, etc. The weights of the framing unit are assigned using random initialization, and the sample perspective images and identification images in a training set are input into the initial measurement part framing unit. The training process includes the layer-by-layer extraction and analysis of image features. After the input layer receives the sample perspective images, the convolutional layer extracts the edge features, texture information, and geometric shapes of the regions of interest. Then, the pooling layer reduces the feature dimension to improve the calculation efficiency of the framing unit. In the fully connected layer, the framing unit comprehensively analyzes the extracted features and generates the predicted coordinates of the regions of interest in the output layer. By using a loss function (such as L1 or L2 loss) to calculate the difference between the predicted coordinates of the regions of interest by the framing unit and the real region coordinates in the sample identification images, and by using the backpropagation algorithm to calculate the gradients of the loss with respect to the weights of each layer layer by layer, and then using the Adam optimizer to update the weights of the framing unit, the parameters of the framing unit are gradually optimized. The above training process is repeated until the set maximum number of iterations is reached or the accuracy of the validation set meets the expectation. After the training is completed, the validation set is used to test the framing unit to evaluate the recognition accuracy of the framing unit for the regions of interest. For example, by verifying whether the framing unit can accurately frame the boundaries of target parts such as ears or foreheads. If the model performance meets the standard, the current framing unit is output as a measurement part framing unit. Otherwise, hyperparameters such as the learning rate and training batch are adjusted and retrained to further improve the framing effect. For the other training sets and validation sets, K - 1 measurement part framing units are constructed in the same way. These measurement part framing units are respectively used for framing the regions of interest in different perspective images to ensure the recognition efficiency and accuracy of the measurement parts. Then, the K measurement part framing units are connected in parallel to integrate the feature extraction capabilities under different perspectives and complete the construction of the measurement part framing model. This model has the ability to automatically frame the regions of interest in multi-perspective images and can achieve efficient localization in new input data.Then, K user images are input into the measurement part selection model, and the model automatically selects the region of interest for the measurement part in each image to generate K groups of associated parts. These associated parts include all candidate areas related to the measurement, ensuring comprehensive coverage without missing key parts. Finally, with the measurement part composition information as a constraint, the K groups of associated parts are screened, irrelevant or redundant areas are eliminated, and only the image areas of the parts involved in the measurement part composition information are retained, and finally K groups of measurement part images are generated. For example, if the measurement part composition information only includes the ears and forehead, the image areas corresponding to the ears and forehead will be screened out from the associated parts. Through the above process, the measurement part-related areas can be efficiently extracted from the multi-view sample images, and automatic processing can be achieved by constructing a selection model. This method not only improves the accuracy of measurement part identification, but also significantly reduces the need for manual intervention, providing high-quality data support for subsequent temperature measurement path planning and data fusion.

[0030] For the measurement identification model, the training method is the same as the above-mentioned training measurement part selection unit, the difference is that the training data used are sample measurement part images and sample measurement part coordinates, which will not be repeated here.

[0031] Furthermore, the present application provides a method for fitting a measurement trajectory according to the spatial distribution of the multiple actual measurement locations and the infrared temperature measurement matrix, and outputting the body temperature measurement path, including:

[0032] Interactively obtain M temperature measurement scanning ranges of the M Bluetooth infrared thermometers in the infrared temperature measurement matrix; arrange and combine the M Bluetooth infrared thermometers to obtain multiple alternative calling device groups, wherein the intersection of the temperature measurement scanning ranges of the multiple alternative calling device groups is an empty set; use the multiple alternative calling device groups to perform temperature measurement scheduling simulation for the multiple actual measurement parts to obtain multiple measurement time consumption information and multiple measurement coverage ratios; extract and obtain multiple alternative calling quantities based on the multiple alternative calling device groups; after performing a primary screening of the multiple alternative calling device groups according to the measurement coverage ratio, perform a secondary screening of the multiple alternative calling device groups according to the weighted calculation results of the multiple alternative calling quantities and the multiple measurement time consumption information to obtain a target calling device group; according to the M temperature measurement scanning ranges, perform measurement trajectory fitting for the multiple actual measurement parts and the target calling device group, and output the temperature measurement path.

[0033] Optionally, interact with M Bluetooth infrared thermometers in the infrared temperature measurement matrix via Bluetooth connection to obtain the temperature measurement scanning range of each thermometer. These ranges are usually described in coordinate form (such as the temperature measurement area in three-dimensional space) and are used for subsequent device combination and measurement path planning. Subsequently, perform permutations and combinations on the M Bluetooth infrared thermometers to generate multiple alternative call device groups. Each device group consists of several thermometers, and the temperature measurement scanning ranges of these devices do not overlap with each other (the intersection is an empty set). This design is to avoid repeated measurement of the same area and improve measurement efficiency. For example, if a device group contains two thermometers, their scanning ranges may cover the user's forehead and ear respectively, rather than acting on the same part simultaneously. Then, for each generated alternative call device group, perform temperature measurement scheduling simulation on multiple actual measurement parts. The simulation process includes verifying the startup sequence, measurement sequence, and coverage range of the device group, and outputting measurement time-consuming information and measurement coverage ratio. Among them, the measurement time-consuming information records the time required for each device group to complete all measurement tasks. The measurement coverage ratio records the percentage of the actual measurement parts covered by the device group to evaluate its coverage effect. In addition, multiple alternative call quantities are extracted from multiple alternative call device groups, that is, the number of devices required to be called by each device group, for subsequent screening. After obtaining the measurement time-consuming information and measurement coverage ratio, screen them according to the measurement coverage ratio of each device group, and eliminate the device groups with a coverage ratio lower than the preset threshold. For example, if a device group only covers 50% of the target part and the threshold is 75%, then this device group will be eliminated. For the device groups that pass the first-level screening, further perform comprehensive weighted calculation based on the measurement time-consuming information and alternative call quantity, and analyze the weighted results. Select the device group with the highest comprehensive score as the target call device group, thus completing the second-level screening of the alternative call device groups. In this process, weight distribution will be carried out according to the ratio of the measurement time-consuming to the standard time-consuming and the ratio of the call quantity to the standard call quantity. The device groups with shorter measurement time-consuming will be assigned higher weights because they have higher overall efficiency. The device groups with fewer call quantities will also be assigned higher weights because they have lower device usage complexity. Then, use the target call device group, combined with the temperature measurement scanning range of each device and the position of the actual measurement parts, to perform measurement trajectory fitting. The fitting process follows the principle of preferentially assigning the device closest to a part to that part, so as to generate an optimal path covering all actual measurement parts and ensure the maximization of measurement efficiency and coverage rate. During the assignment process, if the scanning range of a device overlaps with multiple measurement parts, the part closest to its coverage center point will be preferentially selected. Finally, according to the actual coverage range of the device, adjust the measurement sequence to reduce the number of device switches. After completing the matching between the device and the measurement part, use the actual measurement part as the key point of the path and connect them point by point according to the optimized measurement sequence, and finally output an optimal body temperature measurement path covering all measurement parts.Through the above trajectory fitting process, the generated measurement path can cover the target area to the greatest extent, while reducing the device movement distance and measurement time consumption, and improving the efficiency and accuracy of multi-site body temperature measurement.

[0034] Taking the body temperature measurement path as a constraint, start the infrared temperature measurement matrix to perform multi-site body temperature measurement of the target user, and obtain distributed body temperature path data, where the infrared temperature measurement matrix is composed of M Bluetooth infrared thermometers.

[0035] In one embodiment, the generated body temperature measurement path is used as a guiding constraint for the measurement process. The path defines multiple measurement sites of the target user and their priority order, ensuring the coherence and scientificity of multi-site body temperature measurement. For example, from the left ear canal to the right ear canal to the forehead to the armpit to the oral cavity. Subsequently, start the M Bluetooth infrared thermometers in the infrared temperature measurement matrix through Bluetooth communication. These Bluetooth infrared thermometers will be activated one by one according to the site order of the measurement path to ensure accurate temperature measurement of each Bluetooth infrared thermometer at its responsible measurement site. For example, the Bluetooth infrared thermometer responsible for the left ear canal will be started first, followed by the right ear canal, forehead, etc. Other Bluetooth infrared thermometers are in standby state during non-measurement periods to save energy consumption. Then, according to the measurement path, call each Bluetooth infrared thermometer in turn to measure the target site. For example, the first Bluetooth infrared thermometer measures the left ear canal, collects temperature data in real time, and uploads the data to the system terminal through Bluetooth. After completing the measurement of the left ear canal, activate the second Bluetooth infrared thermometer to measure the right ear canal. Then activate another Bluetooth infrared thermometer to measure the forehead site. Activate the relevant Bluetooth infrared thermometers in sequence according to the path order to complete the measurement of the remaining sites. After the temperature measurement is completed at each site, the Bluetooth infrared thermometer will upload the temperature data to the system terminal in real time through Bluetooth. By associating these data with the path information, a distributed body temperature path data set is formed. The data set not only records the temperature measurement values of each site, but also includes time stamps and Bluetooth infrared thermometer identification information, ensuring data traceability and integrity, and providing reliable data support for subsequent core body temperature analysis.

[0036] Interactively obtain measurement environment information, and perform adaptive environment compensation on the distributed body temperature path data according to the measurement environment information to obtain body temperature environment compensation data.

[0037] In one embodiment, interact with the environmental sensor device via Bluetooth to obtain the measurement environment information of the target user in real time, including wind speed, humidity, etc. These environmental data will serve as the core input for adaptive compensation. Subsequently, input these measurement environment information and the distributed body temperature path data into a plurality of pre-constructed body temperature deviation compensation functions to perform adaptive environmental compensation on the temperature of each measurement site, so as to solve the problem of body temperature deviation caused by sweating due to reasons such as wind speed and humidity. After that, output the compensated body temperature data in the same format as the distributed body temperature path data to form body temperature environment compensation data. The compensated body temperature data is closer to the user's true core body temperature, providing reliable data support for subsequent health analysis and decision-making.

[0038] Further, the present application provides for interacting to obtain the measurement environment information and performing adaptive environmental compensation on the distributed body temperature path data according to the measurement environment information to obtain body temperature environment compensation data, including:

[0039] Decompose the measurement site composition information to obtain a plurality of standard measurement sites; perform network data calls on the first standard measurement site to obtain a plurality of sample environment information, a plurality of sample measured body temperatures, and a plurality of sample compensated body temperatures; perform multi-source regression analysis on the plurality of sample environment information, the plurality of sample measured body temperatures, and the plurality of sample compensated body temperatures to obtain a plurality of body temperature deviation compensation functions; input the measurement environment information and the distributed body temperature path data into the plurality of body temperature deviation compensation functions to generate the body temperature environment compensation data.

[0040] Optionally, first decompose the measurement site composition information into multiple standard measurement sites, such as ears, forehead, armpits, etc. The definition of each standard measurement site includes its position characteristics and temperature measurement requirements, providing a clear processing target for subsequent data calling and analysis. For the decomposed first standard measurement site, call relevant sample data from the historical database through networking, including sample environmental information, sample measured body temperature, and sample compensated body temperature. These sample data provide a basis for analyzing body temperature deviation by correlating the body temperature values before and after compensation with the environmental information. Subsequently, combining the sample environmental information, sample measured body temperature, and sample compensated body temperature, use the multi-source regression analysis method to construct a functional relationship between the body temperature deviation of this site and environmental variables as a body temperature deviation compensation function. Specifically, first use the variables (wind speed, humidity, etc.) in the sample environmental information as independent variables, and the difference between the sample measured body temperature and the compensated body temperature (body temperature deviation) as the dependent variable to construct a multi-source regression model for processing the non-linear relationship between environmental variables and body temperature deviation. Then input the sample environmental information, sample measured body temperature, and sample compensated body temperature into this multi-source regression model. Through the same steps as the aforementioned training measurement site selection unit, gradually improve the parameters in the multi-source regression model through steps such as forward propagation, loss calculation, backpropagation, and parameter optimization until the loss value tends to be stable or reaches the maximum number of iterations. After completing the training of the multi-source regression model, extract the body temperature deviation compensation function corresponding to this site from the multi-source regression model. The compensation function reflects the influence relationship of environmental variables on body temperature deviation. Taking wind speed and humidity as examples, the body temperature deviation compensation function is specifically as follows: ; where is the body temperature deviation, representing the difference between the measured body temperature and the compensated body temperature. x is the wind speed, representing the air flow speed in the measurement environment. y is the humidity, representing the relative humidity level in the measurement environment. a is the quadratic term coefficient of the wind speed, reflecting the influence degree of the square of the wind speed on the body temperature deviation. b is the logarithmic relationship coefficient of the humidity on the body temperature deviation, reflecting the non-linear influence of humidity change on the body temperature deviation. c is the constant term, representing the fixed part of the basic body temperature deviation, usually related to the specific measurement device or scenario. After that, perform the same operation on other standard measurement sites to construct multiple body temperature deviation compensation functions. After obtaining multiple body temperature deviation compensation functions, input the actual measurement environment information of the target user and the distributed body temperature path data into the corresponding body temperature deviation compensation functions, calculate the body temperature compensation values of each measurement site one by one, and automatically correct the distributed body temperature path data according to this body temperature compensation value. The body temperature data after compensation is integrated into body temperature environment compensation data for subsequent core body temperature calculation and health analysis. The compensation data not only solves the interference problem of the measurement environment but also significantly improves the credibility and accuracy of the body temperature data.

[0041] Call associated data according to the user number of the target user, and construct a target body temperature fusion model based on the call result; obtain the real-time core body temperature by synchronizing the body temperature environment compensation data to the target body temperature fusion model for adaptive adjustment of the core body temperature.

[0042] In one embodiment, according to the user number of the target user, historical body temperature data and core body temperature data similar to the target user are called from the associated database. Based on these call results, a personalized body temperature fusion model is constructed. This model can provide targeted body temperature analysis capabilities by integrating the characteristics of body temperature data similar to the target user. Subsequently, the body temperature environment compensation data obtained by real-time measurement is synchronized to the constructed body temperature fusion model. The body temperature fusion model analyzes and fuses the measurement results of multiple parts, and finally outputs the real-time core body temperature of the target user. This core body temperature is closer to the real physiological temperature, can accurately reflect the current health status of the user, and provides a scientific basis for future health monitoring and diagnosis.

[0043] Furthermore, this application provides obtaining the real-time core body temperature by synchronizing the body temperature environment compensation data to the target body temperature fusion model for adaptive adjustment of the core body temperature, including

[0044] Call sample data with the dynamic sign data as the similarity metric to obtain multiple sample initial body temperature data and multiple sample core body temperatures; preprocess the multiple sample initial body temperature data according to the measurement site composition information to obtain multiple sample distributed body temperatures; use the multiple sample distributed body temperatures and multiple sample core body temperatures to construct the target body temperature fusion model; synchronize the body temperature environment compensation data to the target body temperature fusion model for adaptive adjustment of the core body temperature to obtain the real-time core body temperature.

[0045] Optionally, according to the dynamic physical sign data of the target user (such as heart rate, blood oxygen level, current activity status, etc.), similar historical sample data is called from the associated database. The calling process calculates the similarity between the dynamic physical sign data of the target user and the sample dynamic physical sign data in the associated database through methods such as Euclidean distance and cosine similarity, and filters out the samples that meet the similarity threshold as the initial body temperature data and core body temperature of multiple samples. Subsequently, the initial body temperature data of the samples is preprocessed using the measurement site composition information, that is, the obvious outliers caused by environmental interference in the sample data are first removed to ensure data quality. After the removal is completed, the data is classified and aligned according to the measurement site. For example, the temperature measurement value range of the ears is unified. Then, the initial body temperature data after classification and alignment is distributed according to the site to form the sample distributed body temperature characteristics for model construction. After that, the distributed body temperature and core body temperature data of multiple samples are input into the target body temperature fusion model for training. This model can be constructed based on methods such as neural network regression and random forest regression. The specific construction process is similar to the foregoing and is still carried out through steps such as forward propagation, loss calculation, backpropagation, and parameter optimization. After the training is completed, the body temperature environment compensation data measured in real time by the target user is synchronized to the target body temperature fusion model, and the model fuses the body temperature environment compensation data according to the learned mapping relationship to generate the real-time core body temperature. This process effectively improves the scientificity and personalization level of body temperature measurement and provides more reliable health data support for users.

[0046] Interactively obtain the body temperature-related data of the target user, and predict the body temperature change based on the body temperature-related data and the real-time core body temperature to obtain the time-series temperature change prediction information; integrally store the real-time core body temperature and the time-series temperature change prediction information to generate the long-term and short-term monitoring baseline of the target user.

[0047] In one embodiment, it interacts with a user device or a health data management platform via Bluetooth connection to obtain temperature-related data of a target user, including dynamic physiological indicators (such as physiological parameters related to temperature changes, such as heart rate, blood pressure, etc.) and environmental variables (such as room temperature, humidity). These temperature-related data provide key inputs for predicting temperature changes. Subsequently, these data and the real-time core body temperature are input into a temperature change prediction model constructed by means such as LSTM (Long Short-Term Memory Network) and ARIMA (Autoregressive Integrated Moving Average Model). The construction method of this temperature change prediction model is the same as the foregoing. The temperature change prediction model analyzes the relationship between the user's real-time core body temperature and the temperature-related data, and generates short-term predictions (such as the next 1 hour) and long-term predictions (such as the next 1 day) of temperature changes in combination with the mapping relationship learned from sample data, and outputs the short-term prediction and the long-term prediction together to form time-series temperature change prediction information. Then, the time-series temperature change prediction information is integrated with the real-time core body temperature and stored to generate a long-term and short-term monitoring baseline for the target user. This long-term and short-term monitoring baseline is divided into a short-term baseline and a long-term baseline. The short-term baseline is a set of the real-time core body temperature and the short-term prediction, which is used for immediate health status assessment. The long-term baseline is a set of the real-time core body temperature and the long-term prediction, which is used for long-term health trend analysis. These are stored in a structured form, including key information such as timestamps, core body temperature, predicted body temperature, etc., which is convenient for subsequent query and analysis.

[0048] In summary, the embodiments of the present application at least have the following technical effects:

[0049] The embodiments of the present application propose a method for adaptively adjusting multi-site measurement parameters of a Bluetooth infrared thermometer, covering the entire process from generating a user temperature measurement path to calculating the core body temperature and time-series prediction. The method generates a measurement path through human key point detection, uses a temperature measurement matrix composed of M Bluetooth infrared thermometers to complete multi-site measurement, and performs adaptive compensation in combination with measurement environment information to generate accurate temperature environment compensation data. At the same time, based on the user number and associated data, a target body temperature fusion model is constructed, and the real-time core body temperature is accurately calculated through dynamic adjustment, and the temperature change trend is further predicted. This method involves accurate identification of measurement sites, path fitting, environmental compensation, construction of a body temperature fusion model, and data storage and prediction, providing an efficient solution for long-term and short-term body temperature monitoring of users. These technical effects together solve the technical problem that the measurement accuracy of existing infrared thermometers is greatly affected by the measurement environment and user individual differences, and it is difficult to ensure the authenticity of body temperature measurement, and achieve the technical effect of improving the accuracy of body temperature measurement through the collaborative work of multiple Bluetooth infrared thermometers, combined with human key point detection and adaptive compensation.

[0050] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification has been made. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0052] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. Adaptive adjustment method for multi-site measurement parameters of a Bluetooth infrared thermometer, characterized in that The method includes: Performing human key point detection on a target user and generating a body temperature measurement path based on the detection result; Starting an infrared temperature measurement matrix with the body temperature measurement path as a constraint to perform multi-site body temperature measurement on the target user, and obtaining distributed body temperature path data, where the infrared temperature measurement matrix is composed of M Bluetooth infrared thermometers; Interactively obtaining measurement environment information and performing adaptive environment compensation on the distributed body temperature path data according to the measurement environment information to obtain body temperature environment compensation data; Invoking associated data according to the user number of the target user and constructing a target body temperature fusion model based on the invocation result; Performing adaptive adjustment of the core body temperature by synchronizing the body temperature environment compensation data to the target body temperature fusion model to obtain the real-time core body temperature; Interactively obtaining the body temperature associated data of the target user and predicting the body temperature change according to the body temperature associated data and the real-time core body temperature to obtain time-series temperature change prediction information; Integrating and storing the real-time core body temperature and the time-series temperature change prediction information to generate the long-term and short-term monitoring baseline of the target user; The performing human key point detection on a target user and generating a body temperature measurement path based on the detection result includes: Accessing an associated database according to the user number of the target user for information collection to obtain user physical sign data, where the user physical sign data includes static physical sign data and dynamic physical sign data; Extracting measurement site composition information from a temperature measurement site information database according to the static physical sign data; Performing human key point detection on the target user with the measurement site composition information as a constraint to determine multiple actual measurement sites; Interactively obtaining the M temperature measurement scanning ranges of the M Bluetooth infrared thermometers in the infrared temperature measurement matrix; Performing permutation and combination on the M Bluetooth infrared thermometers to obtain multiple alternative call device groups, where the intersection of the temperature measurement scanning ranges of the multiple alternative call device groups is an empty set; Performing temperature measurement scheduling simulation on the multiple actual measurement sites by using the multiple alternative call device groups to obtain multiple measurement time-consuming information and multiple measurement coverage ratios; Extracting multiple alternative call quantities based on the multiple alternative call device groups; After performing a primary screening on the multiple alternative call device groups according to the measurement coverage ratio, performing a secondary screening on the multiple alternative call device groups according to the weighted calculation result of the multiple alternative call quantities and the multiple measurement time-consuming information to obtain a target call device group; Performing measurement trajectory fitting on the multiple actual measurement sites and the target call device group according to the M temperature measurement scanning ranges, and outputting the body temperature measurement path.

2. The multi-site measurement parameter adaptive adjustment method of the Bluetooth infrared thermometer according to claim 1, characterized in that The interactively obtaining measurement environment information and performing adaptive environment compensation on the distributed body temperature path data according to the measurement environment information to obtain body temperature environment compensation data includes: Decomposing the measurement site composition information to obtain multiple standard measurement sites; Performing online data invocation on the first standard measurement site to obtain multiple sample environment information, multiple sample measured body temperatures, and multiple sample compensation body temperatures; Perform multi-source regression analysis on the multiple sample environmental information, multiple sample measured body temperatures, and multiple sample compensated body temperatures to obtain multiple body temperature deviation compensation functions; Input the measurement environmental information and distributed body temperature path data into the multiple body temperature deviation compensation functions to generate the body temperature environmental compensation data.

3. The multi-site measurement parameter adaptive adjustment method of the Bluetooth infrared thermometer according to claim 1, wherein, The core body temperature is adaptively adjusted by synchronizing the body temperature environmental compensation data to the target body temperature fusion model to obtain the real-time core body temperature, including: Call sample data with the dynamic sign data as the similarity metric to obtain multiple sample initial body temperature data and multiple sample core body temperatures; Preprocess the multiple sample initial body temperature data according to the measurement site composition information to obtain multiple sample distributed body temperatures; Construct the target body temperature fusion model using the multiple sample distributed body temperatures and multiple sample core body temperatures; Synchronize the body temperature environmental compensation data to the target body temperature fusion model for core body temperature adaptive adjustment to obtain the real-time core body temperature.

4. The multi-site measurement parameter adaptive adjustment method of the Bluetooth infrared thermometer according to claim 1, characterized in that, Perform human key point detection on the target user with the measurement site composition information as a constraint to determine multiple actual measurement sites, including: Collect multi-view images of the target user to obtain K user images; Select the regions of interest of the K user images with the measurement site composition information as a constraint to obtain K sets of measurement site images; Input the K sets of measurement site images into a measurement identification model for automatic identification to obtain K sets of measurement site coordinate intervals; Unify the coordinates of the K sets of measurement site coordinate intervals with the measurement site composition information as a constraint to obtain the multiple actual measurement sites.

5. The multi-site measurement parameter adaptive adjustment method of the Bluetooth infrared thermometer according to claim 4, characterized in that, The step of selecting the regions of interest of the K user images with the measurement site composition information as a constraint to obtain K sets of measurement site images includes: Interactively obtain K sample view image sets, and select the regions of interest of the K sample view image sets according to the standard measurement sites to obtain K sample identification image sets; Construct K measurement site selection units using the K sample view image sets and K sample identification image sets; Complete the construction of the measurement site selection model by paralleling the K measurement site selection units; Automatically select the regions of interest of the K user images by inputting them into the measurement site selection model to obtain K sets of associated sites; Select the K sets of measurement site images from the K sets of associated sites with the measurement site composition information as a constraint.

6. The multi-site measurement parameter adaptive adjustment method of the Bluetooth infrared thermometer according to claim 1, characterized in that The step of extracting the measurement site composition information from the temperature measurement site information database according to the static sign data includes: Interactively obtain multiple sample measurement distribution coordinates, where the multiple sample measurement distribution coordinates have multiple sample sign data identifiers; Associate and store the multiple sample sign data and multiple sample measurement distribution coordinates to complete the construction of the temperature measurement site information database; Traverse and calculate multiple sign similarities between the static sign data and multiple sample sign data in the temperature measurement site information database; After serializing the multiple sign similarities, call the measurement site composition information from the multiple sample measurement distribution coordinates according to the sorting extreme values.

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