Multifunctional heating system for blood transfusion and transfusion and detachable heating blanket module

Abstract

The invention discloses a multifunctional heating system for blood transfusion and transfusion and detachable heating blanket modules, and belongs to the field of heating blankets, the multifunctional heating system comprises a plurality of heating blanket modules, and each heating blanket module comprises a standardized detachable interface, a module identification unit, a heating assembly, a turning-over identification unit, a self-disinfection unit and a heat storage unit; wherein the module identification unit is used for providing identification information, power parameters and permission levels and supporting construction of a permission hierarchical control model; the heat storage unit comprises a high heat capacity or phase change material, and is switched into a slow release mode to output heat during power failure or communication interruption; by introducing an infrared thermogram and a turning-over recognition unit, dynamic recognition and accurate temperature rise of a local low-temperature area caused by body position changes of a patient are achieved, and the problems that a traditional temperature rise system is lagged in response and the temperature area cannot be self-adapted are solved.

Description

Technical Field

[0001] This invention relates to the field of warming blanket technology, and more specifically, to a multifunctional warming system for blood transfusion and infusion and a detachable warming blanket module. Background Technology

[0002] Maintaining stable body temperature is crucial for surgical safety and recovery during clinical blood transfusion and infusion. Traditional extracorporeal fluid warming methods mainly include water bath heaters, electric heating belts, and radiant heating devices. Most of these devices use centralized control, rely on manual adjustment, and lack real-time feedback on the patient's condition, resulting in problems such as response delay and low temperature control accuracy.

[0003] Although some heating blanket systems have incorporated image monitoring, wireless communication, or modular component designs, many technical challenges remain, such as:

[0004] Existing equipment often lacks the ability to identify areas, making it difficult to automatically identify and dynamically adjust local temperature zones based on changes in the patient's position. The temperature response often lags behind physiological needs.

[0005] Furthermore, most systems lack a complete temperature prediction mechanism and hierarchical access control mechanism, which makes them prone to problems such as control conflicts, uneven energy distribution, and accidental operation in scenarios involving multiple modules and multiple users.

[0006] Furthermore, traditional heating devices generally lack the ability to maintain temperature in the event of a power outage or communication interruption, which can easily lead to drastic fluctuations in the patient's body temperature, affecting the continuity and safety of treatment.

[0007] Therefore, in order to solve the above problems, we propose a multifunctional warming system and a detachable warming blanket module for blood transfusion and infusion. Summary of the Invention

[0008] To address the problems mentioned in the background section, the present invention provides the following technical solution:

[0009] A multifunctional warming system for blood transfusion and intravenous infusion includes multiple warming blanket modules, each of which includes:

[0010] Standardized detachable interface, module identification unit, heating component, turning identification unit, self-disinfection unit, and heat storage unit;

[0011] The module identification unit is used to provide identification information, power parameters and permission levels, and supports the construction of a permission hierarchical control model.

[0012] The thermal storage unit includes high heat capacity or phase change material, and switches to slow release mode to output heat when power is lost or communication is interrupted.

[0013] Also includes:

[0014] Infrared thermal imaging unit is used to acquire images of the patient's body surface temperature and generate a thermal image;

[0015] The AI ​​temperature control prediction unit is used to predict the target temperature trend and output the temperature adjustment curve based on thermograms, turning signals, patient parameters and historical data.

[0016] The main controller is used to establish a Bluetooth Mesh connection with the heating blanket module, build a scheduling model, output temperature adjustment commands based on prediction results, and realize feedforward temperature control response.

[0017] The main controller further includes:

[0018] The historical temperature control trend analysis module is used to extract the rate and duration of temperature change in each region based on continuous infrared thermal image data, and generate corresponding heating priority parameters.

[0019] The overheating detection unit is used to determine whether there is an overheating risk exceeding the safety threshold during the heating process of the target area, based on the heating rate and the current body temperature change rate.

[0020] The thermal anomaly compensation module is used to perform thermal interpolation and abnormal data compensation operations when spatially isolated areas or areas with sudden temperature changes are detected in the infrared thermal image, and to link adjacent modules for auxiliary power control.

[0021] Furthermore, after receiving the turning signal from the turning recognition unit, the main controller combines the body surface temperature image acquired by the infrared thermal imaging unit to identify the local low temperature area caused by the change in body position and update the temperature adjustment target area.

[0022] The main controller calls the AI ​​temperature control prediction unit to generate a thermal regulation prediction curve based on the target area, the patient's current physiological parameters, historical response data, and turning sequence characteristics, and outputs a control instruction set including target power, heating rate, response time, and module priority.

[0023] The main controller further dynamically adjusts the response frequency and output strategy based on the correlation between the turning signal and the changes in the thermogram, so as to achieve priority temperature adjustment and precise temperature rise control in local areas.

[0024] Furthermore, the main controller has a module operation status monitoring function. When it detects that a heating blanket module has communication interruption, abnormal temperature control response, or fault information, it automatically marks it as a degraded state and adjusts the control strategy based on module permissions and spatial distribution.

[0025] Based on the location of the faulty module, the main controller instructs adjacent modules to adjust their power output or response time to form a local thermal compensation area.

[0026] The AI ​​temperature control prediction unit introduces a boundary compensation factor to correct the prediction model boundary and maintain the continuity and stability of temperature control.

[0027] Furthermore, the main controller further includes a historical evolution weight model, which generates temperature control weight factors based on the temperature change rate and duration of each region in the continuous infrared thermogram using a weighted average function, and dynamically adjusts the temperature rise control priority of each region based on the weights.

[0028] The main controller also includes an over-temperature risk prediction mechanism. This mechanism makes predictions based on the slope of the target area's temperature rise curve, the current temperature change trend, and historical response delay values. When the predicted temperature exceeds a set threshold within a set time window, it executes control strategies including power reduction, rate limiting, and output pause.

[0029] The main controller also includes an abnormal temperature zone identification and reconstruction mechanism, which is used to identify spatially isolated or temperature difference abruptly changed areas in the infrared thermal image, trigger image interpolation and response trend fitting algorithms to complete the missing temperature image, and link the AI ​​temperature control prediction unit to coordinate the output of adjacent modules to achieve temperature compensation and continuous heating control in local areas.

[0030] Furthermore, the main controller includes a feedback control mechanism, which, after the heating task is completed, triggers parameter adjustment of the prediction model and power control strategy correction based on the deviation between the measured temperature and the prediction result, so as to improve temperature control accuracy and response stability.

[0031] The main controller also includes a redundancy coordination mechanism. When multiple module abnormalities or regional temperature fluctuation risks are detected, power coordination scheduling is performed to realize multi-module joint load reduction control based on module capacity, distribution location and priority, thereby reducing local thermal shock.

[0032] The main controller further includes a constant temperature maintenance mechanism, which is used to enter a low-power pulse mode after the temperature in the target area stabilizes, and dynamically adjust the pulse period and duty cycle; preferably, it also includes a strategy evolution module, which is used to switch control strategy templates when predicting and correcting failures, to adapt to diverse clinical temperature control scenarios.

[0033] Furthermore, the main controller further includes a parallel control and load balancing mechanism, which is used to dynamically allocate heating tasks and coordinate power output based on module response delay, power load and control priority when multiple heating modules are running simultaneously, so as to realize task sharing and heat load balancing among modules.

[0034] Furthermore, the main controller includes a hierarchical access control mechanism and a data linkage control mechanism, which includes:

[0035] The access control unit is used for permission verification and function restriction of temperature adjustment operations based on module identification information and user permission level. It supports hierarchical setting of doctor permissions, nursing permissions and maintenance permissions.

[0036] The external data interface unit is used to receive real-time monitoring data from devices such as electrocardiogram monitors and pulse oximeters, and to dynamically adjust control parameters in conjunction with the temperature control model.

[0037] The mechanism also includes a docking unit with the hospital information management system, which is used to read basic patient information and recommend temperature control templates and initialize parameters.

[0038] A detachable heating blanket module, the heating blanket module comprising:

[0039] Standardized detachable interface, module identification unit, heating component, turning identification unit, self-disinfection unit, and heat storage unit, among which:

[0040] The module identification unit includes a storage chip and a communication protocol adapter, which are used to provide module identification code, power level information and permission parameters when connected to the main controller, so as to support the system to build a permission hierarchical control strategy model;

[0041] The turning recognition unit includes a flexible pressure sensor array and a triaxial accelerometer, which are used to monitor changes in the patient's position in real time and send the turning signal to the main controller so that the system can identify the area of ​​position change and update the temperature control strategy.

[0042] The self-disinfection unit includes a far-infrared disinfection film and a time-temperature dual trigger controller, which is used to automatically start the high-temperature surface disinfection process after the module is removed from the patient or after the heating task is completed.

[0043] The thermal storage unit uses a high heat capacity material with phase change temperature control function, which can automatically enter the slow release heating mode to maintain the temperature stability of the local temperature zone in the event of a power outage or communication interruption.

[0044] The module is also preferably equipped with a temperature overshoot protection device, which is used to ensure patient safety by limiting current and reducing power or disconnecting the heat source in the event of abnormal heating or feedback failure.

[0045] Furthermore, after receiving the temperature control command sent by the main controller, the module executes the following operation procedure:

[0046] The module identification unit reads the identification code, power level, and access information and sends it back to the main controller;

[0047] The heating components start the heating process according to the target power and response time issued by the main controller scheduling model;

[0048] The rolling-over recognition unit monitors changes in body position in real time and provides feedback on rolling-over signals;

[0049] The self-disinfection unit automatically starts high-temperature surface disinfection when the heating task is completed or when the connection is disconnected.

[0050] The thermal storage unit switches to phase change slow release mode to maintain regional thermal stability in the event of a power outage or communication failure.

[0051] The temperature overshoot protection device monitors the heat output process in real time. When an abnormality is detected, it automatically reduces power or cuts off the heating components to ensure patient safety.

[0052] In summary, the present invention has the following beneficial effects:

[0053] By introducing infrared thermography and a turning recognition unit, the system can dynamically identify and accurately raise the temperature of local low-temperature areas caused by changes in the patient's position, overcoming the problems of slow response and inability to adapt the temperature zone in traditional heating systems.

[0054] Based on the main controller, a multi-module scheduling model and thermal load balancing mechanism are built, which can realize task allocation and power coordination when multiple heating modules run in parallel, thereby improving the stability and temperature control efficiency of the system.

[0055] By combining hierarchical access control, external monitoring data linkage, and power-off slow-release heating design, the system enhances safety and continuity in multiple roles and states in clinical settings, and improves overall intelligent temperature control capabilities. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the overall architecture of the multifunctional heating system of the present invention;

[0058] Figure 2 This is a flowchart of the dynamic temperature control process of the present invention;

[0059] Figure 3 This is a diagram illustrating the fault handling mechanism of the present invention;

[0060] Figure 4 This is a hierarchy control diagram for the present invention. Detailed Implementation

[0061] 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.

[0062] Example:

[0063] The following is in conjunction with the appendix Figure 1-4 The present invention will be described in further detail below.

[0064] Please see Figure 1-4 This invention provides a technical solution: a multifunctional warming system for blood transfusion and infusion, comprising multiple warming blanket modules, each warming blanket module comprising:

[0065] Standardized detachable interface, module identification unit, heating component, turning identification unit, self-disinfection unit, and heat storage unit;

[0066] The module identification unit is used to provide identification information, power parameters and permission levels, and supports the construction of a permission-based hierarchical control model.

[0067] The thermal storage unit includes high heat capacity or phase change materials, which switch to slow release mode to output heat when power is lost or communication is interrupted;

[0068] Also includes:

[0069] An infrared thermal imaging unit is used to collect the temperature distribution on the patient's body surface without contact and generate a two-dimensional thermal image.

[0070] The AI ​​temperature control prediction unit, based on thermograms, turning signals, patient baseline parameters, and historical temperature rise response data, uses neural network models, such as LSTM or TCN, to predict future body surface temperature trends and generate temperature control curves.

[0071] The main controller communicates with all heating modules via a Bluetooth Mesh network, builds a scheduling task model, and outputs temperature control commands based on prediction results to achieve feedforward heating regulation.

[0072] The main controller further includes:

[0073] The historical temperature control trend analysis module is used to extract the rate and duration of temperature change in each region based on continuous infrared thermal image data, and generate corresponding heating priority parameters.

[0074] Specifically, this involves extracting feature information such as the temperature rise slope and duration of each region from continuous heat map data to form a temperature control priority matrix.

[0075] The overheating detection unit is used to determine whether there is an overheating risk exceeding the safety threshold during the heating process of the target area, based on the heating rate and the current body temperature change rate.

[0076] During the actual heating process, the current heating rate and the patient's body temperature change rate are calculated and judged in real time. Once the set threshold is exceeded, the power is immediately reduced or the heating is stopped.

[0077] The heat map anomaly compensation module is used to reconstruct the heat map by performing bilateral interpolation and trend fitting algorithms when isolated areas or areas with drastic temperature changes are detected in the heat map, and to link adjacent modules to perform regional compensation heating operations to ensure thermal field uniformity and temperature control continuity.

[0078] In this embodiment: a standardized detachable interface enables plug-and-play connection with the main controller, facilitating rapid module replacement and flexible deployment; the module identification unit includes a storage chip and a communication protocol adapter, uploading module ID, power level, and control permission information during system initialization, allowing the main controller to build a hierarchical control model; the heating component can dynamically adjust its power according to the main controller's instructions, achieving stable and precise local heating control; the turning recognition unit uses a flexible pressure array and a triaxial accelerometer to monitor changes in patient position in real time; the self-disinfection unit consists of a far-infrared heating film and a time / temperature control trigger, ensuring automatic high-temperature sterilization when the module is not in use; and the heat storage unit incorporates high heat capacity or phase change materials, automatically switching to a slow-release heat state when the system is powered off or communication is interrupted, effectively ensuring the thermal stability of the local temperature zone.

[0079] This design enables the local heating area to be automatically adjusted according to changes in the patient's position, improving the real-time and personalized nature of temperature control. It also supports identification, power outage heat storage, hierarchical access control, and self-disinfection functions at the module level, enhancing system reliability and safety.

[0080] Through the AI ​​temperature control prediction mechanism and feedforward control logic, this system can also overcome the delay problem of traditional feedback control and achieve higher temperature control accuracy.

[0081] Furthermore, by setting up heatmap anomaly compensation and weight priority strategies, the system can maintain stable temperature output and control response even in complex clinical environments.

[0082] like Figure 1-4 As shown, after receiving the turning signal from the turning recognition unit, the main controller calls the infrared thermal imaging unit to obtain the updated body surface temperature image, and based on the thermal image difference information before and after the change in body position, identifies the local area that is currently in a state of temperature drop, and automatically updates the target control area of ​​the heating task.

[0083] The main controller further calls the AI ​​temperature control prediction unit, taking the infrared thermal image data of the target area, the patient's current physiological parameters, including body temperature, blood pressure or heart rate, the time of turning over, and historical temperature control response data as input features, executes the prediction algorithm to generate a thermal regulation trend curve, and generates a temperature control instruction group including target power value, heating rate, response duration and module priority based on the curve.

[0084] The main controller also adjusts the control cycle and power output strategy based on the correlation between the turning signal and the rate of change of the target area's thermal map, so as to achieve priority heating and dynamic temperature control response of the local area after turning over.

[0085] In this embodiment: the main controller receives the turning signal sent by the turning recognition unit and works in conjunction with the infrared thermal imaging unit to achieve intelligent recognition and response regulation of local body temperature changes, specifically as follows:

[0086] When a patient changes position while using the warming system, the turning recognition unit distributed inside the warming blanket module collects changes in the patient's pressure distribution and acceleration signals through a flexible pressure sensor array and a triaxial accelerometer, and sends the judgment result to the main controller in the form of a turning signal. Once the main controller receives the turning signal, it triggers the infrared thermal imaging unit to acquire an image of the current heat distribution on the patient's body surface;

[0087] Based on this, the main controller performs differential processing on the newly acquired thermal images and the previous thermal images to identify localized low-temperature areas caused by changes in body position. For example, if a patient changes from a supine to a lateral position, the previously heated areas are exposed to the air, causing a drop in temperature. Such areas are quickly identified by the thermal image change algorithm.

[0088] Subsequently, the main controller calls the AI ​​temperature control prediction unit, inputting the coordinates of the currently identified local target area and the patient's vital signs parameters, such as weight, body surface area, historical temperature regulation response time series, and turning sequence characteristics, to generate a thermal regulation prediction function, defined as follows:

[0089] (#1)

[0090] in, This is the predicted target power for temperature regulation per unit time output by the system, in watts. The number of currently identified local low-temperature target regions, which is a positive integer; The index number of the current region among all regions; For the first The regional temperature rise priority weight, with a value range of 0-1, can be calculated through the historical temperature control trend analysis module; For the first The rate of temperature change of the region at the current moment, in degrees Celsius per second, is obtained by differential analysis of the infrared thermogram sequence. This represents the temperature difference in the area before and after the patient is turned over, expressed in degrees Celsius, and is used to reflect the degree of abrupt change in a local area. For the first The regional temperature regulation index weight controls the strength of the suppression of the temperature difference on the prediction results. The value range is usually 0.1-1.5, which is set by the training data. For the patient in the first Heart rate values ​​at the time of regional detection, in beats per minute, collected from the electrocardiogram monitoring equipment; The patient’s baseline average heart rate during the warming process, in beats per minute, is used to normalize the effect of heart rate fluctuations on predicted power. For the first The angular difference in body position corresponding to the region, in degrees, is calculated by a triaxial accelerometer. For the first Regional temperature rise response delay time, in seconds, is the actual control delay time during the historical temperature rise response process; To minimize the non-zero stability constant and prevent the denominator from being zero, it is usually set to a value of [value missing]. ;

[0091] The overall output value range of this formula is usually between 0 and 200 watts. Less than 50W indicates a slight local temperature rise adjustment, 50-150W indicates a normal temperature rise response, and more than 150W indicates that the system needs to adjust the temperature quickly to compensate for the thermal imbalance caused by local rapid cooling or turning over.

[0092] After the prediction is completed, the main controller outputs a set of temperature control instructions based on the predicted temperature trend curve. The set of instructions includes, but is not limited to: target heating power value, heating rate limit value, preset response time and target module priority sorting, which are used to control the power distribution and response scheduling of each heating blanket module.

[0093] To enhance dynamic adaptability, the main controller also dynamically assesses the urgency and accuracy of the heating response based on the time correlation between the overturning signal and the evolution of the heat map, and optimizes the control frequency and output strategy.

[0094] For example, in scenarios involving continuous turning or monitoring of high-risk patients, the system can improve response frequency and shorten the closed-loop cycle of prediction and execution to achieve the goal of prioritizing heating and precise temperature control in key areas.

[0095] The method described in this embodiment can effectively solve the problems of slow response to changes in body position and untimely compensation for local low temperature in traditional heating systems, and realize a personalized, intelligent, and closed-loop precise heating control strategy, which significantly improves the safety and comfort of temperature management during surgery or blood transfusion and infusion for critically ill patients.

[0096] like Figure 1-4 As shown, the main controller has a module operation status monitoring function. When it detects that a heating blanket module has communication interruption, abnormal temperature control response, or fault information, it will automatically mark it as a degraded state and adjust the control strategy based on module permissions and spatial distribution.

[0097] Based on the location of the faulty module, the main controller instructs adjacent modules to adjust their power output or response time to form a local thermal compensation area.

[0098] The AI ​​temperature control prediction unit introduces a boundary compensation factor to correct the prediction model boundary and maintain the continuity and stability of temperature control.

[0099] In this embodiment, the main controller integrates a module operation status monitoring mechanism. This mechanism continuously acquires module operation status parameters through the Bluetooth Mesh communication protocol established between the main controller and each heating blanket module, including but not limited to module power response delay, deviation between the current heating temperature and the target temperature, module communication stability and periodic feedback data.

[0100] When the main controller detects any of the following abnormal events in any heating blanket module: such as continuous feedback loss exceeding the set time threshold, temperature control response delay significantly exceeding the standard, or actively reporting fault information, such as abnormal temperature control feedback, over-temperature protection triggering, power circuit disconnection, etc., the module will be automatically marked as "degraded state" and removed from the current heating task scheduling, and will enter the diagnostic or maintenance state.

[0101] After a module is downgraded, the main controller will call a backup heating module or an adjacent module to implement a power compensation strategy based on the module's spatial distribution location, the importance level of the hot zone in the historical heat map, and the module's permission level.

[0102] The compensation strategy includes automatically adjusting the upper limit of the output power of adjacent modules, extending their response time, or increasing their control priority to form a "local thermal compensation area" to ensure the continuity and stability of the heating area.

[0103] In addition, to avoid abnormal prediction model boundaries due to degraded state, the AI ​​temperature control prediction unit in this embodiment introduces a boundary compensation factor, which is dynamically adjusted according to the power coverage range, heat conduction efficiency and historical temperature rise response characteristics of neighboring modules.

[0104] The boundary compensation mechanism uses a high-order interpolation function to correct the prediction boundary of the degraded region, thereby ensuring the continuity of the AI ​​prediction model within the key control area and avoiding the occurrence of thermal regulation blind spots.

[0105] Through the coordinated operation of the above mechanisms, the system can maintain the overall temperature control capability of the heating system in the event of module failure, effectively improving the system's robustness, local recovery capability, and fault-tolerant stability of the heating control.

[0106] like Figure 1-4 As shown, the main controller further includes a historical evolution weight model. This model is based on the temperature change rate and duration of each region in the continuous infrared thermogram. It uses a weighted average function to generate temperature control weight factors and dynamically adjusts the temperature rise control priority of each region based on the weights.

[0107] The main controller also includes an over-temperature risk prediction mechanism. This mechanism makes predictions based on the slope of the target area's temperature rise curve, the current temperature change trend, and historical response delay values. When the predicted temperature exceeds the set threshold within the set time window, it executes control strategies including power reduction, rate limiting, and output suspension.

[0108] The main controller also includes an abnormal temperature zone identification and reconstruction mechanism, which is used to identify spatially isolated or temperature difference abrupt regions in the infrared thermal image, trigger image interpolation and response trend fitting algorithms to complete the missing temperature map, and link the AI ​​temperature control prediction unit to coordinate the output of adjacent modules to achieve temperature compensation and continuous heating control in local areas.

[0109] In this embodiment, the main controller further includes a historical evolution weight model, an overheating risk prediction mechanism, and an abnormal temperature zone identification and reconstruction mechanism. These three components work together to construct a full-cycle dynamic control system for warming tasks, wherein:

[0110] The main controller continuously acquires thermal image data of the patient's body surface through the infrared thermal imaging unit, and extracts the temperature change rate and duration parameters of each region based on image processing algorithms. It then calls the historical evolution weight model for priority evaluation. Specifically, the main controller calculates the dynamic response control function for each control region. Its expression is as follows:

[0111] (#2)

[0112] in, It is the comprehensive control priority index of the control area, which is the output of the main controller to determine the comprehensive response of the current temperature control area. It is the horizontal coordinate index in the heatmap, which is the horizontal axis pixel index of the two-dimensional body surface heatmap; This is the current temperature control time point, representing the corresponding region at that time. Temperature data collected; It is the exponential decay coefficient of the temperature response, used to measure the rate at which the impact of temperature changes on historical moments decreases. It represents the historical backtracking period, which is the time variable in the integration operation; is the i-th discrete time, representing five representative historical nodes sampled in the prediction interval; It is the thermal abrupt change intensity function in the thermal image, which represents the degree of high temperature abrupt change detected at the corresponding time ξ in the infrared thermal image; It is the response delay function, which is the system's response time at time t. The delay experienced in obtaining temperature control feedback; It is a function of the module's actual power output intensity, representing the heating block's performance at... , The module control index indicates the thermal power level at any given time; It is a heating rate prediction function, which is the predicted output of the current target area temperature rise rate; This is the moment of rate estimation, serving as the reference time point for calculating the rate curve; It is a boundary temperature difference anomaly function, representing the degree of temperature discontinuity at the boundary of an abnormal region; This serves as the boundary coordinate index and the location identifier for the boundary detection point. It is the thermal interference function of adjacent modules, representing the degree of influence of the neighboring block conducted at this location; This is a module spatial proximity index, specifically the distance attenuation factor between adjacent modules; The historical control state evaluation function is specifically the control weight factor obtained by the main controller based on the feedback of the heating response in the historical infrared thermogram. It serves as a time-series index for historical regulation and evolution, and is a sequence number variable within the historical state window;

[0113] Theoretically, the function can be solved based on the infrared images collected by the main controller, module status feedback, and historical operating data, and its value range is the range of positive real numbers. This indicates that the current area control response is weak. This indicates that the regulation is moderate, ≥ This indicates that the temperature control pressure is too high, and a power adjustment strategy needs to be implemented.

[0114] Slightly predicted value When the set upper limit is exceeded, the main controller will automatically trigger the over-temperature risk prediction mechanism. By analyzing the slope of the temperature rise curve of the target area, the current temperature change trend and historical response delay, it will determine whether there is a risk of temperature control being ahead of schedule and issue strategies in advance, including power reduction, rate limiting or output suspension.

[0115] To improve the accuracy of infrared image recognition in key areas, the main controller also integrates an abnormal temperature zone recognition and reconstruction mechanism;

[0116] When a spatially isolated region or abrupt temperature change is detected in the heat map, the mechanism will trigger the image interpolation algorithm and response trend fitting operation to reconstruct the missing or abnormal temperature map, and coordinate the power output of adjacent modules with the AI ​​temperature control prediction unit to maintain the continuity and smoothness of the overall heating task.

[0117] By introducing the aforementioned historical evolution weight model and embedded prediction function, not only is the dynamic adjustment capability of response priority in local areas enhanced, but the system also possesses an adaptive heating control strategy with feedforward early warning and closed-loop compensation.

[0118] like Figure 1-4 As shown, the main controller includes a feedback control mechanism, which, after the heating task is completed, triggers parameter adjustment of the prediction model and power control strategy correction based on the deviation between the measured temperature and the prediction result, so as to improve temperature control accuracy and response stability.

[0119] The main controller also includes a redundancy coordination mechanism. When multiple module anomalies or regional temperature fluctuation risks are detected, power coordination scheduling is performed. Based on module capacity, distribution location and priority, multi-module joint load reduction control is realized to reduce local thermal shock.

[0120] The main controller further includes a constant temperature maintenance mechanism, which is used to enter a low-power pulse mode after the temperature in the target area stabilizes, and dynamically adjust the pulse period and duty cycle; preferably, it also includes a strategy evolution module, which is used to switch control strategy templates when predicting correction failures, to adapt to diverse clinical temperature control scenarios.

[0121] In this embodiment, firstly, after the heating task is completed, that is, when the monitoring area reaches the target temperature range, the main controller starts the feedback control mechanism.

[0122] This mechanism collects actual temperature data provided by the infrared thermal imaging unit and compares it in real time with the predicted temperature value output by the AI ​​temperature control prediction unit. It calculates the prediction deviation. If the deviation exceeds the set error tolerance threshold, the main controller will automatically adjust the key parameters in the prediction model, including the input weight coefficient and the temperature control response time constant, and fine-tune the power output curve, such as reducing the power rise slope or shortening the heating duration, thereby improving the accuracy of the prediction model and the stability of the temperature control response.

[0123] Secondly, when the system detects that multiple heating blanket modules are operating abnormally at the same time, such as communication interruption, feedback abnormality or temperature control response lag, the main controller enables the redundancy coordination mechanism.

[0124] Based on the remaining capacity, spatial distribution coordinates and control priorities of each module, this mechanism uses a power coordination scheduling strategy to jointly reduce the load on available modules while meeting the heating demand of hot zones.

[0125] For example, by reducing peak power and extending the heating cycle, the risk of localized thermal shock can be mitigated, and burns caused by excessive heat concentration can be avoided.

[0126] Furthermore, in order to maintain the long-term stability of the target area after reaching the set temperature, the main controller further activates the constant temperature maintenance mechanism, which adopts a low-power pulse control method to dynamically adjust the pulse period and duty cycle.

[0127] For example, under normal circumstances, the main controller intermittently outputs low-power heat with a 30-second cycle and a 30% duty cycle to maintain the thermal balance of the local temperature zone; if a drastic fluctuation in ambient temperature is detected, the controller will respond by shortening the cycle or adjusting the duty cycle to avoid local temperature overshoot or undercooling.

[0128] Finally, if the feedback control mechanism fails to correct multiple times during system operation, or the prediction model continuously exceeds the standard deviation, the main controller will call the strategy evolution module to switch to the preset temperature control strategy template based on the current patient status and scenario type, such as preoperative warming, intraoperative warming, and postoperative rewarming, such as switching to steady-state maintenance mode, rapid temperature adjustment mode, or low temperature monitoring mode, so as to achieve adaptive updating of the system control strategy.

[0129] The above solution effectively enhances the temperature control accuracy and temperature zone stability of the system, meeting the high requirements for precise temperature management during blood transfusion and infusion in clinical practice. It also makes this solution significantly superior to existing single heating logic or fixed strategy systems.

[0130] like Figure 1-4 As shown, the main controller further includes a parallel control and load balancing mechanism, which is used to dynamically allocate heating tasks and coordinate power output based on module response delay, power load and control priority when multiple heating modules are running at the same time, so as to realize task sharing and heat load balancing among modules.

[0131] In this embodiment, the main controller continuously collects the status information of each heating module through the Bluetooth Mesh communication protocol, including but not limited to the current power output value, temperature control response delay, module operation stability, control priority corresponding to the clinical location, and other indicators.

[0132] The system constructs a scheduling model based on these status parameters, and when it receives multiple heating requests, it divides the overall task into multiple subtasks and prioritizes them for modules with fast response, low load, and high priority, so as to avoid local modules from causing abnormal temperature control due to overload.

[0133] During the heating process, if the main controller detects that a module's actual response is lagging, the power load is too high, or the temperature control in a local area is uneven, it will immediately reduce the task intensity of that module and automatically increase the output power of adjacent modules or extend their heating time, thereby forming heat compensation in the local area and ensuring the balance and continuity of temperature distribution.

[0134] In addition, the mechanism also combines historical feedback and prediction results to dynamically adjust the task allocation strategy;

[0135] For example, in scenarios involving large-area blood transfusions or simultaneous heating of multiple sites during surgery, the system can identify areas with high task density and assign them to modules that are spatially distributed and have moderate load capacity, thereby avoiding the superposition of thermal interference between modules and improving the coordination of system operation.

[0136] The main controller further links this mechanism with the AI ​​temperature control prediction module and the feedback control module to build a closed-loop path of prediction, scheduling, execution and feedback. After each round of tasks is completed, the main controller adjusts the allocation logic of subsequent tasks based on the actual temperature feedback, gradually optimizes the load matching between modules, and improves the accuracy of temperature control response and the overall stability of the system.

[0137] Through the above implementation methods, the parallel control and load balancing mechanism can effectively cope with complex clinical temperature control environments such as simultaneous operation of multiple modules, local abnormal fluctuations, and heat compensation requirements, significantly enhancing the temperature control flexibility, safety, and intelligence level of the heating system in multi-task concurrent scenarios.

[0138] like Figure 1-4 As shown, the main controller includes a hierarchical access control mechanism and a data linkage control mechanism, which includes:

[0139] The access control unit is used for permission verification and function restriction of temperature adjustment operations based on module identification information and user permission level. It supports hierarchical setting of doctor permissions, nursing permissions and maintenance permissions.

[0140] The external data interface unit is used to receive real-time monitoring data from devices such as electrocardiogram monitors and pulse oximeters, and to dynamically adjust control parameters in conjunction with the temperature control model.

[0141] The mechanism also includes a unit for interface with the hospital information management system, which is used to read basic patient information and recommend temperature control templates and initialize parameters;

[0142] In this embodiment, the permission control unit has built-in permission level recognition logic, which is used to automatically verify the user's identity information and permission level when the operator sends a temperature adjustment command through the main control interface, such as a mobile terminal or medical panel.

[0143] Specifically, based on the module ID information provided by the module identification unit, the main controller matches it with the user permission parameters to determine whether the user has the corresponding functional permissions.

[0144] For example, when a doctor has the authority to set the temperature control scheme and start / stop the module, the nurse has the authority to adjust the power range and response time, and the maintenance authority can only view the running status and system logs, but cannot change the temperature control strategy, if the permission verification fails, the system will automatically refuse to execute the relevant instructions and record the operation log for traceability.

[0145] The external data interface unit is used to connect to the data input ports of various medical monitoring devices, including electrocardiogram monitors, pulse oximeters, and body temperature recorders. The collected patient physiological signals are uploaded to the main controller in real time and used as input features to participate in the dynamic temperature adjustment calculation of the AI ​​temperature control model.

[0146] For example, if a patient experiences a sudden increase in heart rate or fluctuations in blood oxygen during a blood transfusion, the system will adjust the heating power output and temperature control curve parameters in real time to prioritize the stability of the patient's circulation and avoid thermal load interfering with the physiological state.

[0147] Meanwhile, the hospital information management system interface unit interfaces with the hospital's HIS system through standardized interface protocols, such as HL7 or FHIR protocols.

[0148] The controller can automatically read the patient's hospitalization information, basic vital signs, operation time, past medical history and other data, and perform matching operations based on the built-in temperature control template library to automatically recommend the most suitable temperature rise plan template.

[0149] For example, different scenario modes such as "intraoperative constant temperature maintenance", "postoperative slow recovery" and "ICU hypothermia rewarming" are used to initialize the temperature control target value, power threshold and response frequency of each module, thereby reducing manual intervention and improving temperature control accuracy and response speed.

[0150] Through the synergy of the above mechanisms, this system can not only achieve hierarchical management and authorized control of temperature adjustment operations, but also realize adaptive temperature rise strategy adjustment based on patient status, thereby improving the system's intelligence and clinical adaptability.

[0151] A detachable heating blanket module, suitable for a multi-functional heating system, the heating blanket module comprising:

[0152] Standardized detachable interface, module identification unit, heating component, turning identification unit, self-disinfection unit, and heat storage unit, among which:

[0153] The module identification unit includes a storage chip and a communication protocol adapter, which are used to provide module identification code, power level information and permission parameters when connected to the main controller, so as to support the system to build a permission hierarchical control strategy model;

[0154] The turning recognition unit includes a flexible pressure sensor array and a triaxial accelerometer, which are used to monitor changes in the patient's position in real time and send the turning signal to the main controller so that the system can identify the area of ​​position change and update the temperature control strategy.

[0155] The self-disinfection unit includes a far-infrared disinfection film and a time-temperature dual trigger controller, which is used to automatically start the high-temperature surface disinfection process after the module is removed from the patient or after the heating task is completed.

[0156] The thermal storage unit uses high heat capacity materials with phase change temperature control function, which can automatically enter the slow release heating mode to maintain the temperature stability of the local temperature zone in the event of a power outage or communication interruption.

[0157] The module is also preferably equipped with a temperature overshoot protection device, which is used to ensure patient safety by limiting current and reducing power or disconnecting the heat source in the event of abnormal heating or feedback failure.

[0158] After receiving the temperature control command from the main controller, the module executes the following operation procedure:

[0159] The module identification unit reads the identification code, power level, and access information and sends it back to the main controller;

[0160] The heating components start the heating process according to the target power and response time issued by the main controller scheduling model;

[0161] The rolling-over recognition unit monitors changes in body position in real time and provides feedback on rolling-over signals;

[0162] The self-disinfection unit automatically starts high-temperature surface disinfection when the heating task is completed or when the connection is disconnected.

[0163] The thermal storage unit switches to phase change slow release mode to maintain regional thermal stability in the event of a power outage or communication failure.

[0164] The temperature overshoot protection device monitors the heat output process in real time. When an abnormality is detected, it automatically reduces power or cuts off the heating component to ensure patient safety.

[0165] In this embodiment, the various units of the heating blanket module and their response operation flow have been fully disclosed in the above structural description and operation process description. The relevant module structure and execution flow can be directly implemented by those skilled in the art without creative labor.

[0166] Although some symbols in the formulas in the text are as follows These are commonly used variables, but they represent stress tensor, strain response, temperature function and time variable respectively in different sub-modules. They are all clearly defined in specific embodiments and formula paragraphs, and have uniqueness and non-confusion, ensuring that the present invention has clear disclosure.

[0167] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0168] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A multifunctional heating system for blood transfusion and intravenous infusion, characterized in that, include: Multiple heating blanket modules, each heating blanket module including: Standardized detachable interface, module identification unit, heating component, turning identification unit, self-disinfection unit, and heat storage unit; The module identification unit is used to provide identification information, power parameters and permission levels, and supports the construction of a permission hierarchical control model. The thermal storage unit includes high heat capacity or phase change material, and switches to slow release mode to output heat when power is lost or communication is interrupted. Also includes: Infrared thermal imaging unit is used to acquire images of the patient's body surface temperature and generate a thermal image; The AI ​​temperature control prediction unit is used to predict the target temperature trend and output the temperature adjustment curve based on thermograms, turning signals, patient parameters and historical data. The main controller is used to establish a Bluetooth Mesh connection with the heating blanket module, build a scheduling model, output temperature adjustment commands based on prediction results, and realize feedforward temperature control response. The main controller further includes: The historical temperature control trend analysis module is used to extract the rate and duration of temperature change in each region based on continuous infrared thermal image data, and generate corresponding heating priority parameters. The overheating detection unit is used to determine whether there is an overheating risk exceeding the safety threshold during the heating process of the target area, based on the heating rate and the current body temperature change rate. The thermal anomaly compensation module is used to perform thermal interpolation and abnormal data compensation operations when spatially isolated areas or areas of sudden temperature changes are detected in the infrared thermal image, and to coordinate with adjacent modules to perform auxiliary power control.

2. The multifunctional warming system for blood transfusion and intravenous infusion according to claim 1, characterized in that: After receiving the turning signal from the turning recognition unit, the main controller combines the body surface temperature image acquired by the infrared thermal imaging unit to identify the local low temperature area caused by the change in body position and update the temperature adjustment target area. The main controller calls the AI ​​temperature control prediction unit to generate a thermal regulation prediction curve based on the target area, the patient's current physiological parameters, historical response data, and turning sequence characteristics, and outputs a control instruction set including target power, heating rate, response time, and module priority. The main controller further dynamically adjusts the response frequency and output strategy based on the correlation between the turning signal and the changes in the thermogram, so as to achieve priority temperature adjustment and precise temperature rise control in local areas.

3. A multifunctional warming system for blood transfusion and intravenous infusion according to claim 2, characterized in that: The main controller has a module operation status monitoring function. When it detects that a heating blanket module has communication interruption, abnormal temperature control response, or fault information, it automatically marks it as a degraded state and adjusts the control strategy based on module permissions and spatial distribution. Based on the location of the faulty module, the main controller instructs adjacent modules to adjust their power output or response time to form a local thermal compensation area. The AI ​​temperature control prediction unit introduces a boundary compensation factor to correct the prediction model boundary and maintain the continuity and stability of temperature control.

4. A multifunctional warming system for blood transfusion and intravenous infusion according to claim 3, characterized in that: The main controller further includes a historical evolution weight model, which generates temperature control weight factors based on the rate of temperature change and duration of each region in the continuous infrared thermogram using a weighted average function, and dynamically adjusts the temperature rise control priority of each region based on the weights. The main controller also includes an over-temperature risk prediction mechanism. This mechanism makes predictions based on the slope of the target area's temperature rise curve, the current temperature change trend, and historical response delay values. When the predicted temperature exceeds a set threshold within a set time window, it executes control strategies including power reduction, rate limiting, and output pause. The main controller also includes an abnormal temperature zone identification and reconstruction mechanism, which is used to identify spatially isolated or temperature difference abruptly changed areas in the infrared thermal image, trigger image interpolation and response trend fitting algorithms to complete the missing temperature image, and link the AI ​​temperature control prediction unit to coordinate the output of adjacent modules to achieve temperature compensation and continuous heating control in local areas.

5. A multifunctional warming system for blood transfusion and intravenous infusion according to claim 4, characterized in that: The main controller includes a feedback control mechanism, which, after the heating task is completed, triggers parameter adjustment of the prediction model and correction of the power control strategy based on the deviation between the measured temperature and the prediction result, so as to improve the temperature control accuracy and response stability. The main controller also includes a redundancy coordination mechanism. When multiple module abnormalities or regional temperature fluctuation risks are detected, power coordination scheduling is performed to realize multi-module joint load reduction control based on module capacity, distribution location and priority, thereby reducing local thermal shock. The main controller further includes a constant temperature maintenance mechanism, which is used to enter a low-power pulse mode after the temperature in the target area stabilizes, and dynamically adjust the pulse period and duty cycle; preferably, it also includes a strategy evolution module, which is used to switch control strategy templates when predicting and correcting failures, to adapt to diverse clinical temperature control scenarios.

6. A multifunctional warming system for blood transfusion and intravenous infusion according to claim 5, characterized in that, The main controller further includes a parallel control and load balancing mechanism, which is used to dynamically allocate heating tasks and coordinate power output based on module response delay, power load and control priority when multiple heating modules are running simultaneously, so as to realize task sharing and heat load balancing among modules.

7. A multifunctional warming system for blood transfusion and intravenous infusion according to claim 6, characterized in that, The main controller includes a hierarchical access control mechanism and a data linkage control mechanism, which includes: The access control unit is used for permission verification and function restriction of temperature adjustment operations based on module identification information and user permission level. It supports hierarchical setting of doctor permissions, nursing permissions and maintenance permissions. The external data interface unit is used to receive real-time monitoring data from devices such as electrocardiogram monitors and pulse oximeters, and to dynamically adjust control parameters in conjunction with the temperature control model. The mechanism also includes a docking unit with the hospital information management system, which is used to read basic patient information and recommend temperature control templates and initialize parameters.

8. A detachable heating blanket module, suitable for the multifunctional heating system as described in claims 1 to 7, characterized in that, The heating blanket module includes: Standardized detachable interface, module identification unit, heating component, turning identification unit, self-disinfection unit, and heat storage unit, among which: The module identification unit includes a storage chip and a communication protocol adapter, which are used to provide module identification code, power level information and permission parameters when connected to the main controller, so as to support the system to build a permission hierarchical control strategy model; The turning recognition unit includes a flexible pressure sensor array and a triaxial accelerometer, which are used to monitor changes in the patient's position in real time and send the turning signal to the main controller so that the system can identify the area of ​​position change and update the temperature control strategy. The self-disinfection unit includes a far-infrared disinfection film and a time-temperature dual trigger controller, which is used to automatically start the high-temperature surface disinfection process after the module is removed from the patient or after the heating task is completed. The thermal storage unit uses a high heat capacity material with phase change temperature control function, which can automatically enter the slow release heating mode to maintain the temperature stability of the local temperature zone in the event of a power outage or communication interruption. The module is also preferably equipped with a temperature overshoot protection device, which is used to ensure patient safety by limiting current and reducing power or disconnecting the heat source in the event of abnormal heating or feedback failure.

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