Shadowless lamp platform-based multi-area temperature sensing and self-adaptive thermal radiation regulation and control method

Abstract

The invention provides a shadowless lamp platform-based multi-area temperature sensing and self-adaptive heat radiation regulation and control method and system, relates to the technical field of medical treatment, and aims to solve the technical problems of inaccurate temperature sensing and non-uniform heating control of an operation area in related technologies. The method comprises the following steps: collecting original temperature data output by an infrared temperature measurement sensor array covering a surgical area; obtaining a time-space reliability weight; generating a weighted temperature distribution diagram representing the surgical area; obtaining a thermal inertia coefficient representing the body surface tissue of the patient; for each position point in the weighted temperature distribution diagram, obtaining a heating power demand potential based on the weighted temperature of the position point, a preset target temperature and a thermal inertia coefficient; dividing the operation area into a plurality of heating sub-areas, and obtaining polymerization heating power demand values of the areas; and one heating power is distributed to each heating sub-region.

Description

Technical Field

[0001] This application relates to the field of medical technology, and in particular to a method for multi-region temperature sensing and adaptive thermal radiation control based on a shadowless lamp platform. Background Technology

[0002] Maintaining a patient's normal body temperature during surgical procedures is crucial for reducing intraoperative and postoperative complications and promoting recovery. During surgery, patients are prone to hypothermia due to factors such as the suppression of the thermoregulatory center by anesthetic drugs, exposure of the surgical wound, intravenous fluid administration, and the low ambient temperature of the operating room. Intraoperative hypothermia can lead to a series of adverse consequences, including increased risk of cardiovascular events, coagulation disorders, increased wound infection rates, slowed drug metabolism, and delayed postoperative recovery.

[0003] To address this issue, various warming measures have been adopted clinically, such as warmed infusion / transfusion equipment, circulating water blankets, and inflatable warming blankets. However, these traditional warming methods have certain limitations. For example, warming blankets and other equipment may interfere with aseptic procedures in the surgical area, or their coverage area may be limited, making it impossible to effectively and precisely warm critical surgical wound areas directly.

[0004] In related technologies, surgical shadowless lamps have begun to integrate heating functions, directly heating the surgical area through infrared radiation. This has, to some extent, solved the problem of conflict between heating equipment and the surgical operating area. However, shadowless lamp systems with integrated heating functions are generally quite rudimentary in terms of temperature control. They mostly employ single-point temperature measurement or simple area average temperature feedback for coarse open-loop or closed-loop control. This control method has the following technical problems:

[0005] Single-point temperature measurement cannot reflect the complex temperature distribution of the entire surgical area and is prone to "overgeneralization" due to improper selection of temperature measurement points. Even with multi-point temperature measurement, the raw data is easily affected by temporary obstruction or reflection interference from surgical instruments, dressings, body fluids, etc., resulting in distorted readings. If used directly for control without processing, it will cause misjudgment of the control logic and drastic fluctuations in output power.

[0006] Heating modules typically apply uniform power to the entire irradiation area, failing to provide differentiated and precise heat compensation based on the actual temperature and heat dissipation of different parts of the surgical area (such as the wound center and tissue edges). This can lead to some areas reaching the target temperature while others remain too cold, or to the risk of localized overheating in areas designed to raise the temperature of low-temperature zones.

[0007] Control logic is typically based on simple error feedback of "target temperature - current temperature," resulting in a delayed response and a lack of adaptability to individual patient thermodynamic characteristics (such as thermal inertia of different body types) and surgical dynamics (such as temperature requirements at different stages). This passive control method is prone to overshoot or oscillation when temperature changes occur, making it difficult to achieve stable, rapid, and precise temperature control. Summary of the Invention

[0008] This application provides a method and system for multi-region temperature sensing and adaptive thermal radiation control based on a shadowless lamp platform, which is used to improve the technical problems of inaccurate temperature sensing and uneven heating control in the surgical area in related technologies.

[0009] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0010] In a first aspect, this application provides a method for multi-region temperature sensing and adaptive thermal radiation control based on a shadowless lamp platform, comprising: collecting raw temperature data output by an infrared temperature sensor array covering the surgical area, wherein the infrared temperature sensor array includes multiple temperature sensors; for each temperature sensor, obtaining a time-space reliability weight based on its historical temperature data within a preset time window and the current temperature data of its adjacent sensors; generating a weighted temperature distribution map characterizing the surgical area based on the raw temperature data and the corresponding time-space reliability weight; and based on the initial thermal response of the surgical area... Based on the characteristics, a thermal inertia coefficient characterizing the patient's body surface tissue is obtained; for each location point in the weighted temperature distribution map, a thermal demand potential is obtained based on the weighted temperature of that location point, the preset target temperature, and the thermal inertia coefficient; the surgical area is divided into multiple heating sub-regions, and for each heating sub-region, a regional aggregated thermal demand value is obtained based on the thermal demand potential of all locations within that heating sub-region; based on the proportion of the regional aggregated thermal demand value of each heating sub-region in the sum of the aggregated thermal demand values ​​of all heating sub-regions, a heating power is allocated to each heating sub-region.

[0011] In one possible implementation of the first aspect, the step of obtaining a time-space reliability weight includes: obtaining the historical temperature change rate of the temperature sensor and obtaining the current temperature difference between the temperature sensor and each of its adjacent sensors; generating a time reliability factor based on a comparison between the historical temperature change rate and a preset physical temperature change rate threshold; generating a spatial reliability factor based on a comparison between the current temperature difference and a preset spatial temperature gradient threshold; and combining the time reliability factor and the spatial reliability factor to obtain the time-space reliability weight.

[0012] In one possible implementation of the first aspect, the step of obtaining a thermal inertia coefficient characterizing a patient's body surface tissue includes: at the beginning of the modulation phase, applying a calibration thermal pulse having a preset power and duration to a preset calibration area; collecting temperature change data of the calibration area before and after the application of the calibration thermal pulse; and obtaining the thermal inertia coefficient based on the power and duration of the calibration thermal pulse and the temperature change data.

[0013] In one possible implementation of the first aspect, the step of obtaining a thermal demand potential is further based on a historical heat flow accumulation associated with the location point; the historical heat flow accumulation represents the total thermal radiation energy applied to the heating sub-region where the location point is located over a preset period of time in the past.

[0014] In one possible implementation of the first aspect, after allocating a heating power to each of the heating sub-regions, the method further includes: obtaining a cross-regional thermal influence matrix characterizing the heat interaction between different heating sub-regions; constructing a set of power correction equations with the heating power to be corrected for each heating sub-region as variables, the cross-regional thermal influence matrix as coefficients, and the target thermal response of each heating sub-region as a constant; solving the set of power correction equations to obtain a set of corrected heating powers; and replacing the allocated heating power with the corrected heating power.

[0015] In one possible implementation of the first aspect, the method further includes: periodically applying a preset micro-perturbation power pulse to a single heating sub-region; collecting temperature response data of itself and other heating sub-regions caused by the micro-perturbation power pulse; and updating the elements of the cross-regional thermal influence matrix based on the deviation between the temperature response data and the theoretical temperature response predicted by the cross-regional thermal influence matrix.

[0016] In one possible implementation of the first aspect, the method further includes: obtaining the real-time distance between the shadowless lamp head and the surface of the surgical area; obtaining a power attenuation factor characterizing the relationship between the safe distance and the upper limit of heating power; and adjusting the upper limit of the total power that can be allocated to all heating sub-regions based on the real-time distance and the power attenuation factor.

[0017] In one possible implementation of the first aspect, the method further includes: accumulating the total output energy of each of the heating sub-regions in real time; and stopping heating the heating sub-region when the total output energy of any of the heating sub-regions reaches a preset energy safety threshold.

[0018] Secondly, this application also provides a multi-area temperature sensing and adaptive thermal radiation control system based on a shadowless lamp platform, comprising: a data acquisition module for acquiring raw temperature data output by an infrared temperature sensor array covering the surgical area, wherein the infrared temperature sensor array includes multiple temperature sensors; a weight generation module for obtaining a time-space reliability weight for each temperature sensor based on its historical temperature data within a preset time window and the current temperature data of its adjacent sensors; a temperature map generation module for generating a weighted temperature distribution map characterizing the surgical area based on the raw temperature data and the corresponding time-space reliability weight; and a coefficient acquisition module for obtaining coefficients based on the surgical area. The system includes: an initial thermal response characteristic module to obtain a thermal inertia coefficient characterizing the patient's surface tissue; a potential function processing module to obtain a thermal demand potential for each location point in the weighted temperature distribution map, based on the weighted temperature of that location point, a preset target temperature, and the thermal inertia coefficient; a region aggregation module to divide the surgical area into multiple heating sub-regions and, for each heating sub-region, to obtain a region aggregated thermal demand value based on the thermal demand potential of all locations within that sub-region; and a power allocation module to allocate a heating power to each heating sub-region based on the proportion of the region aggregated thermal demand value of each heating sub-region to the sum of the aggregated thermal demand values ​​of all heating sub-regions.

[0019] In one possible implementation of the second aspect, it further includes: a power correction module, used to obtain a cross-regional thermal influence matrix characterizing the heat interaction between different heating sub-regions, construct and solve a set of power correction equations with the heating power to be corrected in each heating sub-region as variables, the cross-regional thermal influence matrix as coefficients, and the target thermal response of each heating sub-region as constants, to obtain a set of corrected heating powers, and use the corrected heating powers to replace the allocated heating power. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a multi-region temperature sensing and adaptive thermal radiation control method based on a shadowless lamp platform, provided for some embodiments of this application;

[0021] Figure 2 A flowchart illustrating a multi-region temperature sensing and adaptive thermal radiation control method based on a shadowless lamp platform, provided for other embodiments of this application;

[0022] Figure 3 A flowchart illustrating a multi-region temperature sensing and adaptive thermal radiation control method based on a shadowless lamp platform, provided for other embodiments of this application;

[0023] Figure 4A flowchart illustrating a multi-region temperature sensing and adaptive thermal radiation control method based on a shadowless lamp platform, provided for other embodiments of this application;

[0024] Figure 5 A schematic diagram of the structure of a multi-zone temperature sensing and adaptive thermal radiation control system on a shadowless lamp platform provided for some embodiments of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0026] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0027] Furthermore, in this application, directional terms such as "upper," "lower," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and may change accordingly depending on the orientation of the components in the accompanying drawings.

[0028] In this application, unless otherwise expressly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium. Furthermore, the term "electrical connection" can refer to the manner in which an electrical connection is used to achieve signal transmission.

[0029] As used herein, “about,” “approximately,” or “approximately” includes the stated value and a reference value within an acceptable range of deviation from the given value, characterized in that the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the given quantity (i.e., the limitations of the measurement method).

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it.

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it.

[0032] This invention provides a multi-region temperature sensing and adaptive thermal radiation control system and method based on a surgical lamp platform. The system integrates a high-precision multi-point temperature sensing array and a zone-based, independently controllable thermal radiation module, aiming to solve the problems caused by the passive and crude management of surgical area surface temperature during existing surgical procedures, such as localized overcooling or overheating, inability to adapt to dynamic changes during surgery, and potential safety risks.

[0033] In a typical application scenario, the system is deployed on a surgical light in an operating room. The heating module of the surgical light is integrated with or parallel to the lighting module, and the surgical area beneath it is covered by an array of miniature infrared temperature sensors. This sensor array can be, for example, a single... The grid layout enables real-time capture of surface temperature information in the surgical area. The heating module is designed to provide independent or differentiated power output to different sub-regions. For example, the entire heating area can be divided into four independently controllable heating sub-regions: the central region, the left side region, the right side region, and the edge region.

[0034] like Figure 1 As shown, the specific process of this method may include the following steps:

[0035] S100: Collects raw temperature data output from the infrared temperature sensor array covering the surgical area.

[0036] After the system starts up, the control core continuously acquires data from an array of infrared temperature sensors deployed on the operating room platform. Each sensor in the array corresponds to a monitoring point in the surgical area. The collected data forms the basis for all subsequent analysis and control.

[0037] In one embodiment, the sensor array is organized into a two-dimensional grid, and its output can be represented as a two-dimensional matrix. , of which elements Represents time Located in grid coordinates The raw temperature values ​​are measured by the sensor. The sampling frequency can be set according to the accuracy requirements of the surgery, for example, 10 times per second. These raw data directly reflect the instantaneous temperature of the surgical area surface, but may contain outliers introduced by sensor noise, electromagnetic interference, or, more importantly, by temporary obstructions caused by surgical instruments, dressings, body fluids, etc.

[0038] S200. For each of the temperature sensors, obtain a time-space reliability weight.

[0039] To address potential noise and outliers in the original data, this invention introduces the aforementioned "time-space reliability weight". This weighting does not simply filter the data, but rather assigns a weight between [a certain value] and [a certain value] to the reading of each sensor at each time step. A reliability score is assigned between the data and the data. This score combines the continuity of the data in the temporal dimension and the reasonableness in the spatial dimension, thereby intelligently identifying and reducing the impact of unreliable data in subsequent calculations.

[0040] This step can be broken down into the following sub-steps:

[0041] S210. Obtain the historical temperature change rate of the temperature sensor and obtain the current temperature difference between the temperature sensor and each of its adjacent sensors.

[0042] For those located The system queries the sensor for its data within a very short time window (e.g., the most recent). Historical temperature data within a few seconds. Based on this data, the historical temperature change rate can be calculated. .

[0043] For example, the rate of change can be obtained as follows:

[0044]

[0045] in, It is the original temperature at the current moment. It is the original temperature at the previous sampling time. It is the sampling time interval.

[0046] At the same time, the system obtains the current time information of the sensor and its directly adjacent sensors (e.g., the four neighbors above, below, left, and right). The original temperature readings. And calculate the set of absolute values ​​of the temperature differences. .

[0047] For example, for sensors and one of its neighbors The temperature difference is .

[0048] S220. Based on the comparison between the historical temperature change rate and the preset physical temperature change rate threshold, a time reliability factor is generated.

[0049] Biological tissues, due to their specific heat capacity and thermal conductivity, have a physical upper limit to their rate of temperature change. Without sudden changes from a strong external heat or cold source, their temperature will not undergo an instantaneous jump. Using this prior knowledge, the reliability of a reading over time can be determined. This invention constructs a time reliability factor. .

[0050] Its construction logic is based on the temperature change rate measured by the sensor. Far exceeding a threshold for the rate of change of physical temperature set based on common physiological knowledge. (For example, If the reading is abnormal (e.g., a reflective metal object momentarily obstructs the sensor's field of view), then the reading is considered abnormal.

[0051] For example, time reliability factor It can be generated using a decay function:

[0052]

[0053] in, It is a normal constant that adjusts the decay rate. When hour, for This indicates complete reliability; when it exceeds the threshold, It will drop rapidly, approaching... .

[0054] S230. Based on the comparison between the current temperature difference and the preset spatial temperature gradient threshold, a spatial reliability factor is generated.

[0055] Similarly, on a macroscopic scale, the temperature distribution on the surface of biological tissues is typically continuous and smooth, without significant temperature cliffs within adjacent, extremely small regions (e.g., a few millimeters). This invention utilizes this characteristic to construct a spatial reliability factor. .

[0056] Its construction logic is that if a sensor The reading differed too much from the readings of its neighbors, exceeding a preset spatial temperature gradient threshold. (For example, If the reading is not spatially reliable, then the reading may be unreliable.

[0057] For example, spatial reliability factor This can be calculated based on the temperature difference with all neighbors. First, calculate an average spatial deviation. ,in yes The neighbor set. Then, it is generated through a similar decay function. :

[0058]

[0059] in, It is a normal value that adjusts the decay rate.

[0060] S240. Combine the time reliability factor with the space reliability factor to obtain the time-space reliability weight.

[0061] Final Time-Space Reliability Weights It is a comprehensive assessment of reliability across both temporal and spatial dimensions. A data point must simultaneously satisfy both temporal continuity and spatial plausibility to be considered highly reliable.

[0062] For example, the final weight can be obtained by multiplying the two:

[0063]

[0064] Thus, only when and They are close to hour, Only approaching Unreliability in any dimension will lead to a significant reduction in the final weight.

[0065] S300. Based on the original temperature data and the corresponding time-space reliability weights, generate a weighted temperature distribution map characterizing the surgical area.

[0066] With reliability weights assigned to each data point, the system no longer uses the raw temperature data directly. Instead, it generates a corrected, weighted temperature distribution map that better reflects the true physical conditions. The temperature value at each point on this graph is a weighted average of its own original reading and the readings of its neighbors; the weights are calculated as described above. .

[0067] For location Weighted temperature The calculation method is as follows:

[0068]

[0069] In the above formula, if the sensor Reliability weight Very high (close to) Since its neighbors also have high weights, the result will be a smoothed average of these highly reliable data.

[0070] If the sensor weight If it suddenly becomes very low (e.g., due to obstruction), then its original reading... The contribution at the molecular level becomes negligible, while the weighted temperature... It will be primarily "filled" by the temperature of its highly reliable neighbors.

[0071] This method effectively eliminates outliers and uses available information from the surrounding environment to make a reasonable estimate of the contaminated area, generating a smooth and reliable real-time temperature map.

[0072] S400. Based on the initial thermal response characteristics of the surgical area, obtain a thermal inertia coefficient characterizing the patient's body surface tissue.

[0073] To achieve proactive and personalized temperature control, simply knowing the current temperature is insufficient; it is also necessary to understand the characteristics of the object being heated, namely its response speed to heat input. Patients of different ages, body types, and tissue types have varying thermal inertia (the ability to absorb and dissipate heat) in their body surface tissues. This invention utilizes the thermal inertia coefficient... This is used to quantify these individual differences.

[0074] For example, its specific implementation is as follows:

[0075] S410. At the beginning of the control phase, a calibration thermal pulse with a preset power and duration is applied to a preset calibration area.

[0076] During the surgical preparation phase, after anesthesia is completed, the system will select a small area at the center of the surgical area (e.g., by...). The area covered by each heating unit is used as the calibration area. The control system then applies a precisely controlled, brief, low-power thermal pulse to this calibration area. For example, using... The power, continuous The total applied energy is .

[0077] S420. Collect temperature change data of the calibration area before and after the calibration thermal pulse.

[0078] Throughout the application of the thermal pulse and for a short period afterward, the system will frequently collect weighted temperature data from all temperature sensors within the calibration area. Record from the initial temperature To reach the peak temperature The complete temperature rise curve. The key data is the total temperature rise. .

[0079] S430. Based on the power and duration of the calibration thermal pulse and the temperature change data, obtain the thermal inertia coefficient.

[0080] Thermal inertia coefficient It is defined as the temperature change per unit area caused by a unit energy input. It can be expressed as:

[0081]

[0082] in, It is the area of ​​the calibration region. The unit can be This coefficient directly reflects the sensitivity of the patient's skin tissues to heat: The larger the value, the easier the tissue is to heat and the smaller its thermal inertia; conversely, the smaller the value, the less easily it is to heat and the larger its thermal inertia.

[0083] This was obtained through active detection. This will be used in all subsequent control calculations, so that the entire regulatory system matches the specific physiological characteristics of the current patient from the very beginning.

[0084] S500. For each location point in the weighted temperature distribution map, obtain a thermal demand potential.

[0085] Traditional methods such as PID control primarily focus on the current error (the difference between the target temperature and the actual temperature). This invention, however, constructs a more predictive index, defined as the "thermal demand potential." It not only represents the current temperature difference, but also incorporates predictions of future temperature trends and avoidance of the risk of overheating.

[0086] "Heat demand trend" It was designed as a scalar field, distributed throughout the entire surgical area. At any point... Its value is calculated based on a deterministic combination of multiple factors.

[0087] In one embodiment, the calculation formula is as follows:

[0088]

[0089] Among them, basic driving items It is the most basic driving force, namely the target temperature. (This can be set by the doctor according to the stage of the surgery, for example, the central area) Edge area ) and current weighted temperature The difference. The larger the difference, the stronger the fundamental driving force.

[0090] Personalized adjustment items This step incorporates the patient's thermal inertia coefficient obtained in the previous step. Its reciprocal This represents raising the temperature at that point. Required energy density. For patients with high thermal inertia ( (smaller) A larger value amplifies the thermal demand potential, causing the system to output stronger power to overcome its sluggish thermal response. Conversely, for patients with sensitive constitutions, the demand will be reduced accordingly to prevent overshoot, thus achieving personalized control.

[0091] Historical suppression item It is a dynamic damping term designed to prevent the system from "excessively applying force". It is a "historical heat flow accumulation", which records the heat flow over a period of time. Inside, applied to point The total thermal radiation energy of the heating sub-region.

[0092] For example, It can be calculated using a recursive formula with a forgetting factor:

[0093]

[0094] in, It is the previous control cycle point. The heating sub-region 'output power' It is close to The forgetting factor determines the "half-life" of historical memory.

[0095] It is the hyperbolic tangent function, and its range is . It is a positive constant used to adjust the sensitivity of the inhibition term. (When historical heating amount...) When I was very young, near Suppression term near This will have no impact on demand.

[0096] When the system continuously outputs high power, it leads to When it accumulates to a large amount, Approaching Suppression term Approaching This will significantly curb the potential for increased heat demand. Even if the current temperature difference still exists, the system will actively reduce the heating intensity, thus cleverly using historical information to predict and avoid future temperature overshoot.

[0097] In summary, the demand for heat is trending towards... It is a forward-looking composite indicator that integrates "current state", "individual characteristics" and "historical behavior".

[0098] S600. Divide the surgical area into multiple heating sub-regions, and obtain a regional aggregated heat demand value for each heating sub-region.

[0099] The heating module of the shadowless lamp is physically divided into several sub-regions whose power can be independently controlled, for example... And the demand for heat... It is a field distributed across a continuous (or high-density discrete) sensor grid. Therefore, it is necessary to aggregate the demands at the "points" into demands on the "surface".

[0100] This step will set the coordinates of the sensor grid. Mapped to its corresponding heating sub-region Then, for each sub-region Calculate its "regional aggregated heat demand value". .

[0101] For example, the most straightforward way to aggregate is by summation:

[0102]

[0103] The summation iterates through all regions located in the subregion. Sensor location points within. Represents the first The total and urgent heating demand of each heating sub-region at the current moment.

[0104] S700. Based on the proportion of the regional polymerization heat demand value of each heating sub-region to the total polymerization heat demand value of all heating sub-regions, allocate a heating power to each heating sub-region.

[0105] This step converts the demand values ​​for each region into specific heating power. This invention employs a demand-based allocation strategy to ensure that total power is rationally distributed among the areas where it is most needed.

[0106] First, calculate the total aggregate heat demand for all regions:

[0107]

[0108] Then, the Power of each heating sub-region Allocation based on their proportion of demand:

[0109]

[0110] in, This is the maximum allowed total output power of the system at the current moment. This maximum value is dynamic and is subject to various safety mechanisms (such as distance sensors and cumulative energy monitoring, which will be mentioned later).

[0111] This ensures that regions with higher demand receive higher power, and the sum of the power of all sub-regions equals the current total allowed power. When the demand of a certain region is met (its... (When the power decreases), its power will automatically decrease and "transfer" this power to other areas that need it more.

[0112] In the above scheme, the S700's power distribution is based on the premise that each heating sub-region is thermodynamically independent. However, in reality, heating a region (such as the central region) inevitably generates heat radiation and conduction, thus affecting its neighboring regions (such as the left and right regions). If this "cross-region effect" or "thermal crosstalk" is ignored, it may lead to a decrease in control accuracy, especially at region boundaries. Therefore, as... Figure 2 As shown, the method of this application further includes:

[0113] S810. Obtain a cross-regional thermal influence matrix that characterizes the thermal interaction between different heating sub-regions.

[0114] The system can determine this matrix using an automated testing program. This matrix... It is square array ( (This refers to the number of heated sub-regions). Matrix elements. Represents when pair of sub-regions When a unit power is applied, in the sub-region The contribution of steady-state temperature rise that can be measured at the center point.

[0115] For example, determine The process is as follows:

[0116] With a known, constant power alone Pair of sub-regions Initiate heating while shutting off all other areas. Wait sufficient time for the entire temperature field to reach a new steady state. Measure the temperature in the sub-region at this point. The average temperature rise relative to the initial temperature .So, For all By repeating this process, the complete matrix can be constructed. Diagonal elements Indicates self-heating efficiency, off-diagonal elements This indicates the intensity of crosstalk to other regions.

[0117] S820. Construct and solve a power correction equation set.

[0118] In obtaining the initially allocated power Subsequently, it is not used directly. This is because the purpose of this application is not power itself, but the target thermodynamic response caused by power. In S500-S600, the polymerization heat demand value for each region was calculated. This can be considered as the desired "target thermal response".

[0119] set up Let be the corrected heating power to be solved. Considering the cross-regional effect, the first... The total thermal effect ultimately felt by a region is the sum of the heating contributions from all regions, i.e. In order for this overall effect to accurately match the target thermodynamic response. A system of linear equations can be constructed:

[0120]

[0121] in, It is a column vector containing all the corrected powers to be solved. . It is a column vector containing the aggregated heat demand values ​​for all regions. . This refers to the cross-regional thermal influence matrix obtained earlier.

[0122] S830 uses the modified heating power for output.

[0123] By solving the above system of linear equations (for example, using Gaussian elimination or matrix inversion), the corrected power vector can be obtained. This group This is the theoretically optimal power allocation scheme after taking thermal crosstalk into account. The system will use this modified power to drive each heating sub-region.

[0124] This correction mechanism elevates the control logic from a simple proportional allocation to a multivariable decoupled control model, which can proactively compensate for the mutual influence between regions, thereby achieving more precise coordinated heating.

[0125] Cross-regional thermal influence matrix It may be fixed after initial calibration. However, during surgery, factors such as the laying of the surgical drape and minor changes in the patient's position can alter the path and absorption rate of heat radiation, thus changing the fixed state. The matrix becomes inaccurate. To solve this problem, such as... Figure 3 As shown, the method of this application further includes:

[0126] S910: Periodically apply a preset micro-perturbation power pulse to a single heating sub-region.

[0127] The system can perform proactive "system identification" without affecting overall temperature stability. For example, every few minutes, the system will select a heating sub-region. At its current output power On top of that, superimpose a very small (e.g., power) And short-lived (e.g., Square wave or sine wave power pulse (seconds) .

[0128] S920: Collects temperature response data caused by micro-perturbation power pulses.

[0129] During this micro-perturbation, the system will closely monitor all sub-regions. The rate of temperature change, and calculate the result from Actual temperature response caused .

[0130] S930. Based on the deviation between the actual response and the theoretical response, the cross-regional thermal influence matrix is ​​updated.

[0131] According to the current Matrix, by The theoretical temperature response that should be caused is By comparing the actual observed values ​​with the theoretical predicted values, an error can be obtained. .

[0132] Using this error, we can... The corresponding elements of the matrix are fine-tuned. For example, gradient descent or LMS (least mean square) algorithms are used to update the matrix. :

[0133]

[0134] in It uses a small learning rate. By periodically and sequentially applying micro-perturbations and updating all sub-regions, the system can dynamically and online track environmental changes, continuously optimize its internal model, and maintain high control accuracy and strong robustness.

[0135] In addition to the core control logic mentioned above, such as Figure 4 As shown, this invention also integrates a multi-level security mechanism to ensure that patient safety is given the highest priority under any circumstances. The method further includes:

[0136] S1010, Power constraint based on attitude / distance sensor.

[0137] The operating light head is equipped with a distance sensor (such as ultrasonic or ToF lidar) to monitor the distance between the light head and the surface of the surgical area in real time. The system has a preset safe distance curve. This curve defines the maximum permissible total power at different distances. When the distance... Less than a certain safety threshold hour, It will drop sharply to zero. The upper limit of total power calculated in the S700 step. This constraint applies:

[0138]

[0139] in This is the absolute maximum power configured in the system. This effectively prevents the risk of burns caused by the lamp holder being accidentally lowered.

[0140] S1020, Hard shutdown based on cumulative working energy.

[0141] The system will heat each sub-region Maintain a cumulative output energy counter that starts timing from the beginning of the surgery. Meanwhile, the system has a preset total energy safety threshold based on the type of surgery and the patient's condition. Once any region If the threshold is exceeded, regardless of the current temperature, the system will forcibly stop heating the area and send an alarm to medical personnel. This prevents prolonged, unrestricted heating due to sensor malfunction or control logic failure.

[0142] The present invention also provides a multi-region temperature sensing and adaptive thermal radiation control system based on a shadowless lamp platform. This system is configured to perform all or part of the steps of the above-described method. Specifically, the system may include one or more processors and a memory, the memory storing computer program instructions, which, when executed by the processor, cause the system to perform the above-described method.

[0143] In some embodiments, such as Figure 5As shown, the system includes a data acquisition module for acquiring raw temperature data output by an infrared temperature sensor array covering the surgical area, the infrared temperature sensor array including multiple temperature sensors; a weight generation module for obtaining a time-space reliability weight for each temperature sensor based on its historical temperature data within a preset time window and the current temperature data of its adjacent sensors; a temperature map generation module for generating a weighted temperature distribution map characterizing the surgical area based on the raw temperature data and the corresponding time-space reliability weight; and a coefficient acquisition module for obtaining a coefficient characterizing the patient's body temperature based on the initial thermal response characteristics of the surgical area. The system includes a thermal inertia coefficient table; a potential function processing module, used to obtain a thermal demand potential for each location point in the weighted temperature distribution map based on the weighted temperature of that location point, a preset target temperature, and the thermal inertia coefficient; a region aggregation module, used to divide the surgical area into multiple heating sub-regions, and for each heating sub-region, use to obtain a region aggregated thermal demand value based on the thermal demand potential of all locations within that heating sub-region; and a power allocation module, used to allocate a heating power to each heating sub-region based on the proportion of the region aggregated thermal demand value of each heating sub-region to the sum of the aggregated thermal demand values ​​of all heating sub-regions.

[0144] In some embodiments, a power correction module is further included, which is used to obtain a cross-regional thermal influence matrix characterizing the heat interaction between different heating sub-regions, construct and solve a set of power correction equations with the heating power to be corrected in each heating sub-region as variables, the cross-regional thermal influence matrix as coefficients, and the target thermal response of each heating sub-region as constants, so as to obtain a set of corrected heating powers, and use the corrected heating powers to replace the allocated heating power.

[0145] In addition, a security monitoring module may be included to perform the security protection functions described in S1010 and S1020.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Depending on actual needs, some or all of the units can be selected to achieve the purpose of this embodiment.

[0150] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware.

[0151] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for multi-region temperature sensing and adaptive thermal radiation control based on a shadowless lamp platform, characterized in that, include: The raw temperature data output by an infrared temperature sensor array covering the surgical area is collected. The infrared temperature sensor array includes multiple temperature sensors. For each of the temperature sensors, a time-space reliability weight is obtained based on its historical temperature data within a preset time window and the current temperature data of its adjacent sensors. Based on the original temperature data and the corresponding time-space reliability weights, a weighted temperature distribution map characterizing the surgical area is generated. Based on the initial thermal response characteristics of the surgical area, a thermal inertia coefficient characterizing the patient's body surface tissue is obtained. For each location point in the weighted temperature distribution map, a thermal demand potential is obtained based on the weighted temperature of that location point, the preset target temperature, and the thermal inertia coefficient. The surgical area is divided into multiple heating sub-regions, and for each heating sub-region, a region aggregated heat demand value is obtained based on the heat demand potential of all locations within that heating sub-region. Based on the proportion of the regional polymerization heat demand value of each heating sub-region to the total polymerization heat demand value of all heating sub-regions, a heating power is allocated to each heating sub-region.

2. The method according to claim 1, characterized in that, The step of obtaining a time-space reliability weight includes: Obtain the historical temperature change rate of the temperature sensor, and obtain the current temperature difference between the temperature sensor and each of its adjacent sensors; A time reliability factor is generated based on the comparison between the historical temperature change rate and the preset physical temperature change rate threshold. Based on the comparison between the current temperature difference and the preset spatial temperature gradient threshold, a spatial reliability factor is generated. The time reliability factor and the space reliability factor are combined to obtain the time-space reliability weight.

3. The method according to claim 1, characterized in that, The step of obtaining a thermal inertia coefficient characterizing the patient's body surface tissue includes: At the beginning of the control phase, a calibration thermal pulse with a preset power and duration is applied to a preset calibration area; Collect temperature change data of the calibration area before and after the calibration thermal pulse; The thermal inertia coefficient is obtained based on the power and duration of the calibration thermal pulse and the temperature change data.

4. The method according to claim 1, characterized in that, The step of obtaining a thermal demand potential is also based on a historical heat flow accumulation associated with the location point; the historical heat flow accumulation represents the total thermal radiation energy applied to the heating sub-region where the location point is located over a preset period of time.

5. The method according to claim 1, characterized in that, After assigning a heating power to each of the heating sub-regions, the method further includes: Obtain a cross-regional thermodynamic influence matrix that characterizes the thermal interaction between different heating sub-regions; Construct a set of power correction equations with the heating power to be corrected in each heating sub-region as variables, the cross-regional thermal influence matrix as coefficients, and the target thermal response of each heating sub-region as constants; Solving the power correction equations yields a set of corrected heating powers; The allocated heating power is replaced with the corrected heating power.

6. The method according to claim 5, characterized in that, The method further includes: A preset micro-perturbation power pulse is periodically applied to a single heating sub-region; Collect temperature response data of itself and other heated sub-regions caused by the micro-perturbation power pulse; Based on the deviation between the temperature response data and the theoretical temperature response predicted by the cross-regional thermal influence matrix, the elements of the cross-regional thermal influence matrix are updated.

7. The method according to claim 1, characterized in that, The method further includes: Obtain the real-time distance between the shadowless lamp head and the surface of the surgical area; Obtain a power attenuation factor that characterizes the relationship between the safety distance and the upper limit of heating power; Based on the real-time distance and the power attenuation factor, adjust the upper limit of the total power that can be allocated to all heating sub-regions.

8. The method according to claim 1, characterized in that, The method further includes: The total output energy of each of the heating sub-regions is accumulated in real time. When the total output energy of any of the heating sub-regions reaches a preset energy safety threshold, heating of that heating sub-region is stopped.

9. A multi-region temperature sensing and adaptive thermal radiation control system based on a shadowless lamp platform, characterized in that, include: The data acquisition module is used to acquire raw temperature data output by an infrared temperature sensor array covering the surgical area. The infrared temperature sensor array includes multiple temperature sensors. The weight generation module is used to obtain a time-space reliability weight for each of the temperature sensors based on its historical temperature data within a preset time window and the current temperature data of its adjacent sensors. The temperature map generation module is used to generate a weighted temperature distribution map characterizing the surgical area based on the original temperature data and the corresponding time-space reliability weights. The coefficient acquisition module is used to acquire a thermal inertia coefficient characterizing the patient's body surface tissue based on the initial thermal response characteristics of the surgical area. The potential function processing module is used to obtain a thermal demand potential for each location point in the weighted temperature distribution map based on the weighted temperature of that location point, the preset target temperature, and the thermal inertia coefficient. The region aggregation module is used to divide the surgical area into multiple heating sub-regions, and for each heating sub-region, obtain a region aggregation thermal demand value based on the thermal demand potential of all locations within that heating sub-region. The power allocation module is used to allocate a heating power to each of the heating sub-regions based on the proportion of the regional aggregated heat demand value of each heating sub-region to the total aggregated heat demand value of all heating sub-regions.

10. The system according to claim 9, characterized in that, Also includes: The power correction module is used to obtain a cross-regional thermodynamic influence matrix that characterizes the heat interaction between different heating sub-regions, construct and solve a set of power correction equations with the heating power to be corrected in each heating sub-region as variables, the cross-regional thermodynamic influence matrix as coefficients, and the target thermodynamic response of each heating sub-region as constants, so as to obtain a set of corrected heating power, and use the corrected heating power to replace the allocated heating power.

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