Intelligent ward digital management method and system based on digital twinning

Abstract

The embodiment of the invention provides an intelligent ward digital management method and system based on digital twinning. A ward digital twinborn body is constructed, a sensor array is used for collecting sickbed area heat radiation distribution, operation area airflow mode and medical equipment energy pulse data in real time, and field intensity superposition is carried out on airflow data in combination with a nursing track to form a dynamic airflow model. An energy pulse phase abrupt change point and a thermal radiation attenuation curve are analyzed by establishing an energy interference spectrum, and a negentropy compensation strategy is generated. When a medical equipment start-stop event is detected, topological distortion caused by a space overlapping area of dynamic airflow and a transfer path is predicted, vortex density distribution is reconstructed, a dynamic balance threshold value of an entropy increase gradient and a negentropy shunting rate is calculated based on updated parameters, and finally an intelligent regulation and control instruction set of a thermal stability coefficient and airflow purification efficiency is output. According to the technical scheme provided by the embodiment of the invention, the ward environment regulation and control precision and response speed are remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of digital management, and in particular, to a digital management method and system for a smart ward based on digital twin. Background Art

[0002] With the development of smart healthcare, hospital wards need to achieve dynamic monitoring and precise regulation of environmental parameters to ensure patient comfort, reduce the risk of cross-infection, and improve the operating efficiency of medical equipment. Specific requirements include multi-dimensional data fusion: real-time synchronous acquisition of multi-source heterogeneous data such as thermal radiation distribution (monitoring patient body temperature and posture changes), airflow mode (controlling the air purification path), and equipment energy pulses (ensuring the stability of equipment start and stop); dynamic environment modeling: establishing a three-dimensional space model to reflect the dynamic interaction effect of thermal radiation and airflow, and predicting environmental disturbances caused by medical operations (such as equipment start and stop, patient transfer); active compensation regulation: quickly generating thermal radiation refocusing and airflow diversion strategies under complex energy disturbances (such as equipment start and stop pulses, sudden changes in airflow caused by personnel movement) to maintain environmental thermodynamic stability.

[0003] The current mainstream ward environment management solutions include two categories. Traditional sensor network systems: Use independently deployed temperature, humidity, and PM2.5 sensors to trigger local ventilation or air conditioning adjustment based on thresholds, but lack the ability of multi-parameter collaborative analysis and spatial field modeling; Basic digital twin systems: Visualize the ward layout through three-dimensional modeling, integrate part of the sensor data to achieve static environment monitoring, but are insufficient in responding to dynamic scenarios such as thermal radiation-airflow coupling effects and energy pulse disturbances, and the regulation depends on manual experience.

[0004] However, the existing solutions still have the following defects: data fragmentation and response lag: Traditional systems cannot integrate the spatio-temporal correlation of thermal radiation, airflow, and energy pulse data, resulting in contradictions between local regulation and global imbalance. For example, the thermal radiation attenuation and airflow disorder caused by equipment start and stop cannot be compensated synergistically; Lack of dynamic modeling: Existing digital twin models are mostly based on static topologies and are difficult to predict the dynamic effects of medical behaviors (such as transfer paths) on the distribution of airflow vortex density and the gradient of thermal radiation entropy increase, resulting in ineffective regulation under sudden disturbances; Coarse-grained compensation strategy: Existing solutions use fixed thresholds or rule bases to generate regulation instructions, and cannot dynamically adjust negative entropy compensation parameters (such as diversion ratio, refocusing intensity) according to the phase mutation points and attenuation curves of the energy interference map, which is likely to cause resource waste or overshoot in regulation. Summary of the Invention

[0005] The embodiments of the present application provide a digital management method and system for a smart ward based on digital twin to solve the problem in the prior art that it is difficult to predict the dynamic effects of medical behaviors on the distribution of airflow vortex density and the gradient of thermal radiation entropy increase.

[0006] In a first aspect, an embodiment of the present application provides a digital management method for a smart ward based on digital twins, including:

[0007] Construct a ward digital twin, and synchronously collect the thermal radiation distribution data in the hospital bed area, the air flow modal data in the operation area, and the energy pulse data of medical devices through a sensor array;

[0008] Perform spatial convolution on the thermal radiation distribution data and the thermodynamic characteristics of the patient's posture to generate a three-dimensional radiation topology. At the same time, perform field strength superposition on the air flow modal data and the fluid characteristics of the nursing trajectory to form a dynamic air flow model;

[0009] Establish an energy interference map based on the three-dimensional radiation topology and the dynamic air flow model, analyze the phase mutation points of the energy pulse data and the thermal radiation attenuation curve, and generate a negative entropy compensation strategy including the air flow energy shunt ratio and the thermal radiation refocusing parameter;

[0010] Embed a medical behavior response mechanism in the digital twin. When a device start / stop event is detected, predict the topological distortion according to the spatial overlap area between the transfer path and the dynamic air flow model, and reconstruct the vortex density distribution of the air flow model;

[0011] Based on the updated energy interference map, the vortex density distribution, and the negative entropy compensation strategy, calculate the dynamic balance threshold of the thermal radiation entropy increase gradient and the negative entropy shunt rate, and generate a regulation instruction set including the thermal stability coefficient and the air flow purification efficiency.

[0012] Optionally, establishing an energy interference map based on the three-dimensional radiation topology and the dynamic air flow model, analyzing the phase mutation points of the energy pulse data and the thermal radiation attenuation curve, and generating a negative entropy compensation strategy including the air flow energy shunt ratio and the thermal radiation refocusing parameter, including:

[0013] Couple the node energy level distribution of the three-dimensional radiation topology with the field strength gradient of the dynamic air flow model in space to establish a multi-layer interference channel of the energy interference map;

[0014] Analyze the phase mutation points of the energy pulse data in the energy interference map, and generate an energy transition path of the phase mutation points by correlating the fluctuation frequency of the thermal radiation attenuation curve with the energy transition direction of the phase mutation points;

[0015] Perform energy level segmentation on the thermal radiation attenuation curve based on the energy transition path, extract the air flow energy shunt ratio thresholds in the dynamic air flow model that match different energy level segments, and synchronously calculate the energy compensation amount of the thermal radiation refocusing parameter in the three-dimensional radiation topology;

[0016] According to the dynamic matching relationship between the air flow energy shunt ratio threshold and the energy compensation amount, generate a negative entropy compensation strategy including the air flow energy shunt ratio and the thermal radiation refocusing parameter in the energy interference map, and map the energy transition path to the execution priority of the negative entropy compensation strategy.

[0017] Optionally, based on the energy transition path, perform energy level segmentation on the thermal radiation attenuation curve, extract the air flow energy shunt ratio thresholds in the dynamic air flow model that match different energy level segments, and synchronously calculate the energy compensation amount of the thermal radiation refocusing parameter in the three-dimensional radiation topology, including:

[0018] According to the transition direction and amplitude of the energy transition path, divide the thermal radiation attenuation curve into multiple energy level segments, and generate a segment identifier for each energy level segment that includes the range of field strength gradient;

[0019] Based on the segment identifier, traverse the field strength gradient distribution of the dynamic air flow model, extract the energy fluctuation amplitudes in the overlapping regions of the field strength gradient ranges of each energy level segment, and generate a set of air flow energy shunt ratio thresholds;

[0020] Combine the thermal radiation attenuation fluctuation amplitude of the energy level segment and the node energy level distribution density of the three-dimensional radiation topology, calculate the energy compensation amount corresponding to each energy level segment for the thermal radiation refocusing parameter, and form a compensation amount sequence;

[0021] Align the set of air flow energy shunt ratio thresholds and the compensation amount sequence formed by the energy compensation amount according to the segment identifier, generate a dynamic matching table including the gradient distribution of the energy compensation amount and the correlation relationship of the shunt ratio threshold, and update the regulation parameters of the negative entropy compensation strategy based on the dynamic matching table.

[0022] Optionally, according to the dynamic matching relationship between the air flow energy shunt ratio threshold and the energy compensation amount, generate a negative entropy compensation strategy including the air flow energy shunt ratio and the thermal radiation refocusing parameter in the energy interference map, and map the energy transition path to the execution priority of the negative entropy compensation strategy, including:

[0023] Based on the spatial distribution characteristics of the dynamic matching relationship, calculate the coupling strength between the air flow energy shunt ratio threshold and the energy compensation amount of the thermal radiation refocusing parameter, and generate a dynamic matching matrix for the interference channel level in the energy interference map;

[0024] According to the coupling strength distribution of the dynamic matching matrix, divide the regulation levels of the shunt ratio threshold and the refocusing parameter in the negative entropy compensation strategy, and assign an energy compensation path corresponding to the energy level segment of the thermal radiation attenuation curve to each regulation level;

[0025] Convert the transition direction and amplitude of the energy transition path into an execution priority sequence of the negative entropy compensation strategy, and generate an execution priority coefficient by superimposing the coupling strength of the dynamic matching matrix and the compensation efficiency of the energy compensation path;

[0026] Based on the corresponding relationship between the regulation level and the execution priority coefficient, construct a negative entropy compensation strategy in the energy interference map, and map the execution priority sequence to the vortex density distribution reconstruction strategy of the dynamic air flow model.

[0027] Optionally, align the compensation amount sequence formed by the air flow energy shunt ratio threshold set and the energy compensation amount according to the segmentation identifier, generate a dynamic matching table including the gradient distribution of the energy compensation amount and the correlation relationship of the shunt ratio threshold, and update the regulation parameters of the negative entropy compensation strategy based on the dynamic matching table, including:

[0028] Based on the field strength gradient range of the segmentation identifier, align the compensation amount sequence formed by the air flow energy shunt ratio threshold set and the energy compensation amount in the spatial dimension, and generate a three-dimensional mapping relationship including the energy level segmentation boundary;

[0029] According to the energy compensation amount gradient distribution in the three-dimensional mapping relationship, perform compensation gradient superposition on each segmentation identifier association area of the dynamic matching table to form a compensation gradient mapping relationship;

[0030] Extract the energy fluctuation amplitude overlapping with the compensation gradient mapping relationship in the shunt ratio threshold set to generate a dynamic matching table including the gradient distribution of the energy compensation amount and the correlation relationship of the shunt ratio threshold;

[0031] Based on the dynamic matching table, hierarchically update the regulation parameters of the negative entropy compensation strategy, generate a regulation parameter sequence matching the energy level segmentation boundary, and synchronize the compensation gradient mapping relationship to the vortex density distribution reconstruction strategy of the dynamic air flow model.

[0032] Optionally, combine the thermal radiation attenuation fluctuation amplitude of the energy level segmentation and the node energy level distribution density of the three-dimensional radiation topology, calculate the energy compensation amount of the thermal radiation refocusing parameter corresponding to each energy level segmentation, and form a compensation amount sequence, including:

[0033] Based on the thermal radiation attenuation fluctuation amplitude of the energy level segmentation, extract the spatial gradient characteristics of the node energy level distribution density in the three-dimensional radiation topology to generate an energy attenuation compensation baseline for each energy level segmentation;

[0034] According to the superposition relationship between the energy attenuation compensation baseline and the field strength gradient distribution in the dynamic air flow model, calculate the energy compensation amount of the thermal radiation refocusing parameter within the energy level segmentation to generate an initial compensation amount sequence;

[0035] Modify the path weights of the initial compensation value sequence according to the transition direction of the energy transition path to generate an energy compensation value correction that matches the energy level segmentation boundary;

[0036] Bind the energy compensation value correction to the shunt ratio threshold set according to the segmentation identifier to form a compensation value sequence containing the compensation gradient distribution of the thermal radiation refocusing parameters, and synchronize it to the regulation parameter update process of the dynamic matching table.

[0037] Optionally, based on the correspondence between the regulation level and the execution priority coefficient, construct a negative entropy compensation strategy in the energy interference spectrum, and map the execution priority sequence to the vortex density distribution reconstruction strategy of the dynamic airflow model, including:

[0038] According to the binding relationship between the shunt ratio threshold and the energy compensation path in the regulation level, sort the energy compensation path priorities of the interference channel levels in the energy interference spectrum to generate a negative entropy compensation strategy;

[0039] Based on the distribution characteristics of the execution priority coefficient, perform spatial matching between the energy compensation paths at different levels in the negative entropy compensation strategy and the vortex density distribution regions of the dynamic airflow model;

[0040] Combine the transition direction of the energy transition path with the energy compensation path priority of the regulation level to calculate the execution timing weights of different regions in the vortex density distribution reconstruction strategy;

[0041] According to the correspondence between the execution timing weights and the negative entropy compensation strategy, generate a vortex density distribution reconstruction instruction sequence that synchronously responds to the negative entropy compensation strategy in the dynamic airflow model.

[0042] In a second aspect, an embodiment of the present application provides a digital management system for a smart ward based on digital twin, including:

[0043] A construction module for constructing a ward digital twin, and synchronously collecting thermal radiation distribution data in the hospital bed area, airflow modal data in the operation area, and energy pulse data of medical equipment through a sensor array;

[0044] A convolution module for spatially convolving the thermal radiation distribution data with the thermodynamic characteristics of the patient's posture to generate a three-dimensional radiation topology, and at the same time performing field strength superposition on the airflow modal data and the fluid characteristics of the nursing trajectory to form a dynamic airflow model;

[0045] An analysis module, configured to establish an energy interference map based on a three-dimensional radiation topology and a dynamic air flow model, analyze phase mutation points of energy pulse data and a thermal radiation attenuation curve, and generate a negative entropy compensation strategy including an air flow energy shunt ratio and a thermal radiation refocusing parameter;

[0046] A prediction module, configured to embed a medical behavior response mechanism in a digital twin. When a device start / stop event is detected, predict topological distortion according to an overlapping area of a transfer path and a dynamic air flow model in space, and reconstruct a vortex density distribution of the air flow model;

[0047] A calculation module, configured to calculate a dynamic balance threshold of a thermal radiation entropy increase gradient and a negative entropy shunt rate based on the updated energy interference map, the vortex density distribution, and the negative entropy compensation strategy, and generate a regulation instruction set including a thermal stability coefficient and an air flow purification efficiency.

[0048] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a digital management method for a smart ward based on digital twin as described in the first aspect above.

[0049] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a digital management method for a smart ward based on digital twin as described in the first aspect.

[0050] In the embodiment of the present application, a digital twin of a ward is constructed. Heat radiation distribution data in a hospital bed area, air flow modal data in an operation area, and energy pulse data of medical devices are synchronously collected through a sensor array; the heat radiation distribution data is spatially convolved with the thermodynamic characteristics of the patient's posture to generate a three-dimensional radiation topology, and at the same time, the air flow modal data is field-strength superposed with the fluid characteristics of the nursing trajectory to form a dynamic air flow model; an energy interference map is established based on the three-dimensional radiation topology and the dynamic air flow model, phase mutation points of energy pulse data and a thermal radiation attenuation curve are analyzed, and a negative entropy compensation strategy including an air flow energy shunt ratio and a thermal radiation refocusing parameter is generated; a medical behavior response mechanism is embedded in the digital twin. When a device start / stop event is detected, topological distortion is predicted according to an overlapping area of a transfer path and a dynamic air flow model in space, and a vortex density distribution of the air flow model is reconstructed; based on the updated energy interference map, the vortex density distribution, and the negative entropy compensation strategy, a dynamic balance threshold of a thermal radiation entropy increase gradient and a negative entropy shunt rate is calculated, and a regulation instruction set including a thermal stability coefficient and an air flow purification efficiency is generated.

[0051] The technical solution of the present application has the following beneficial effects:

[0052] By constructing a digital twin of the ward and integrating multi-source sensor data (thermal radiation, air flow, energy pulses), a three-dimensional radiation topology and air flow model are dynamically generated. By combining the energy interference spectrum to analyze the fluctuations of environmental parameters, the interference of medical device start-stop events on the ward environment is predicted in real time. Through the negative entropy compensation strategy, the re-focusing of thermal radiation and the energy shunt ratio of air flow are actively regulated, and finally an intelligent instruction set for thermal stability and air flow purification is generated. The dynamic balance and precise regulation of ward environmental parameters are achieved, reducing the health risks of patients caused by uneven thermal radiation or air flow disorders, improving the operating stability of medical devices and the efficiency of nursing operations, and at the same time reducing the interference of environmental mutations on the treatment process through predictive intervention.

[0053] Furthermore, by coupling the node energy levels of the three-dimensional radiation topology with the dynamic air flow field strength gradient to form multi-layer interference channels, the phase mutation points of energy pulses and the thermal radiation attenuation curve are analyzed. By dividing the energy levels according to the transition path, the shunt ratio threshold and the energy compensation amount of the re-focusing parameters are extracted, and a negative entropy compensation strategy with hierarchical execution priorities is generated based on the dynamic matching relationship. Through the refined association of the energy transition path and the energy level segmentation, the dynamic adaptation and priority optimization of the compensation strategy are realized, enhancing the rapid response ability to sudden energy fluctuations (such as device start-stop), avoiding the out-of-control of local environmental parameters, and at the same time improving the resource allocation efficiency of negative entropy compensation.

[0054] Furthermore, an energy attenuation compensation baseline is generated through the thermal radiation attenuation amplitude and the node energy level density. The re-focusing parameter compensation amount is calculated by combining the superposition relationship of the field strength gradient of the dynamic air flow. The shunt threshold and the compensation sequence are aligned through the segment identifier, and a dynamic matching table is constructed to update the regulation parameters. Based on the spatial alignment of the segment identifier and the compensation gradient mapping, the precise matching of thermal radiation attenuation and air flow energy shunt is realized, significantly improving the local adaptability of the compensation strategy and the parameter update efficiency, avoiding the waste of resources caused by global regulation, and at the same time enhancing the robustness of the system to complex environmental changes through the dynamic matching table.

[0055] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0057] Figure 1 The flowchart of a digital management method for a smart ward based on digital twin provided by the present application is shown;

[0058] Figure 2 Shows a schematic structural diagram of a digital management system for a smart ward based on digital twin provided by this application;

[0059] Figure 3 Shows a schematic structural diagram of a computing device provided by this application. Detailed implementation manners

[0060] In order to enable the personnel in the technical field to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application.

[0061] In some processes described in the specification, claims and the above-mentioned accompanying drawings of this application, a plurality of operations that appear in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., do not represent the sequence, and do not limit that "first" and "second" are different types.

[0062] The smart ward needs to achieve dynamic environmental balance. However, the traditional solution relies on static monitoring and manual regulation, and it is difficult to cope with the heat radiation and sudden changes in air flow caused by medical behaviors (such as equipment start / stop, patient transfer). This solution is based on digital twin technology, and a closed-loop regulation system is constructed through multi-source data fusion, dynamic modeling and negative entropy compensation mechanism. First, multi-dimensional data of the ward (heat radiation, air flow, equipment energy consumption) is collected and mapped into a three-dimensional topology and dynamic model; then the energy interference effect is analyzed to generate an adaptive compensation strategy; finally, combined with the prediction of medical behavior interference, the regulation instructions are updated in real time to achieve the maximum of thermodynamic stability and air flow purification efficiency.

[0063] The technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0064] Figure 1 Is a flowchart of a digital management method for a smart ward based on digital twin provided by an embodiment of this application. As Figure 1 shown, this method includes:

[0065] 101. Build a digital twin of the ward, and synchronously collect the thermal radiation distribution data in the hospital bed area, the air flow modal data in the operation area, and the energy pulse data of medical equipment through a sensor array;

[0066] In this step, the digital twin of the ward refers to the digital mapping of the physical space and logical relationship of the ward constructed through virtual modeling technology, including the geometric and physical attributes of the hospital bed area, the operation area, and the equipment layout. The sensor array includes:

[0067] Thermal radiation distribution data: Collect the surface temperature distribution of the hospital bed area through an infrared thermal imaging sensor, which is used to monitor the patient's body temperature and posture changes;

[0068] Air flow modal data: Capture the air flow velocity, direction, and vortex characteristics in the operation area through an ultrasonic anemometer and a particle image velocimeter (PIV), which is used to analyze the air purification path;

[0069] Energy pulse data: Collect the transient energy consumption and electromagnetic interference when medical equipment (such as ventilators, CT machines) starts and stops through a current sensor and an electromagnetic field probe, which is used to evaluate the operation stability of the equipment.

[0070] In the embodiment of the present application, deploy an infrared thermal imager array (resolution 0.1°C) on the top of the hospital bed area to generate a real-time thermal radiation distribution map of the patient's body surface; embed an ultrasonic anemometer grid (sampling frequency 10Hz) in the ceiling of the operation area to synchronously record the air flow vector data; install a high-precision current sensor (accuracy ±0.5%) at the power interface of the medical equipment to capture the energy pulse waveform. Transmit the three types of data to the digital twin database in real time through the Internet of Things protocol (such as MQTT), and mark the space-time stamp to achieve multi-source data synchronization and alignment.

[0071] Taking the ICU ward as an example, when the patient turns over and causes a change in the thermal radiation distribution, the infrared thermal imager detects that the temperature in the leg area drops by 2°C, and simultaneously triggers the ultrasonic anemometer to record that the air flow velocity in the operation area increases from 0.3m / s to 0.8m / s. At the same time, the ventilator starts to generate an energy pulse peak (the current jumps from 5A to 12A). The digital twin associates and stores these three types of data, providing input for subsequent modeling.

[0072] 102. Perform spatial convolution on the thermal radiation distribution data and the thermodynamic characteristics of the patient's posture to generate a three-dimensional radiation topology, and at the same time perform field strength superposition on the air flow modal data and the fluid characteristics of the nursing trajectory to form a dynamic air flow model;

[0073] In this step, the three-dimensional radiation topology refers to a three-dimensional temperature field model generated by fusing thermal radiation data with the thermodynamic characteristics of the patient's posture (such as body surface curvature, heat conduction coefficient) through a spatial convolution algorithm; the dynamic airflow model refers to a time-varying hydrodynamic field model formed by superimposing airflow modal data and the care trajectory (such as the movement path of medical staff, the operation range of instruments), which is used to predict the propagation law of airflow disturbance.

[0074] In the embodiment of the present application, a deep learning framework (such as 3D CNN) is used to perform spatial convolution on thermal radiation data: input the thermal imaging map and the three-dimensional scan model of the patient's lying posture (obtained by a ToF camera), and output a three-dimensional radiation topology with body surface curvature correction (accuracy ±0.5°C). At the same time, the airflow modal data and the care trajectory (tracked by UWB positioning tags) are input into the computational fluid dynamics (CFD) simulation engine, and the airflow field strength distribution is dynamically corrected based on the Navier-Stokes equation to generate a dynamic airflow model with vortex density gradient.

[0075] When the caregiver's cart passes by the hospital bed, the UWB positioning system records its movement trajectory (speed 0.5m / s, path length 3m), and the CFD model predicts that a low-pressure vortex area (vortex density increases by 15%) will be formed behind the cart by the airflow based on this, and this result is superimposed on the initial airflow field. At the same time, the patient's lateral lying position causes an increase in the thermal radiation intensity on the back, and the 3D CNN model marks this area as a "heat accumulation risk area" in the radiation topology.

[0076] 103. Establish an energy interference map based on the three-dimensional radiation topology and the dynamic airflow model, analyze the phase mutation points of the energy pulse data and the thermal radiation attenuation curve, and generate a negative entropy compensation strategy including the airflow energy shunt ratio and the thermal radiation refocusing parameters;

[0077] In this step, the energy interference map refers to an energy interaction network formed by coupling the node energy levels of the three-dimensional radiation topology and the field strength gradient of the dynamic airflow, which is used to quantify the correlation between thermal radiation attenuation and airflow energy shunt; the negative entropy compensation strategy refers to dynamically adjusting the airflow shunt ratio (such as the air volume distribution of the purification system) and the thermal radiation refocusing parameters (such as the local heating / cooling intensity) by analyzing the phase mutation points of the energy pulse (such as the instant of equipment start / stop) and the thermal radiation attenuation curve to offset the environmental entropy increase.

[0078] In the embodiments of the present application, the node energy levels (temperature values) of the three-dimensional radiation topology are mapped to the "heat source layer" of the interference pattern, and the field strength gradient of the dynamic airflow model is mapped to the "airflow layer". The energy coupling coefficient between the two layers is calculated by a multi-layer perceptron (MLP). When the CT machine starts to generate an energy pulse, the fast Fourier transform (FFT) identifies its phase mutation point (such as a 200% increase in current within 0.1 s), correlates with the thermal radiation attenuation curve (such as a 0.8 °C / s decrease in the temperature of adjacent hospital beds), and the MLP generates a flow diversion ratio threshold (such as directing 40% of the purified air flow to the CT operation area) and a refocusing parameter (such as starting an electric blanket in the attenuation area to compensate 5 W / m 2 ).

[0079] When the CT machine starts, the energy interference pattern detects a sudden change in the field strength gradient of the airflow in the operation area (increasing from 2 Pa / m to 8 Pa / m), and at the same time, the thermal radiation intensity of adjacent hospital beds decays at a rate of 0.6 °C / s. The system triggers a negative entropy compensation strategy: adjusting the purification system to direct 30% of the air volume to the CT area to stabilize the airflow, and controlling the intelligent mattress to apply 4 W / m 2 heating power to restore thermal radiation equilibrium within 10 seconds.

[0080] 104. Embed a medical behavior response mechanism in the digital twin. When a device start / stop event is detected, predict topological distortion based on the spatial overlap area between the transfer path and the dynamic airflow model, and reconstruct the vortex density distribution of the airflow model;

[0081] In this step, the medical behavior response mechanism refers to embedding event-driven logic in the digital twin. When a device start / stop or patient transfer is detected, predict its interference with the thermal radiation topology and the airflow model; the reconstruction of the vortex density distribution refers to dynamically adjusting the vortex generation probability and intensity in the CFD model according to the spatial overlap area between the transfer path and the airflow field (such as the intersection point of the trolley path and the air supply duct) to match the actual disturbance.

[0082] In the embodiments of the present application, the status signals of medical devices (such as device start / stop instructions in the DICOM protocol) are monitored through an event bus. When it is detected that the MRI device is shut down, its historical transfer path (such as the 3D trajectory from the equipment room to the corridor) is extracted, and a spatial Boolean operation is performed with the dynamic airflow model to locate the overlap area (such as the overlap area between the path and the air return opening accounts for 15% of the area). Based on this, topological distortion (such as the risk of airflow countercurrent) is predicted, and the CFD solver is called to reconstruct the vortex density distribution (such as correcting the vortex density coefficient in the overlap area from 0.3 to 0.7).

[0083] When transporting the ventilator, the digital twin detects that its moving path overlaps with the main air supply channel, and predicts that turbulence will be generated in the overlapping area (the vortex density increases by 25%). The system reduces the air flow velocity in the overlapping area from 1.2 m / s to 0.8 m / s in advance, and switches the purification mode from "balanced air supply" to "directional steady flow" to avoid aerosol diffusion caused by equipment movement.

[0084] 105. Based on the updated energy interference map, vortex density distribution, and negative entropy compensation strategy, calculate the dynamic balance threshold of the heat radiation entropy increase gradient and the negative entropy diversion rate, and generate a set of control instructions including the thermal stability coefficient and the air flow purification efficiency.

[0085] In this step, the heat radiation entropy increase gradient refers to the rate of disorder of the thermal energy distribution caused by equipment start / stop or personnel activities; the negative entropy diversion rate refers to the efficiency of offsetting entropy increase through air flow purification and heat radiation compensation; the dynamic balance threshold refers to the critical ratio when the two reach a steady state (such as the entropy increase rate ≤ 90% of the diversion rate), which is used to determine whether to trigger the control instructions.

[0086] In the embodiment of the present application, based on the updated energy interference map and vortex density distribution, the Kalman filter is used to predict the entropy increase gradient (such as 0.05 °C / (m 2 ·s)) and the negative entropy diversion rate (such as 0.06 °C / (m 2 ·s)), and calculate the ratio of the two (0.83). If it is lower than the threshold (0.9), then generate control instructions: adjust the air supply temperature of the air conditioner (such as raising it by 1 °C) to reduce the heat radiation attenuation, and at the same time increase the ventilation frequency of the purification system (from 6 times / h to 8 times / h), and output the thermal stability coefficient (0.92) and the air flow purification efficiency (PM2.5 removal rate ≥ 95%) to the monitoring terminal.

[0087] When the start / stop of multiple devices at the same time causes the entropy increase gradient to rise to 0.08 °C / (m 2 ·s), the system calculates the diversion rate to be 0.07 °C / (m 2 ·s), and triggers control instructions: adjust the target temperature of the air conditioner in the ward area from 24 °C to 25.5 °C, and the purification system switches to the "strong effect mode" (the air volume is increased by 40%). After 10 minutes, the thermal stability coefficient returns to 0.89, and the PM2.5 concentration drops from 35 μg / m 3 to 12 μg / m 3 .

[0088] To sum up, in steps 101 to 105, through multi-source data fusion (heat, air flow, energy), dynamic modeling (three-dimensional topology and CFD simulation), and negative entropy compensation mechanism, the closed-loop control of the ward environment is realized:

[0089] Precise perception: Infrared, ultrasonic, and current sensors cooperate to capture the environmental dynamics;

[0090] Intelligent prediction: The energy interference map quantifies the thermal-airflow coupling effect and identifies in advance the interference caused by the start and stop of the equipment;

[0091] Adaptive regulation: Based on the dynamic balance of entropy increase gradient and shunt rate, automatically generate thermal stability and air flow purification instructions;

[0092] Risk suppression: Through the vortex density reconstruction and priority compensation strategy, reduce the risks of cross-infection and equipment overheating. Finally, improve the environmental safety of the ward (thermal radiation uniformity error ≤ 0.3 °C), equipment energy efficiency (energy saving 15% - 20%) and nursing response speed (regulation delay < 2 s).

[0093] In order to further improve the fineness and dynamic adaptability of the negative entropy compensation strategy, in the traditional scheme, the energy interference analysis is only based on the static threshold of a single physical field (such as temperature or air flow), which cannot quantify the coupling effect of thermal radiation and air flow, and the fixed compensation parameters lead to waste of resources. This scheme establishes a hierarchical energy interference map through four steps: spatial coupling, transition path modeling, energy level segmentation and dynamic matching, and converts the spatio-temporal correlation between the phase mutation of the energy pulse and the attenuation of thermal radiation into an executable priority compensation strategy, realizing the accurate positioning of environmental interference and the directional allocation of resources.

[0094] In some embodiments, in step 103, an energy interference map is established based on the three-dimensional radiation topology and the dynamic air flow model, the phase mutation points of the energy pulse data and the thermal radiation attenuation curve are analyzed, and a negative entropy compensation strategy including the air flow energy shunt ratio and the thermal radiation refocusing parameter is generated, including:

[0095] 1031. Spatially couple the node energy level distribution of the three-dimensional radiation topology with the field strength gradient of the dynamic air flow model to establish a multi-layer interference channel of the energy interference map;

[0096] In this step, the multi-layer interference channel refers to the energy interaction network level formed by spatially superimposing the node energy levels (i.e., the temperature values are mapped to energy levels) of the three-dimensional radiation topology and the field strength gradient (i.e., the air flow velocity change rate) of the dynamic air flow model, and is used to characterize the thermal-airflow coupling strength in different regions. The node energy level distribution is obtained through spatial interpolation and normalization processing of the thermal radiation data; the field strength gradient is generated by calculating the spatial derivative of the air flow velocity vector.

[0097] In the embodiments of the present application, a spatial convolution algorithm is used to discretize and layer the node temperature values (such as 25°C to 32°C) of the three-dimensional radiation topology (with each 0.5°C as a layer) to generate a "heat source layer"; at the same time, the field strength gradient (such as 0 to 10 Pa / m) of the dynamic airflow model is equally spaced (with each 2 Pa / m as a layer) to generate an "airflow layer". The two types of layers are spatially coupled through matrix multiplication to form multiple interference channels (for example, the overlapping area between the heat source layer of 25°C - 26°C and the airflow layer of 4 Pa / m - 6 Pa / m is defined as "interference channel C3"), and the energy interaction coefficient of each channel is calculated (such as the coupling coefficient of C3 is 0.78).

[0098] 1032. Analyze the phase mutation points of the energy pulse data in the energy interference map, and generate the energy transition path of the phase mutation points by correlating the fluctuation frequency of the thermal radiation attenuation curve with the energy transition direction of the phase mutation points;

[0099] The phase mutation point refers to the point where the current / electromagnetic field changes violently at the moment of equipment start / stop in the energy pulse data (such as the current jumps from 5 A to 20 A when the CT machine starts), and its frequency mutation characteristics are extracted through fast Fourier transform (FFT); the energy transition path refers to the propagation trajectory of the thermal radiation attenuation fluctuation caused by the phase mutation point in the time - space dimension, and is generated by correlating the fluctuation frequency of the attenuation curve (such as 0.5 Hz to 2 Hz) with the phase mutation direction (positive / negative transition).

[0100] In the embodiments of the present application, FFT analysis is performed on the energy pulse data (sampling rate 1 kHz) to identify the frequency bands (such as 50 Hz to 100 Hz) where the amplitude mutation exceeds the threshold (such as 200%) as the phase mutation points. At the same time, the time derivative of the thermal radiation attenuation curve (such as -0.6°C / s) and its spatial fluctuation frequency (calculated through wavelet transform) are extracted, and the two are input into the LSTM network to predict the energy transition direction (such as spreading from the equipment area to the hospital bed area). Finally, the spatial coordinate sequence of the transition path (such as the path point set {(x1,y1,z1,t1),(x2,y2,z2,t2)...}) and the energy attenuation weight (such as the path point weight 0.3 to 0.9) are generated.

[0101] 1033. Based on the energy transition path, perform energy level segmentation on the thermal radiation attenuation curve, extract the airflow energy shunt ratio thresholds in the dynamic airflow model that match different energy level segments, and synchronously calculate the energy compensation amount of the thermal radiation refocusing parameter in the three-dimensional radiation topology;

[0102] Energy level segmentation means dividing the thermal radiation attenuation curve into multiple intervals (such as high attenuation segment, stable segment, low attenuation segment) according to the amplitude and direction of the energy transition path, and matching corresponding air flow diversion ratio thresholds (such as 50% of the purified air flow needs to be diverted in the high attenuation segment) and thermal radiation refocusing parameters (such as compensation power 8W / m 2 ) for each interval. The energy compensation amount is calculated through the thermodynamic conservation equation and dynamically corrected in combination with the node energy level density and air flow diversion efficiency.

[0103] In the embodiments of the present application, based on the weight distribution of the transition path, the attenuation curve is divided into three segments according to the slope threshold (such as < -0.4℃ / s is the high attenuation segment, -0.4℃ / s to -0.2℃ / s is the medium attenuation segment). The extreme value of the field strength gradient in the corresponding spatial region of the dynamic air flow model is extracted for each segment (such as the field strength gradient in the high attenuation segment ≥ 6Pa / m), and the air flow diversion ratio threshold is calculated through the regression model (such as for every 1Pa / m increase in the field strength gradient, the air flow diversion ratio increases by 5%). The energy compensation amount of each node in the three-dimensional radiation topology is calculated synchronously: based on the heat conduction equation (Q = λΔT / Δx), in combination with the node energy level density (such as when the node density ≥ 0.8 / m 3 , the compensation amount increases by 20%), a compensation power sequence is generated (such as 4W / m 2 ~12W / m 2 ).

[0104] 1034. According to the dynamic matching relationship between the air flow energy diversion ratio threshold and the energy compensation amount, generate a negative entropy compensation strategy including the air flow energy diversion ratio and the thermal radiation refocusing parameter in the energy interference spectrum, and map the energy transition path to the execution priority of the negative entropy compensation strategy.

[0105] The dynamic matching relationship refers to the collaborative constraint conditions of the air flow diversion ratio threshold and the thermal radiation compensation amount in the time - space dimension (such as for every 10% increase in the diversion ratio, the compensation amount needs to be reduced by 2W / m 2 to avoid overshoot); the execution priority is sorted according to the propagation speed (such as 0.5m / s) and attenuation weight (such as 0.7) of the energy transition path, and the path segments with high weight and fast propagation are processed preferentially.

[0106] In the embodiments of the present application, a two - dimensional matrix of diversion ratio - compensation amount is constructed, and the environmental stability index (such as the thermal uniformity error ≤ 0.3℃) of different matching combinations is predicted based on the Kalman filter. The optimal combination (such as diversion ratio 45% + compensation amount 6W / m 2 ) is selected as the core parameter of the negative entropy compensation strategy. At the same time, the transition paths are arranged in descending order of propagation speed (such as path A speed 0.8m / s > path B speed 0.5m / s), and more resources are allocated to the high - priority paths (such as the diversion ratio of path A is increased to 50%).

[0107] The following is a specific example:

[0108] Suppose in an intensive care unit, a high-power medical device suddenly stops running, resulting in abnormal attenuation of thermal radiation and air flow disorder in the surrounding environment. The digital twin system spatially couples the thermal radiation distribution data generated when the device stops operating with the real-time air flow field strength gradient, identifies that the thermal-air flow interaction intensity in the area around the device has increased significantly, and constructs a multi-layer interference channel marked as a high-risk energy coupling area. The system detects the characteristic of phase mutation of the energy pulse at the moment when the device stops operating, correlates the fluctuation frequency and propagation direction of the thermal radiation attenuation in the adjacent hospital bed area, and predicts that the energy transition path diffuses along a specific direction towards the patient activity area. According to the amplitude and direction of the transition path, the thermal radiation attenuation curve is divided into different energy level segments, the extreme values of the air flow field strength gradient in the corresponding areas are extracted, and the air flow shunt ratio threshold and the energy compensation amount required for thermal radiation refocusing are dynamically calculated. Based on the dynamic matching relationship between the shunt ratio threshold and the energy compensation amount, a priority compensation strategy is generated, and resources are preferentially allocated to the path segments with high propagation speed and high influence weight. The system directionally adjusts the purification air flow distribution and starts local thermal compensation to quickly stabilize the environmental parameters.

[0109] Through the hierarchical energy interference analysis mechanism (steps 1031-1034), this solution accurately locates the environmental interference sources and propagation ranges caused by events such as device startup and shutdown and personnel activities through thermal-air flow coupling modeling and phase mutation analysis; based on energy level segmentation and transition path weights, it realizes on-demand resource allocation, avoiding energy consumption waste or local over-compensation caused by "one-size-fits-all" regulation; the priority strategy ensures an immediate response to high-threat interferences (such as rapidly spreading thermal attenuation), significantly reducing the regulation delay; by dynamically matching the shunt ratio and compensation amount, it adaptively cancels the superimposed interferences in complex scenarios (multiple devices starting and stopping concurrently, frequent personnel movement) and maintains environmental stability. Without manual intervention, it realizes the collaborative optimization of the thermal radiation balance and air flow purification efficiency in the ward, providing a high-stability environmental guarantee for patient treatment and medical operations.

[0110] To further improve the dynamic matching accuracy of the energy level segmentation and compensation parameters, through energy level division driven by the transition path, field strength gradient overlap analysis, node density compensation calculation, and dynamic matching table construction, the thermal radiation attenuation characteristics and the air flow shunt ratio are spatially aligned to solve the problem of mismatch between the compensation amount and the scene requirements. In some embodiments, based on the energy transition path, the thermal radiation attenuation curve is divided into energy level segments, the air flow energy shunt ratio thresholds matching different energy level segments in the dynamic air flow model are extracted, and the energy compensation amount of the thermal radiation refocusing parameters in the three-dimensional radiation topology is synchronously calculated, including:

[0111] 2031. Divide the thermal radiation attenuation curve into multiple energy level segments according to the transition direction and amplitude of the energy transition path, and generate a segment identifier containing the range of field strength gradient for each energy level segment;

[0112] In this step, the energy level segment refers to dividing the thermal radiation attenuation curve into multiple continuous or discrete intervals according to the propagation direction (such as spreading from the equipment area to the hospital bed area) and amplitude (such as the temperature attenuation rate) of the energy transition path. Each interval reflects a different energy attenuation intensity level. The segment identifier is a unique code generated based on the range of field strength gradient corresponding to the segment interval (such as low gradient, medium gradient, high gradient), and is used to associate the air flow field strength distribution with the thermal radiation compensation requirement.

[0113] In the embodiment of the present application, a clustering algorithm (such as K-means) is used to jointly analyze the amplitude (temperature attenuation rate) and direction (spatial propagation angle) of the energy transition path, and three types of energy level segments, namely high attenuation, medium attenuation, and low attenuation, are divided. For each type of segment, the extreme value of the dynamic air flow field strength gradient in its covered area is extracted (such as the field strength gradient in the high attenuation segment ≥ a specific threshold), and a segment identifier is generated (such as "S1-High" indicating the area associated with the high attenuation segment and high field strength gradient).

[0114] 2032. Traverse the field strength gradient distribution of the dynamic air flow model based on the segment identifier, extract the energy fluctuation amplitude of the overlapping area with the field strength gradient range of each energy level segment, and generate a set of air flow energy shunt ratio thresholds;

[0115] The air flow energy shunt ratio threshold refers to the lower or upper limit of the air flow energy distribution ratio required to offset the thermal radiation attenuation in different field strength gradient ranges (such as more purified air flow needs to be distributed in the high gradient area). The energy fluctuation amplitude refers to the fluctuation intensity of the air flow velocity or pressure in the overlapping area of the field strength gradient, which is calculated by the standard deviation or peak-to-peak value, and is used to quantify the shunt ratio requirement.

[0116] In the embodiment of the present application, based on the field strength gradient range of the segment identifier, traverse the grid data of the dynamic air flow model, and extract the overlapping area of the field strength gradient of each segment (such as the area with a field strength gradient of 6 Pa / m to 10 Pa / m corresponding to the identifier "S1-High"). Calculate the statistical characteristics of the energy fluctuation amplitude in the overlapping area (such as the median of the fluctuation amplitude), establish a field strength gradient - shunt ratio mapping relationship through a regression model (such as for every 1 Pa / m increase in the gradient, the shunt ratio increases by 4%), and generate a set of shunt ratio thresholds (such as the shunt ratio in the high attenuation segment ≥ 40%).

[0117] 2033. Combine the thermal radiation attenuation fluctuation amplitude of the energy level segment with the node energy level distribution density of the three-dimensional radiation topology, calculate the energy compensation amount of the thermal radiation refocusing parameter corresponding to each energy level segment, and form a compensation amount sequence;

[0118] The node energy level distribution density refers to the comprehensive index of the number and energy intensity of thermal radiation nodes per unit volume in a three-dimensional radiation topology, reflecting the aggregation degree of the local thermal environment; the energy compensation amount refers to the power density applied by heating / cooling equipment to offset the attenuation of thermal radiation. Its calculation needs to combine the node density (higher compensation efficiency is required in high-density areas) and the attenuation fluctuation amplitude (more aggressive compensation is required for high fluctuations).

[0119] In the embodiment of the present application, the attenuation fluctuation amplitude (such as the temperature change rate) of thermal radiation for each energy level segment is normalized, and combined with the node energy level distribution density of the three-dimensional radiation topology (such as node density ≥ 0.5 per m 3 is defined as the high-density area), a compensation amount calculation model is constructed: Compensation amount = basic coefficient × attenuation amplitude × density weight; for example, the compensation amount in the high-attenuation segment (attenuation amplitude coefficient 1.2) and the high-density area (density weight 1.5) is 1.8 times the basic value. Finally, a compensation amount sequence corresponding one-to-one to the energy level segments is generated.

[0120] 2034. Align the set of air flow energy shunt ratio thresholds and the compensation amount sequence formed by the energy compensation amount according to the segment identifier to generate a dynamic matching table including the gradient distribution of the energy compensation amount and the associated relationship of the shunt ratio threshold, and update the regulation parameters of the negative entropy compensation strategy based on the dynamic matching table.

[0121] The dynamic matching table refers to a two-dimensional relationship table formed by aligning the set of shunt ratio thresholds and the compensation amount sequence according to the segment identifier, which is used to describe the coordination rules of air flow shunting and thermal compensation under different energy level segments (such as a shunt ratio of 40% corresponding to a compensation amount of 8 W / m 2 ). The update of the regulation parameters refers to dynamically adjusting the execution parameters of the negative entropy compensation strategy according to the matching table to adapt to the real-time environmental changes.

[0122] In the embodiment of the present application, the set of shunt ratio thresholds and the compensation amount sequence are associated according to the segment identifier (such as "S1-High", "S2-Medium") to generate a dynamic matching table including the following fields:

[0123] Segment identifier;

[0124] Range of shunt ratio thresholds (such as 35% - 45%);

[0125] Compensation amount gradient (such as 7 W / m 2 - 9 W / m 2 );

[0126] Priority weight (calculated according to the attenuation amplitude and propagation speed);

[0127] Based on this table, the control parameter combinations of the negative entropy compensation strategy are optimized using a reinforcement learning algorithm. For example, in the overlapping conflict area (such as when multiple segmentation identifiers cover the same space), the parameters with the highest comprehensive weight are selected for execution.

[0128] The following is a specific example:

[0129] Suppose in an operating room, when a high-frequency electrosurgical unit is used, it causes a sudden drop in local thermal radiation and at the same time leads to a reverse flow of the air current in the operation area. The system divides the thermal radiation attenuation curve into three energy level segments: high, medium, and low, according to the amplitude (rapid decay) and direction (diffusing from the operation point of the electrosurgical unit to the surrounding) of the energy transition path, and generates segmentation identifiers (such as "S1-High" corresponding to a field strength gradient of 5 Pa / m to 8 Pa / m). Traverse the dynamic air current model, extract the overlapping area of the field strength gradient of the "S1-High" segment, calculate the amplitude of its energy fluctuation, and determine that at least 38% of the air current energy needs to be allocated to this area for shunt stabilization. Combining the fluctuation amplitude of the high attenuation segment with the node density (high density area) in the radiation topology, calculate that a thermal compensation power of 7.5 W / m 2 needs to be applied in the corresponding area. Bind the shunt ratio of 38% and the compensation amount of 7.5 W / m 2 to the "S1-High" identifier and update the dynamic matching table. The system preferentially starts directional air current shunt and thermal compensation for this area, and restores thermal equilibrium and suppresses reverse flow within 5 seconds.

[0130] Through the above steps, based on the dynamic division of the transition path direction and amplitude, this solution avoids under-compensation or overloading caused by traditional fixed thresholds; through the overlapping analysis of the field strength gradient and the gradient mapping of the compensation amount, it improves the coordination efficiency of air current-thermal radiation regulation; the dynamic matching table realizes the precise adaptation of the shunt ratio and the compensation amount, reducing ineffective energy consumption (such as global air supply or overheating); the priority weight mechanism solves the control conflicts in the multi-segment identifier area, ensuring priority compensation for key areas; the policy parameter iteration driven by reinforcement learning enhances the adaptability of the system to complex interferences (such as concurrent start and stop of equipment, personnel flow). Through the energy level segmentation aligned in space and the dynamic matching table, it realizes the rapid positioning of thermal-air current coupling interference and the optimal allocation of resources, providing high-precision environmental guarantee for medical operations.

[0131] To optimize the timing coordination and resource allocation efficiency of the compensation strategy, through the coupling strength quantization matrix, regulation level division, priority coefficient calculation, and cross-model instruction mapping, hierarchical response and spatio-temporal coordination of thermal compensation and air current vortex control are realized. In some embodiments, according to the dynamic matching relationship between the air current energy shunt ratio threshold and the energy compensation amount, a negative entropy compensation strategy including the air current energy shunt ratio and the thermal radiation refocusing parameter is generated in the energy interference map, and the energy transition path is mapped to the execution priority of the negative entropy compensation strategy, including:

[0132] 3031. Calculate the coupling strength between the air flow energy shunt ratio threshold and the energy compensation amount of the thermal radiation refocusing parameter based on the spatial distribution characteristics of the dynamic matching relationship, and generate a dynamic matching matrix for the interference channel level in the energy interference map;

[0133] In this step, the dynamic matching matrix refers to a multi-dimensional matrix formed based on the spatial coupling strength (reflecting the efficiency of the two cooperating to offset the entropy increase) between the air flow energy shunt ratio threshold and the energy compensation amount of the thermal radiation refocusing parameter. The coupling strength is obtained by calculating the energy interaction efficiency of the two in the spatial overlap region (such as the change rate of the compensation amount corresponding to each unit increase in the shunt ratio).

[0134] In the embodiment of the present application, a spatial convolution kernel is used to traverse the interference channel level of the energy interference map, and a correlation analysis is performed on the shunt ratio threshold and the compensation amount in each channel. For example, if the compensation amount requirement decreases significantly when the shunt ratio in a certain channel increases, it is determined that its coupling strength is "high" and mapped to a high weight value in the matrix. Finally, a dynamic matching matrix including the channel level, coupling strength, and spatial coordinates is generated.

[0135] 3032. Divide the regulation levels of the shunt ratio threshold and the refocusing parameter in the negative entropy compensation strategy according to the coupling strength distribution of the dynamic matching matrix, and assign an energy compensation path corresponding to the energy level segmentation of the thermal radiation attenuation curve to each regulation level;

[0136] The regulation level refers to dividing the negative entropy compensation strategy into control levels with different priorities (such as emergency, high, medium, low) according to the coupling strength distribution of the dynamic matching matrix. The energy compensation path refers to the compensation execution path corresponding to the energy level segmentation of the thermal radiation attenuation curve assigned to each regulation level (such as preferentially processing the high attenuation section).

[0137] In the embodiment of the present application, a clustering analysis is performed on the dynamic matching matrix, and the region with a coupling strength higher than the set threshold is divided into the "core regulation layer", and the rest are divided into the "auxiliary regulation layer" and the "observation layer" in decreasing order of strength. Each layer is bound to a specific energy level segmentation (such as the core layer corresponding to the high attenuation section), and a directional compensation path is assigned to it (such as the core layer path forcibly covering the equipment start-stop impact area).

[0138] 3033. Convert the transition direction and amplitude of the energy transition path into an execution priority sequence of the negative entropy compensation strategy, and generate an execution priority coefficient by superimposing the coupling strength of the dynamic matching matrix and the compensation efficiency of the energy compensation path;

[0139] The execution priority coefficient refers to a quantitative index generated by synthesizing the propagation speed of the comprehensive energy transition path (e.g., rapid diffusion needs to be processed first), amplitude (e.g., high attenuation requires high priority), and the coupling strength of the dynamic matching matrix, and is used to determine the execution order of the compensation strategy.

[0140] In the embodiments of the present application, the propagation direction of the energy transition path is converted into a spatial vector, the amplitude is converted into a weight factor, and weighted superposition is performed with the coupling strength of the dynamic matching matrix. For example, if a certain path has a fast propagation speed and a high coupling strength, its priority coefficient is increased to the highest level to ensure that the compensation action is triggered first.

[0141] 3034. Based on the corresponding relationship between the regulation level and the execution priority coefficient, construct a negative entropy compensation strategy in the energy interference map, and map the execution priority sequence to the vortex density distribution reconstruction strategy of the dynamic airflow model.

[0142] The negative entropy compensation strategy refers to a hierarchical control instruction set formed by binding the compensation parameters (shunt ratio, refocusing intensity) of different regulation levels to the execution priority; the vortex density distribution reconstruction strategy refers to adjusting the vortex generation and dissipation rules in the dynamic airflow model according to the priority coefficient to synergistically compensate for the increase in thermal radiation entropy.

[0143] In the embodiments of the present application, real-time compensation instructions (such as immediately starting shunt and heating) are assigned to the core regulation layer, and delayed execution instructions (such as triggering after the core layer is completed) are assigned to the auxiliary layer. At the same time, the priority coefficient is mapped to the vortex density control module of the dynamic airflow model. For example, in the high-priority area, vortex generation is suppressed to stabilize the airflow, and in the low-priority area, moderate vortices are allowed to maintain the ventilation efficiency.

[0144] The following is a specific example:

[0145] Suppose multiple monitoring devices in a ward start and stop simultaneously, causing abnormal thermal radiation and airflow disorder. The system detects the energy pulse caused by the start and stop of the device group, generates a dynamic matching matrix, and marks the area around the device as an interference channel with "extremely high" coupling strength. The area with the highest coupling strength in the matrix is designated as the core regulation layer, and the compensation path in the high-attenuation section is bound to forcibly cover the device area and adjacent hospital beds. Analyze the fast diffusion characteristics of the energy transition path, and generate the highest priority coefficient after superimposing the matrix coupling strength to ensure that the compensation in the device area is executed immediately. The hierarchical strategy triggers the directional airflow shunt (suppressing vortices) and local thermal compensation in the device area. After the core layer is completed, the auxiliary layer starts gentle regulation for the edge area. The airflow model synchronously reconstructs the vortex density distribution to avoid global airflow overload.

[0146] Through the above steps, this solution realizes instant response in key areas and on-demand regulation in non-key areas through coupling intensity-driven hierarchical division to avoid resource crowding; the priority coefficient converts the spatiotemporal characteristics of the energy transition path into execution order to ensure priority suppression of rapid diffusion interference; the linkage mechanism of vortex density reconstruction and thermal compensation simultaneously optimizes airflow stability and thermal balance; the real-time update capability of matrix and hierarchy supports continuous regulation of multi-device concurrent interference scenarios. Through hierarchical strategies and priority mapping, the "precision strike" and "system balance" of environmental regulation can be achieved in complex medical scenarios, significantly improving the safety redundancy and energy efficiency of the ward environment.

[0147] In order to solve the spatial misalignment problem of airflow diversion and thermal compensation, a dynamic mapping rule between diversion ratio and compensation amount is established through field intensity gradient alignment, compensation gradient superposition, dynamic index construction and strategy synchronization mechanism to achieve cross-model parameter collaborative update. In some embodiments, the airflow energy diversion ratio threshold set and the compensation amount sequence formed by the energy compensation amount are aligned according to the segment identifier, and a dynamic matching table containing the relationship between the energy compensation amount gradient distribution and the diversion ratio threshold is generated, and the control parameters of the negative entropy compensation strategy are updated based on the dynamic matching table, including:

[0148] 4031. Based on the field intensity gradient range of the segment identifier, align the airflow energy diversion ratio threshold set with the compensation amount sequence formed by the energy compensation amount in spatial dimension to generate a three-dimensional mapping relationship including energy level segment boundaries;

[0149] In this step, the three-dimensional mapping relationship refers to the field strength gradient range based on the segmented identifier (such as low gradient area, medium gradient area, and high gradient area), and the airflow energy diversion ratio threshold set and the thermal radiation compensation amount sequence are aligned in the spatial dimension to form an association network, which is used to describe the coordinated rules of diversion and compensation under different field strength environments.

[0150] In the embodiment of the present application, the field intensity gradient range of the segment identifier (such as the gradient range A to B corresponding to the identifier "G1") is grid-matched with the spatial distribution of the compensation amount sequence through a spatial interpolation algorithm to generate a three-dimensional mapping relationship including the energy level segment boundary. For example, the diversion ratio threshold of the high gradient area is mapped to the high compensation amount area to ensure the physical space consistency of the airflow and thermal compensation.

[0151] 4032. Perform compensation gradient superposition on each segment identifier associated region of the dynamic matching table according to the energy compensation amount gradient distribution in the three-dimensional mapping relationship to form a compensation gradient mapping relationship;

[0152] The compensation gradient mapping relationship refers to the gradient superposition (such as linear increase, exponential decay) of the energy compensation amounts in the associated regions of each segmentation identifier in the dynamic matching table, forming a compensation intensity distribution rule that matches the variation trend of the field strength gradient.

[0153] In the embodiments of the present application, for the spatial region covered by each segmentation identifier, the distribution characteristics of its energy compensation amount are analyzed (such as high compensation demand in the central region and low demand in the edge region), and a gradient field generation algorithm (such as radial basis function interpolation) is used to smoothly superpose the compensation amounts, forming a decreasing gradient of compensation intensity from the core to the periphery, and binding it to the corresponding identifier in the dynamic matching table to form a compensation gradient mapping relationship.

[0154] 4033. Extract the energy fluctuation amplitudes that overlap with the compensation gradient mapping relationship in the set of shunt ratio threshold values, and generate a dynamic matching table that includes the gradient distribution of energy compensation amounts and the associated relationship of shunt ratio threshold values.

[0155] The dynamic matching table refers to establishing a real-time linkage rule between the shunt ratio and the compensation amount by extracting the energy fluctuation characteristics (such as fluctuation frequency, amplitude) in the overlapping region between the set of shunt ratio threshold values and the compensation gradient mapping relationship, and is used to quickly retrieve the optimal combination of regulation parameters.

[0156] In the embodiments of the present application, each grid unit of the compensation gradient mapping relationship is traversed, and the corresponding shunt ratio threshold value and energy fluctuation amplitude are extracted (such as a higher shunt ratio is required in the region with a high fluctuation amplitude), and a dynamic matching table with an index structure is constructed through a hash table. For example, the shunt ratio threshold value in the high fluctuation amplitude region is bound to the high compensation amount as a key-value pair to support real-time query and parameter retrieval.

[0157] 4034. Hierarchically update the regulation parameters of the negative entropy compensation strategy based on the dynamic matching table, generate a sequence of regulation parameters that matches the energy level segmentation boundary, and synchronize the compensation gradient mapping relationship to the vortex density distribution reconstruction strategy of the dynamic air flow model.

[0158] The hierarchically arranged sequence of regulation parameters refers to dividing the regulation parameters into different execution levels (such as emergency execution, regular execution) according to the energy level segmentation boundary based on the priority of the dynamic matching table; the synchronization of the vortex density reconstruction strategy refers to transmitting the compensation gradient mapping relationship to the dynamic air flow model to guide it to adjust the vortex generation rule to adapt to the heat compensation demand.

[0159] In the embodiments of the present application, based on the key-value weights of the dynamic matching table, the regulation parameters are sorted by priority (such as the parameters in the high fluctuation amplitude region are updated first) to generate a hierarchical instruction sequence. At the same time, the compensation gradient mapping relationship is converted into vortex density control parameters (such as suppressing vortex generation in the high compensation amount region), and is synchronized to the calculation engine of the dynamic air flow model through the API interface to achieve cross-model strategy coordination.

[0160] The following is a specific example:

[0161] Suppose in a ward, due to the intermittent operation of equipment, local thermal radiation is unstable and air flow is disturbed. The system performs the following operations: According to the field strength gradient range of the segmentation identifier, spatially align the shunt ratio threshold in the high-gradient area with the corresponding heat compensation amount to generate a three-dimensional mapping relationship covering the equipment area and the hospital bed area. The compensation gradient is superimposed on the mapping relationship to form a distribution pattern with a high compensation intensity in the core area of the equipment and a decreasing intensity in the edge area, and it is bound to the dynamic matching table. Extract the energy fluctuation amplitude characteristics in the high-gradient area to generate an index table, and dynamically associate the high shunt ratio with the high compensation amount. Update the control parameters according to the priority of the index table, immediately start shunting and compensation for the core area of the equipment, and at the same time synchronize the compensation gradient to the air flow model to suppress the generation of vortices in this area. Within seconds, the thermal radiation returns to equilibrium and the air flow tends to be stable.

[0162] Through the above steps, this solution avoids the error of "the compensation area deviating from the interference source" in the traditional solution through the spatial alignment of the field strength gradient and the compensation amount; the compensation gradient mapping relationship supports local intensity adaptive adjustment, improving the resource utilization efficiency; the dynamic matching table realizes the fast retrieval and execution of the shunt ratio and the compensation amount, reducing the control delay; the vortex density reconstruction strategy synchronization mechanism ensures the physical effect consistency of the air flow and heat compensation control; the hierarchical parameter update avoids multi-region control conflicts and preferentially guarantees the stability of key regions. Through the dynamic matching table and cross-model collaboration, the refined and systematic thermal-air flow coupling control is realized, providing a highly robust dynamic balance guarantee for the medical environment.

[0163] In order to improve the scene adaptability of the thermal radiation refocusing compensation, by compensating baseline modeling, field strength gradient superposition correction, path weight optimization and sequence binding synchronization, the node density, air flow influence and transition path depth are deeply integrated to generate a dynamically corrected compensation gradient. In some embodiments, combining the thermal radiation attenuation fluctuation amplitude of the energy level segmentation with the node energy level distribution density of the three-dimensional radiation topology, calculate the energy compensation amount of the thermal radiation refocusing parameter corresponding to each energy level segmentation to form a compensation amount sequence, including:

[0164] 5031. Based on the thermal radiation attenuation fluctuation amplitude of the energy level segmentation, extract the spatial gradient characteristics of the node energy level distribution density in the three-dimensional radiation topology to generate the energy attenuation compensation baseline for each energy level segmentation;

[0165] In this step, the energy attenuation compensation baseline refers to the basic compensation amount comprehensively calculated based on the attenuation fluctuation amplitude (such as the temperature change rate) segmented by energy levels and the node energy level distribution density of the three-dimensional radiation topology (the aggregation degree of thermal radiation nodes in unit space), reflecting the minimum compensation requirement of the local thermal environment. The spatial gradient feature is extracted through the spatial derivative of the node density distribution (such as the density change rate).

[0166] In the embodiment of the present application, the attenuation fluctuation amplitude segmented by energy levels is normalized, combined with the node density distribution of the three-dimensional radiation topology (such as high-density areas and low-density areas), and an interpolation algorithm is used to generate a spatial gradient field. The compensation baseline is set based on the extreme values of the gradient field (such as the peak region of the density change rate), for example, the magnification of the baseline compensation amount in the high-attenuation and high-density areas is increased to the standard value.

[0167] 5032. Calculate the energy compensation amount of the thermal radiation refocusing parameter within the energy level segment according to the superposition relationship between the energy attenuation compensation baseline and the field strength gradient distribution in the dynamic air flow model, and generate an initial value sequence of the compensation amount;

[0168] The initial value sequence of the compensation amount refers to the set of initially calculated compensation amounts, which is generated by superimposing the energy attenuation compensation baseline and the field strength gradient distribution of the dynamic air flow model (such as the air flow velocity change rate), reflecting the enhancement or weakening effect of the air flow on the thermal compensation.

[0169] In the embodiment of the present application, the compensation baseline is input into the thermodynamic coupling model, combined with the field strength gradient data of the dynamic air flow model (such as if the field strength gradient in a certain area is high, the compensation amount needs to be reduced to balance the influence of the air flow), and the initial value sequence is calculated through linear weighting. For example, for each unit increase in the field strength gradient, the initial value of the compensation amount is reduced by a set ratio.

[0170] 5033. Perform path weight correction on the initial value sequence of the compensation amount in combination with the transition direction of the energy transition path, and generate an energy compensation amount correction value that matches the boundary of the energy level segment;

[0171] Path weight correction refers to adjusting the initial value of the compensation amount according to the propagation direction of the energy transition path (such as spreading from the equipment area to the hospital bed area). For example, the compensation amount is gradually increased along the path direction to offset the propagation loss.

[0172] In the embodiment of the present application, the energy transition path is converted into a spatial vector field, and a directional weight coefficient (such as the upstream weight of the path is 1.2 and the downstream weight is 0.8) is superimposed on the initial value of the compensation amount along the path direction to generate a correction value that matches the boundary of the energy level segment, ensuring that the compensation amount dynamically adapts along the interference propagation path.

[0173] 5034. Bind the energy compensation amount correction value to the shunt ratio threshold set according to the segmentation identifier, form a compensation amount sequence including the compensation gradient distribution of the thermal radiation refocusing parameter, and synchronize it to the regulation parameter update process of the dynamic matching table.

[0174] The compensation gradient distribution refers to the intensity change rule of the compensation amount correction value in the spatial dimension (such as high compensation in the core area and decreasing in the edge area); the dynamic matching table synchronization means that after binding the compensation sequence and the shunt ratio threshold according to the segmentation identifier, it is updated to the global regulation parameter library.

[0175] In the embodiment of the present application, a hash table is used to bind the energy compensation amount correction value and the shunt ratio threshold according to the segmentation identifier (such as "S1-High") to form a key-value pair structure. The compensation gradient distribution is synchronized to the dynamic matching table through the message queue, triggering the real-time iterative optimization of the regulation strategy.

[0176] The following is a specific example:

[0177] Suppose in a certain ward, a certain treatment device runs intermittently, causing periodic thermal radiation attenuation. The system performs the following operations: According to the attenuation fluctuation amplitude and the node density distribution, identify the area around the device as a high-attenuation and high-density area, and generate a high compensation baseline. Superimpose the dynamic air flow field strength gradient data, and calculate the initial value sequence (such as the compensation amount in the device area is appropriately reduced due to the high gradient). Along the diffusion direction of the energy transition path, add a direction weight to the initial value to generate the compensation amount correction value in the device area (such as increasing the compensation intensity upstream of the path). Bind the correction value to the shunt ratio threshold and synchronously update the dynamic matching table. The system immediately starts directional compensation in the device area to suppress the diffusion of thermal radiation fluctuations.

[0178] Through the above steps, this solution integrates the compensation baseline of node density and attenuation amplitude, avoiding the compensation deviation caused by traditional single indicators; through the superposition of field strength gradient and path weight adjustment, enhancing the scene adaptability of the compensation amount; the binding mechanism of the compensation sequence and the shunt threshold realizes the physical effect coordination of heat-air flow regulation; the dynamic matching table synchronization supports the millisecond-level update of the compensation strategy, improving the response robustness under complex interference. Through the dynamic correction and global synchronization of the compensation amount, the precise control of thermal radiation refocusing is realized, providing a high-stability thermal environment guarantee for the operation of medical devices and patient care.

[0179] In order to eliminate the timing conflict and resource occupation of cross-level regulation, through instruction priority sorting, spatial matching, timing weight calculation and reconstructed instruction generation, the spatio-temporal collaborative response of heat compensation and vortex density control is realized. In some embodiments, based on the corresponding relationship between the regulation level and the execution priority coefficient, a negative entropy compensation strategy is constructed in the energy interference map, and the execution priority sequence is mapped to the vortex density distribution reconstruction strategy of the dynamic air flow model, including:

[0180] 6031. Based on the binding relationship between the shunt ratio threshold and the energy compensation path in the regulation hierarchy, prioritize the energy compensation paths for the interference channel levels of the energy interference map to generate a negative entropy compensation strategy;

[0181] The regulation hierarchy refers to the priority levels of compensation strategies (such as the core layer and the auxiliary layer) divided according to the coupling strength of the dynamic matching matrix, which is used to distinguish the urgency of environmental regulation in different regions.

[0182] The shunt ratio threshold refers to the lower or upper limit of the air flow energy ratio required for stabilizing the air flow in different field strength gradient regions.

[0183] The energy compensation path refers to the directional heat compensation execution route designed for the thermal radiation attenuation region (such as the compensation propagation path from the equipment area to the hospital bed area).

[0184] The interference channel level refers to the interaction network level formed by the coupling of the heat source layer and the air flow layer in the energy interference map, which is used to quantify the heat-air flow energy interaction intensity.

[0185] In the embodiment of the present application, first, the coupling strength of the dynamic matching matrix (such as 0.3 - 0.9) is hierarchically divided by the clustering algorithm: the region with a coupling strength ≥ 0.7 is designated as the core regulation layer, 0.4 - 0.7 as the auxiliary layer, and the rest as the observation layer. The core layer is bound to a high-priority energy compensation path (such as the area affected by equipment start and stop), and the auxiliary layer is bound to a medium-priority path (such as the area of personnel flow). Based on the binding relationship between the shunt ratio threshold and the compensation path (such as the shunt ratio in the core layer ≥ 40%), prioritize the interference channel levels (such as channels C1 - C5). For example, assign the interference channel C3 with the highest coupling strength to the core layer to generate the corresponding negative entropy compensation strategy, including the shunt ratio threshold (45%) and the compensation power (8W / m 2 ) parameter combination.

[0186] 6032. Based on the distribution characteristics of the execution priority coefficient, spatially match the energy compensation paths at different levels in the negative entropy compensation strategy with the vortex density distribution regions of the dynamic air flow model;

[0187] The execution priority coefficient refers to a quantitative index generated by comprehensively considering the propagation speed, amplitude, and coupling strength of the energy transition path, which is used to determine the execution order of the compensation strategy.

[0188] Spatial matching refers to the process of physically aligning the priority of the energy compensation path with the vortex density distribution region in the dynamic air flow model.

[0189] In the embodiments of the present application, the execution priority coefficient is calculated by weighted superposition of the propagation speed (e.g., 0.5 m / s) and the coupling strength (e.g., 0.8) of the energy transition path (formula: priority coefficient = speed × 0.6 + strength × 0.4). Based on the coefficient distribution (e.g., the core layer coefficient ≥ 0.7), the high-priority compensation path (e.g., device area directional shunt) is mapped to the high-risk area of the vortex density in the dynamic airflow model (e.g., the air supply duct intersection). For example, through the spatial grid indexing technology, the compensation path coordinates of the device area are matched with the vortex density distribution grid (resolution 0.1 m) of the airflow model to ensure that the regulation instructions accurately cover the target area.

[0190] 6033. Combine the transition direction of the energy transition path and the priority of the energy compensation path at the regulation level to calculate the execution timing weights of different regions in the vortex density distribution reconstruction strategy;

[0191] The transition direction refers to the propagation direction in space of the thermal radiation attenuation caused by the energy pulse (e.g., spreading from the device area to the hospital bed area).

[0192] The execution timing weight refers to the weight value of the execution order of the regulation instructions for different regions dynamically allocated according to the priority and the transition direction.

[0193] In the embodiments of the present application, combining the transition direction of the energy transition path (e.g., diffusion angle 30°) and the priority at the regulation level (core layer > auxiliary layer), the vector projection algorithm is used to calculate the timing weights of each region. For example, the weight of the upstream region (close to the interference source) along the transition direction is set to 1.2, and the downstream region is set to 0.8; the weight of the core layer region is additionally increased by 0.3. Finally, a timing weight matrix of the vortex density reconstruction strategy is generated (e.g., the weight of the device area is 1.5, and the weight of the hospital bed edge area is 0.9) to guide the instruction execution order.

[0194] 6034. According to the correspondence between the execution timing weights and the negative entropy compensation strategy, generate a vortex density distribution reconstruction instruction sequence that synchronously responds to the negative entropy compensation strategy in the dynamic airflow model.

[0195] The reconstruction instruction sequence refers to a set of control instructions generated according to the timing weights for adjusting the vortex density distribution in the dynamic airflow model.

[0196] In the embodiments of the present application, based on the timing weight matrix, a fluid dynamics solver (e.g., ANSYS Fluent) is called to generate a reconstruction instruction sequence: the regions with a weight ≥ 1.2 immediately execute vortex suppression (reduce the density coefficient to 0.2), and the regions with a weight of 0.8 - 1.2 execute with a delay of 2 seconds (adjust the density coefficient to 0.5). The instructions are encapsulated in JSON format and sent to the control interface of the dynamic airflow model to ensure synchronous effectiveness with the negative entropy compensation strategy (e.g., adjustment of the shunt ratio).

[0197] For example, in an ICU ward, the simultaneous start and stop of multiple ventilators lead to a sudden drop in thermal radiation and air current counterflow. The system performs the following operations: detecting that the coupling strength in the equipment area is 0.85, classifying it as the core control layer, binding the shunt ratio threshold of 45% and the compensation power of 10 W / m 2 , and generating a priority compensation path (covering the equipment area and adjacent hospital beds). Calculating the priority coefficient of the equipment area as 0.9 (propagation speed of 1.2 m / s × 0.6 + coupling strength of 0.85 × 0.4), mapping its compensation path to the high-vortex-density area (vortex coefficient of 0.8) of the air current model. Setting the upstream weight as 1.5 and the downstream weight as 1.0 along the transition direction (equipment → hospital bed), and superimposing the core layer weight of 0.3 to generate a timing weight matrix (equipment area 1.8, hospital bed area 1.3). The equipment area immediately starts vortex suppression (density coefficient of 0.2), and the hospital bed area executes with a 1-second delay (density coefficient of 0.5). Synchronously triggering the shunt ratio to increase to 45% and local heating compensation, the thermal radiation returns to equilibrium within 5 seconds, and the air current counterflow is eliminated.

[0198] Through the regulation level division and priority mapping, the immediate response in key areas and the demand-based regulation in non-key areas are realized, avoiding resource occupation; the timing weight mechanism ensures the priority suppression of fast-spreading interference; the synchronous execution of the reconstruction instruction and the compensation strategy synchronously optimizes the air current stability and thermal equilibrium.

[0199] Figure 2 The structural schematic diagram of a digital management system for an intelligent ward based on digital twin is provided for the embodiments of this application, as Figure 2 shown. The device includes:

[0200] A construction module 21, used to construct a ward digital twin, and synchronously collect the thermal radiation distribution data in the hospital bed area, the air current modal data in the operation area, and the energy pulse data of medical equipment through a sensor array;

[0201] A convolution module 22, used to perform spatial convolution on the thermal radiation distribution data and the thermodynamic characteristics of the patient's posture to generate a three-dimensional radiation topology, and at the same time perform field strength superposition on the air current modal data and the fluid characteristics of the nursing trajectory to form a dynamic air current model;

[0202] An analysis module 23, used to establish an energy interference map based on the three-dimensional radiation topology and the dynamic air current model, analyze the phase mutation points of the energy pulse data and the thermal radiation attenuation curve, and generate a negative entropy compensation strategy including the air current energy shunt ratio and the thermal radiation refocusing parameters;

[0203] A prediction module 24, used to embed a medical behavior response mechanism in the digital twin. When detecting a device start / stop event, predict the topological distortion according to the spatial overlapping area of the transfer path and the dynamic air current model, and reconstruct the vortex density distribution of the air current model;

[0204] The calculation module 25 is configured to calculate the dynamic equilibrium threshold of the heat radiation entropy increase gradient and the negative entropy diversion rate based on the updated energy interference map, the vortex density distribution, and the negative entropy compensation strategy, and generate a regulation instruction set including the thermal stability coefficient and the air flow purification efficiency.

[0205] Figure 2 The described digital management device for a smart ward based on digital twin can execute Figure 1 The described digital management method for a smart ward based on digital twin in the illustrated embodiment, and its implementation principle and technical effects will not be elaborated. For the digital management device for a smart ward based on digital twin in the above embodiments, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0206] In a possible design, Figure 2 The digital management device for a smart ward based on digital twin in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, the computing device may include a storage component 31 and a processing component 32;

[0207] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0208] The processing component 32 is used for the Figure 1 digital management method for a smart ward based on digital twin in the above

[0209] embodiment. Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0210] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0211] Of course, the computing device necessarily may also include other components, such as input / output interfaces, display components, communication components, etc.

[0212] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0213] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0214] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0215] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 A digital management method for a smart ward based on digital twins shown in the embodiment.

[0216] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0217] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0218] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0219] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A digital management method for smart wards based on digital twins, characterized in that: include: Build a digital twin of the ward, and use the sensor array to synchronously collect the thermal radiation distribution data of the bed area, the airflow modal data of the operation area, and the energy pulse data of the medical equipment; The thermal radiation distribution data is spatially convolved with the thermodynamic characteristics of the patient's posture to generate a three-dimensional radiation topology, and the airflow modal data and the nursing trajectory fluid characteristics are superimposed on the field intensity to form a dynamic airflow model; Based on the three-dimensional radiation topology and dynamic airflow model, the energy interference spectrum is established, the phase mutation points and thermal radiation attenuation curves of the energy pulse data are analyzed, and the negative entropy compensation strategy including the airflow energy diversion ratio and thermal radiation refocusing parameters is generated; A medical behavior response mechanism is embedded in the digital twin. When a device start-stop event is detected, the topological distortion is predicted based on the spatial overlap area between the transport path and the dynamic airflow model, and the vortex density distribution of the airflow model is reconstructed. Based on the updated energy interference map, vortex density distribution and negative entropy compensation strategy, the dynamic equilibrium threshold of the thermal radiation entropy increase gradient and the negative entropy diversion rate is calculated, and a control instruction set including the thermal stability coefficient and airflow purification efficiency is generated.

2. The method according to claim 1, characterized in that Based on the three-dimensional radiation topology and dynamic airflow model, the energy interference spectrum is established, the phase mutation point and thermal radiation attenuation curve of the energy pulse data are analyzed, and the negative entropy compensation strategy including the airflow energy diversion ratio and thermal radiation refocusing parameters is generated, including: The node energy level distribution of the three-dimensional radiation topology is spatially coupled with the field intensity gradient of the dynamic airflow model to establish a multi-layer interference channel of the energy interference spectrum; Analyzing the phase mutation point of the energy pulse data in the energy interference spectrum, and generating the energy transition path of the phase mutation point by associating the fluctuation frequency of the thermal radiation attenuation curve with the energy transition direction of the phase mutation point; The thermal radiation attenuation curve is segmented into energy levels based on the energy transition path, the airflow energy diversion ratio thresholds matching different energy level segments in the dynamic airflow model are extracted, and the energy compensation amount of the thermal radiation refocusing parameter in the three-dimensional radiation topology is simultaneously calculated; According to the dynamic matching relationship between the airflow energy diversion ratio threshold and the energy compensation amount, a negative entropy compensation strategy including the airflow energy diversion ratio and thermal radiation refocusing parameters is generated in the energy interference spectrum, and the energy transition path is mapped to the execution priority of the negative entropy compensation strategy.

3. The method according to claim 2, characterized in that The thermal radiation attenuation curve is segmented into energy levels based on the energy transition path, the airflow energy diversion ratio thresholds matching different energy level segments in the dynamic airflow model are extracted, and the energy compensation amount of the thermal radiation refocusing parameter in the three-dimensional radiation topology is simultaneously calculated, including: According to the transition direction and amplitude of the energy transition path, the thermal radiation attenuation curve is divided into a plurality of energy level segments, and a segment identifier including a field intensity gradient range is generated for each energy level segment; Traversing the field intensity gradient distribution of the dynamic airflow model based on the segment identifier, extracting the energy fluctuation amplitude of the area overlapping with the field intensity gradient range of each energy level segment, and generating a set of airflow energy diversion ratio threshold values; The energy compensation amount of the thermal radiation refocusing parameter corresponding to each energy level segment is calculated by combining the thermal radiation attenuation fluctuation amplitude of the energy level segment and the node energy level distribution density of the three-dimensional radiation topology.

4. The method according to claim 2, characterized in that: According to the dynamic matching relationship between the airflow energy diversion ratio threshold and the energy compensation amount, a negative entropy compensation strategy including the airflow energy diversion ratio and the thermal radiation refocusing parameter is generated in the energy interference spectrum, and the energy transition path is mapped to the execution priority of the negative entropy compensation strategy, including: Based on the spatial distribution characteristics of the dynamic matching relationship, the coupling strength between the airflow energy diversion ratio threshold and the energy compensation amount of the thermal radiation refocusing parameter is calculated to generate a dynamic matching matrix of the interference channel level in the energy interference spectrum; According to the coupling strength distribution of the dynamic matching matrix, the regulation levels of the shunt ratio threshold and the refocusing parameter in the negative entropy compensation strategy are divided, and an energy compensation path corresponding to the energy level segment of the thermal radiation attenuation curve is allocated to each regulation level; Converting the transition direction and amplitude of the energy transition path into an execution priority sequence of the negative entropy compensation strategy, and generating an execution priority coefficient by superimposing the coupling strength of the dynamic matching matrix and the compensation efficiency of the energy compensation path; Based on the correspondence between the control level and the execution priority coefficient, a negative entropy compensation strategy is constructed in the energy interference map, and the execution priority sequence is mapped to the vortex density distribution reconstruction strategy of the dynamic airflow model.

5. The method according to claim 3, characterized in that: Also includes: Based on the field intensity gradient range of the segment identifier, the airflow energy diversion ratio threshold set and the compensation amount sequence formed by the energy compensation amount are spatially aligned to generate a three-dimensional mapping relationship including energy level segment boundaries; According to the energy compensation amount gradient distribution in the three-dimensional mapping relationship, compensation gradient superposition is performed on each segment identifier associated area in the dynamic matching table to form a compensation gradient mapping relationship; The energy fluctuation amplitude overlapping with the compensation gradient mapping relationship in the diversion ratio threshold set is extracted to generate a dynamic matching table including the energy compensation gradient distribution and the diversion ratio threshold correlation relationship.

6. The method according to claim 3, characterized in that: Combining the thermal radiation attenuation fluctuation amplitude of the energy level segment and the node energy level distribution density of the three-dimensional radiation topology, the energy compensation amount of the thermal radiation refocusing parameter corresponding to each energy level segment is calculated, including: Based on the thermal radiation attenuation fluctuation amplitude of the energy level segment, the spatial gradient characteristics of the node energy level distribution density in the three-dimensional radiation topology are extracted to generate an energy attenuation compensation baseline for each energy level segment; The energy compensation amount of the thermal radiation refocusing parameter within the energy level segment is calculated according to the superposition relationship between the energy attenuation compensation baseline and the field intensity gradient distribution in the dynamic airflow model.

7. The method according to claim 4, characterized in that Based on the corresponding relationship between the control level and the execution priority coefficient, a negative entropy compensation strategy is constructed in the energy interference map, and the execution priority sequence is mapped to the vortex density distribution reconstruction strategy of the dynamic airflow model, including: According to the binding relationship between the diversion ratio threshold and the energy compensation path in the regulation level, the energy compensation path priority is sorted for the interference channel level of the energy interference spectrum to generate a negative entropy compensation strategy; Based on the distribution characteristics of the execution priority coefficient, spatially matching the energy compensation paths of different levels in the negative entropy compensation strategy with the vortex density distribution area of ​​the dynamic airflow model; Combining the transition direction of the energy transition path with the energy compensation path priority of the control level, calculating the execution timing weights of different areas in the vortex density distribution reconstruction strategy; According to the corresponding relationship between the execution timing weight and the negative entropy compensation strategy, a vortex density distribution reconstruction instruction sequence that responds synchronously with the negative entropy compensation strategy is generated in the dynamic airflow model.

8. A digital twin-based smart ward digital management system, characterized in that: include: The construction module is used to build a digital twin of the ward, which synchronously collects the thermal radiation distribution data of the bed area, the airflow modal data of the operation area, and the energy pulse data of the medical equipment through the sensor array; The convolution module is used to spatially convolve the thermal radiation distribution data with the thermodynamic characteristics of the patient's posture to generate a three-dimensional radiation topology, and to superimpose the airflow modal data with the nursing trajectory fluid characteristics to form a dynamic airflow model; The analytical module is used to establish an energy interference spectrum based on the three-dimensional radiation topology and dynamic airflow model, analyze the phase mutation points and thermal radiation attenuation curves of the energy pulse data, and generate a negative entropy compensation strategy including the airflow energy diversion ratio and thermal radiation refocusing parameters; The prediction module is used to embed the medical behavior response mechanism in the digital twin. When the equipment start-stop event is detected, the topological distortion is predicted based on the spatial overlap area between the transport path and the dynamic airflow model, and the vortex density distribution of the airflow model is reconstructed; The calculation module is used to calculate the dynamic equilibrium threshold of the thermal radiation entropy increase gradient and the negative entropy diversion rate based on the updated energy interference map, vortex density distribution and negative entropy compensation strategy, and generate a control instruction set including the thermal stability coefficient and airflow purification efficiency.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a digital management method for a smart ward based on digital twins as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a digital management method for a smart ward based on digital twins as described in any one of claims 1 to 7 is implemented.

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