Emergency clinical data real-time sharing method and system based on Internet of Things

Abstract

The embodiment of the invention provides an emergency clinical data real-time sharing method and system based on the Internet of Things. Wherein the biological parameters of the wound part are monitored in real time by using the intelligent dressing integrated with the antibacterial coating, and the data are transmitted to the emergency clinical data collaboration platform through the low-power wide area network. According to the relationship between the aperture of the antibacterial coating and the drug release rate, an optimal drug release curve is screened out. In combination with cross-mechanism wound infection historical data, federal learning is adopted to construct an infection risk evolution model, and infection risk quantitative parameters are generated. The data are comprehensively analyzed, the drug release rate and the effective time window are dynamically adjusted, and the abnormal correlation threshold value is corrected. And based on the analysis result, automatically generating a wound infection early warning instruction and an emergency treatment data sharing instruction so as to assist in clinical decision making. According to the technical scheme provided by the embodiment of the invention, the efficiency and accuracy of real-time sharing of emergency clinical data can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of real-time sharing of emergency clinical data, and in particular, to a method and system for real-time sharing of emergency clinical data based on the Internet of Things. Background Art

[0002] In emergency and trauma care, real-time monitoring of the biological parameters of the trauma site is crucial for early detection of signs of infection. The application scenario requires the ability to quickly and accurately collect and analyze this data to support the clinical decision-making process and control the infection risk through an effective drug release mechanism. In addition, data sharing and collaboration between different medical institutions are also extremely critical, which helps to improve the treatment effect and reduce complications.

[0003] Currently, some medical devices and technologies can already achieve the monitoring of biological parameters at the trauma site. For example, intelligent dressings are used to collect local environmental information (such as temperature, humidity, etc.). At the same time, methods based on big data analysis are also used to evaluate the risk of trauma infection. However, the existing solutions mainly rely on traditional data transmission methods and lack an efficient cross-institutional data sharing mechanism.

[0004] The existing solutions have several obvious deficiencies: the low efficiency of data transmission and the insufficient cross-institutional data sharing result in the inability to obtain comprehensive information in a timely manner for comprehensive analysis; the traditional drug release mechanism is difficult to dynamically adjust according to the specific situation of the trauma site, limiting the treatment effect; due to the lack of an intelligent warning system, medical staff may not be able to identify potential infection risks in the first place, thus delaying the best treatment opportunity; the application of federated learning has not been popularized, limiting the possibility of effective cooperation and knowledge sharing between different medical institutions. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for real-time sharing of emergency clinical data based on the Internet of Things, so as to solve the problems of low efficiency and poor accuracy in the real-time sharing of emergency clinical data in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a method for real-time sharing of emergency clinical data based on the Internet of Things, including:

[0007] Real-time collecting a dynamic sequence of biological parameters of a trauma site through an intelligent dressing integrated with an antibacterial coating, and transmitting the dynamic sequence of biological parameters to an emergency clinical data collaboration platform through a low-power wide-area network;

[0008] Screening a drug release rate curve according to the actual pore size of the antibacterial coating of the intelligent dressing, and the pore size-drug sustained release mapping relationship in the antibacterial coating database defines the non-linear attenuation characteristics of the associated drug sustained release curve when the pore size meets a preset condition;

[0009] Based on the historical dataset of trauma infections in medical institutions, a cross-institutional infection risk evolution model is constructed using the federated learning framework to generate infection risk quantification parameters associated with the dynamic sequence of the biological parameters and the drug release rate curve;

[0010] Perform multi-source collaborative analysis on the dynamic sequence of the biological parameters, the drug release rate curve, and the infection risk quantification parameters. Among them, the transmission delay parameter of the low-power wide-area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold between the pH value and temperature fluctuations through the model weights of the federated learning framework;

[0011] Generate a trauma infection warning instruction and an emergency data sharing instruction according to the results of the multi-source collaborative analysis. The emergency data sharing instruction pushes clinical decision-making assistance information to the designated emergency unit through the low-power wide-area network.

[0012] Optionally, the performing multi-source collaborative analysis on the dynamic sequence of the biological parameters, the drug release rate curve, and the infection risk quantification parameters. Among them, the transmission delay parameter of the low-power wide-area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold between the pH value and temperature fluctuations through the model weights of the federated learning framework, includes:

[0013] According to the corresponding relationship between the transmission delay parameter of the low-power wide-area network and the effective time window of the drug release rate curve, establish a delay compensation mechanism of the transmission delay parameter for the effective time window to generate a dynamic adjustment factor;

[0014] Perform phase synchronization processing on the dynamic adjustment factor and the periodic change trend of the dynamic sequence of the biological parameters to generate a synchronized biological dynamic sequence, and couple it with the dynamic adjustment factor to generate a biological dynamic coupling coefficient;

[0015] Generate a collaborative control variable according to the linear superposition result of the infection risk quantification parameter and the biological dynamic coupling coefficient. The collaborative control variable dynamically adjusts the release gradient of the drug release rate curve and the termination boundary of the effective time window through the feedback link. The infection risk quantification parameter corrects the abnormal correlation threshold between the pH value and temperature fluctuations through the model weights of the federated learning framework;

[0016] Perform non-linear constraint on the collaborative control variable and the synchronized biological dynamic sequence to generate the release frequency peak for updating the drug release rate curve and the delay compensation threshold of the effective time window;

[0017] The delay compensation threshold is iteratively matched with the real-time change amount of the transmission delay parameter to update the compensation weight of the dynamic adjustment factor, forming a closed-loop dynamic adjustment link.

[0018] Optionally, the non-linearly constraining the cooperative control variable and the synchronized biological dynamic sequence to generate a release frequency peak for updating the drug release rate curve and a delay compensation threshold for the effective time window includes:

[0019] Coupling and correlating the temporal change rate of the cooperative control variable with the dynamic fluctuation characteristics of the synchronized biological dynamic sequence to generate a set of dynamic coupling parameters;

[0020] Performing multi-level parameter decomposition on the set of dynamic coupling parameters, and dynamically weighting the multi-level parameter decomposition results through the time slice division result of the synchronized biological dynamic sequence to generate a set of decomposed parameters;

[0021] Dividing the synchronized biological dynamic sequence into a time slice sequence, extracting the dynamic fluctuation feature vector of the time slice sequence, and performing cross-operation on the dynamic fluctuation feature vector and the set of decomposed parameters to generate a set of modulation parameters;

[0022] Inputting the set of modulation parameters into a constraint generator in a non-linear constraint model, and adjusting the weight distribution of the set of modulation parameters through an iterative feedback mechanism to generate an initial release frequency peak and an initial delay compensation threshold;

[0023] Jointly optimizing the initial release frequency peak and the initial delay compensation threshold, and by constraining the dynamic balance condition of the synchronized biological dynamic sequence and the boundary condition of the cooperative control variable, correcting the phase offset of the initial release frequency peak and the window length of the initial delay compensation threshold to generate an optimized release frequency peak and delay compensation threshold parameter group;

[0024] Mapping the optimized release frequency peak and delay compensation threshold parameter group to the update process of the drug release rate curve to achieve the delay compensation threshold of the drug release rate curve in the effective time window.

[0025] Optionally, the jointly optimizing the initial release frequency peak and the initial delay compensation threshold, and by constraining the dynamic balance condition of the synchronized biological dynamic sequence and the boundary condition of the cooperative control variable, correcting the phase offset of the initial release frequency peak and the window length of the initial delay compensation threshold to generate an optimized release frequency peak and delay compensation threshold parameter group includes:

[0026] By constraining the dynamic equilibrium conditions of the synchronized biological dynamic sequence, align the fluctuation change rate of the synchronized biological dynamic sequence with the biological rhythm parameters in the time domain to generate dynamic equilibrium constraint parameters;

[0027] Establish the boundary condition constraint rules for the collaborative control variables, and generate a boundary range including phase deviation and response window threshold according to the transmission path length and diffusion delay characteristics of the collaborative control variables in the thermoresponsive spraying structure;

[0028] Execute the joint optimization iteration of the initial release frequency peak and the initial delay compensation threshold. Based on the dynamic equilibrium constraint parameters and the boundary range, establish a joint optimization objective for phase synchronization error and window coverage rate;

[0029] Based on the joint optimization objective, correct the phase offset of the initial release frequency peak. Introduce a phase correction factor according to the gradient descent direction of the phase synchronization error, and perform waveform shaping on the time domain distribution of the initial release frequency peak to generate an optimized initial release frequency peak;

[0030] Adjust the window length of the initial delay compensation threshold, calculate the time expansion amount based on the residual term of the window coverage rate, and combine the drug diffusion rate feedback of the thermoresponsive spraying structure to perform non-linear compression compensation on the window length to generate an optimized delay compensation threshold parameter set.

[0031] Optionally, based on the historical dataset of trauma infections in medical institutions, use the federated learning framework to construct a cross-institutional infection risk evolution model, and generate infection risk quantification parameters associated with the biological parameter dynamic sequence and the drug release rate curve, including:

[0032] Construct a local dynamic time series model based on the historical dataset of trauma infections in medical institutions, and combine the local features related to the drug release rate curve to generate a dynamic weight assignment that is passed as an intermediate parameter to the federated aggregation module;

[0033] Fuse the local dynamic time series model in the federated aggregation module, construct a global time series evolution relationship according to the dynamic weight assignment, and generate a global evolution matrix by adjusting the association mode between the biological parameter dynamic sequence and the drug release rate curve;

[0034] Pass the global evolution matrix to the feature coupling module, generate a feature coupling coefficient based on the global evolution matrix. The feature coupling coefficient maps the drug release rate curve to the biological parameter dynamic sequence through multi-modal feature fusion to generate a feature association chain, and the feature association chain is passed as an intermediate parameter to the dynamic optimization module;

[0035] In the dynamic optimization module, adjust the phase offset of the drug release rate curve according to the feature coupling coefficient to generate a dynamic constraint boundary, and transfer the dynamic constraint boundary to the risk quantification module as an intermediate parameter;

[0036] Adopt a federated learning framework to construct a cross-institutional infection risk evolution model, and generate infection risk quantification parameters associated with the dynamic sequence of biological parameters and the drug release rate curve through a closed-loop feedback mechanism.

[0037] Optionally, transfer the global evolution matrix to the feature coupling module, generate a feature coupling coefficient based on the global evolution matrix, and map the drug release rate curve to the dynamic sequence of biological parameters through multimodal feature fusion to generate a feature association chain, including:

[0038] Transfer the global evolution matrix to the feature coupling module, and generate a feature coupling coefficient according to the internal structural relationship of the global evolution matrix in the feature coupling module;

[0039] Perform phase offset adjustment on the drug release rate curve based on the feature coupling coefficient, and the adjusted drug release rate curve is synchronized with the dynamic sequence of biological parameters in time series to generate a dynamic association time series;

[0040] Input the dynamic association time series into the feature association chain generation module, map the drug release rate curve to the dynamic sequence of biological parameters through multimodal feature fusion, and iteratively optimize the feature coupling coefficient through a coupling feedback mechanism to generate a feature association chain with a hierarchical dependence relationship.

[0041] Optionally, generate a trauma infection warning instruction and an emergency data sharing instruction according to the multi-source collaborative analysis result, and push clinical decision-making auxiliary information to a specified emergency unit through a low-power wide-area network, including:

[0042] Generate a trauma infection risk level parameter according to the superposition relationship between the biological dynamic coupling coefficient and the infection risk quantification parameter in the multi-source collaborative analysis result, and generate a priority evaluation parameter in combination with the load status parameter of the emergency unit;

[0043] Generate a trauma infection warning instruction and an emergency data sharing instruction according to the multi-source collaborative analysis result, and construct an auxiliary information aggregation template based on the historical response efficiency of the priority evaluation parameter and the clinical decision-making auxiliary information;

[0044] Dynamically match the label mapping relationship between the trauma infection warning instruction and the auxiliary information aggregation template to generate a decision matching degree index, and adjust the push priority sequence of the clinical decision-making auxiliary information through a low-power wide-area network;

[0045] Dynamically calibrate the trauma infection risk level parameter according to the deviation range between the decision matching degree index and the multi-source collaborative analysis result, and generate a calibrated early warning trigger threshold;

[0046] Push the clinically assisted decision-making information to the designated emergency unit through the low-power wide-area network by using the calibrated early warning trigger threshold and the push priority sequence.

[0047] In a second aspect, an embodiment of the present application provides an emergency clinical data real-time sharing system based on the Internet of Things, including:

[0048] An acquisition module, which uses an intelligent dressing integrated with an antibacterial coating to collect the dynamic sequence of biological parameters at the trauma site in real time, and transmits the dynamic sequence of biological parameters to the emergency clinical data collaboration platform through a low-power wide-area network;

[0049] A screening module, which screens the drug release rate curve according to the actual pore size of the antibacterial coating of the intelligent dressing. The pore size-drug sustained release mapping relationship in the antibacterial coating database defines the non-linear attenuation characteristics of the associated drug release curve when the pore size meets the preset conditions;

[0050] A construction module, which constructs a cross-institutional infection risk evolution model based on the historical data set of trauma infections in medical institutions by using a federated learning framework, and generates an infection risk quantification parameter associated with the dynamic sequence of biological parameters and the drug release rate curve;

[0051] An analysis module, which performs multi-source collaborative analysis on the dynamic sequence of biological parameters, the drug release rate curve, and the infection risk quantification parameter. The transmission delay parameter of the low-power wide-area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal association threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework;

[0052] A generation module, which generates a trauma infection early warning instruction and an emergency data sharing instruction according to the multi-source collaborative analysis result. The emergency data sharing instruction pushes clinically assisted decision-making information to the designated emergency unit through a low-power wide-area network.

[0053] 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 an emergency clinical data real-time sharing method based on the Internet of Things as described in the first aspect above.

[0054] Fourthly, an embodiment of the present application provides a computer storage medium storing a computer program, which when executed by a computer, implements a method for real-time sharing of emergency clinical data based on the Internet of Things as described in the first aspect.

[0055] In the embodiment of the present application, an intelligent dressing integrated with an antibacterial coating is used to collect the dynamic sequence of biological parameters at the trauma site in real time, and the dynamic sequence of biological parameters is transmitted to the emergency clinical data collaboration platform through a low-power wide-area network; a drug release rate curve is screened according to the actual pore size of the antibacterial coating of the intelligent dressing, and the pore size-drug sustained release mapping relationship in the antibacterial coating database defines the non-linear attenuation characteristics of the drug sustained release curve associated when the pore size meets the preset conditions; based on the historical dataset of trauma infections in medical institutions, a cross-institutional infection risk evolution model is constructed using a federated learning framework to generate an infection risk quantification parameter associated with the dynamic sequence of biological parameters and the drug release rate curve; the dynamic sequence of biological parameters, the drug release rate curve, and the infection risk quantification parameter are subjected to multi-source collaborative analysis, where the transmission delay parameter of the low-power wide-area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework: an early warning instruction for trauma infection and an emergency data sharing instruction are generated according to the multi-source collaborative analysis result, and the emergency data sharing instruction pushes clinical decision-making assistance information to a specified emergency unit through the low-power wide-area network.

[0056] The technical solution of the present application has the following beneficial effects:

[0057] The present application uses an intelligent dressing integrated with an antibacterial coating to collect biological parameters at the trauma site in real time and transmits them to the emergency clinical data collaboration platform through a low-power wide-area network, greatly improving the ability of early warning of trauma infection. The best drug release rate curve is screened based on the pore size of the antibacterial coating, which can not only effectively control the drug release amount and speed, but also dynamically adjust according to the specific situation of the trauma. The cross-institutional infection risk evolution model constructed in combination with the federated learning framework generates an infection risk quantification parameter associated with the dynamic sequence of biological parameters and the drug release rate curve, enabling medical institutions to carry out precise treatment on the basis of fully understanding the patient's condition. In addition, this method promotes data sharing and collaboration between different medical institutions, improves the efficiency and accuracy of emergency treatment, helps reduce the risk of trauma infection and improves the patient's prognosis.

[0058] Furthermore, by dynamically adjusting the effective time window of the drug release rate curve through the transmission delay parameters of the low-power wide-area network, a delay compensation mechanism for the effective time window with respect to the transmission delay parameters is established, a dynamic adjustment factor is generated, and phase synchronization processing is performed with the periodic change trend of the dynamic biological parameter sequence to ensure that the release gradient of the drug release rate curve and the termination boundary of the effective time window can be flexibly adjusted according to actual needs. Meanwhile, the infection risk quantification parameter corrects the abnormal correlation threshold of the acid-base value and temperature fluctuation through the model weight of the federated learning framework, enhancing the system's ability to resist external environmental interference. This method not only improves the pertinence and effectiveness of drug treatment but also achieves precise control of the drug release process through a closed-loop dynamic adjustment link, significantly improving the treatment success rate and the patient's recovery speed.

[0059] 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

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in 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.

[0061] Figure 1 The flowchart of a method for real-time sharing of emergency clinical data based on the Internet of Things provided by the present application is shown;

[0062] Figure 2 The structural schematic diagram of a system for real-time sharing of emergency clinical data based on the Internet of Things provided by the present application is shown;

[0063] Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] In order to enable those skilled in the art to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0065] In some of the processes described in the specification, claims, and the above-mentioned drawings of this application, a number of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein 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 order of execution. Additionally, 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", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0066] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0067] This solution aims to realize the real-time monitoring and analysis of biological parameters at the trauma site through intelligent dressings and low-power wide-area network technology. Combining the drug release characteristics of the antibacterial coating and the historical data across medical institutions, a system capable of predicting and warning of trauma infection risks is constructed. The entire process from data collection, transmission, processing to the final risk assessment and warning forms a closed-loop management mechanism, aiming to improve the efficiency and accuracy of emergency medical decision-making.

[0068] Figure 1 The following is a flowchart of a method for real-time sharing of emergency clinical data based on the Internet of Things provided by an embodiment of the present application, as Figure 1 shown, the method includes:

[0069] 101. Real-time collect the dynamic sequence of biological parameters at the trauma site through an intelligent dressing integrated with an antibacterial coating, and transmit the dynamic sequence of biological parameters to the emergency clinical data collaboration platform through a low-power wide-area network;

[0070] In this step, the intelligent dressing is a special dressing integrated with sensor technology, which can collect the dynamic sequence of biological parameters such as temperature and humidity at the wound in real time.

[0071] The low-power wide-area network is a communication technology designed for the Internet of Things, suitable for long-distance data transmission with low energy consumption.

[0072] The emergency clinical data collaboration platform is a centralized management system for storing and analyzing medical data from different sources.

[0073] A dynamic sequence of biological parameters refers to a series of data on the environmental state of the wound site collected from intelligent dressing sensors, such as information on temperature, humidity, etc. that changes over time. These data are used to monitor the wound healing process and detect signs of infection at an early stage.

[0074] In the embodiments of the present application, first, an intelligent dressing integrated with sensors is applied to the wound site, and these sensors are responsible for monitoring changes in the wound environment. Secondly, the data collected are preliminarily processed by a microprocessor built into the dressing, and the processed information is sent to the emergency clinical data collaboration platform through a low-power wide-area network. Then, after the platform receives data from multiple intelligent dressings, specific algorithms are used for further analysis to identify any potential problems or trends.

[0075] A patient suffered severe abrasions on the leg due to a traffic accident and received debridement surgery in the hospital emergency room. After that, the doctor covered the wound with an intelligent dressing. As the patient moved, the intelligent dressing continuously monitored the biological parameters of the wound area and transmitted this information to the hospital's data collaboration platform through a low-power wide-area network. This enabled medical staff to understand the wound healing situation in real time even when not beside the patient.

[0076] 102. Screen the drug release rate curve according to the actual pore size of the antibacterial coating of the intelligent dressing. The pore size-drug sustained release mapping relationship in the antibacterial coating database defines the non-linear decay characteristics of the drug release curve associated when the pore size meets the preset conditions;

[0077] In this step, the antibacterial coating refers to a layer of material coated on the surface of the dressing with the ability to inhibit bacterial growth. The pore size refers to the size of the micro-channels in the coating, which directly affects whether drug molecules can smoothly pass through the coating and be released to the wound.

[0078] The infection risk quantification parameter associated with the drug release rate curve refers to the relationship between the drug release rate determined based on the pore size of the antibacterial coating and the infection risk. This relationship helps to precisely control the amount of drug released to most effectively combat possible bacterial infections.

[0079] The pore size-drug sustained release mapping relationship describes the variation law of the drug release rate under different pore size dimensions. This mapping helps to precisely control the amount of drug released.

[0080] In the embodiments of the present application, first, the most suitable drug release rate curve for the current wound condition is screened according to the actual pore size of the antibacterial coating. Secondly, the pore size-drug sustained release mapping relationship in the antibacterial coating database is used to determine the associated drug release curve and its non-linear decay characteristics when the pore size meets specific conditions. Then, based on this information, the design of the antibacterial coating is adjusted to ensure that it can release drugs at the expected speed within the preset time.

[0081] Continuing with the above example, the doctor found that there were slight signs of infection in the patient's wound and decided to use an antibacterial coated dressing with a specific pore size. This dressing can slowly release antibiotics to effectively combat possible bacterial infections. At the same time, since the smart dressing provides real-time feedback on the wound status, the doctor can adjust the treatment plan at any time according to the actual situation.

[0082] 103. Based on the historical dataset of trauma infections in medical institutions, a cross-institutional infection risk evolution model is constructed using the federated learning framework to generate infection risk quantification parameters associated with the dynamic sequence of the biological parameters and the drug release rate curve.

[0083] In this step, the historical dataset of trauma infections is the historical records of post-trauma infections collected from multiple medical institutions. These data are used to construct a cross-institutional infection risk evolution model to predict the likelihood of an individual getting infected.

[0084] The federated learning framework is a distributed machine learning method that allows models to collaborate in training without sharing local data, thus protecting privacy.

[0085] The cross-institutional infection risk evolution model is a model established based on the historical data provided by multiple medical institutions to predict the likelihood of an individual getting infected.

[0086] In the embodiments of the present application, first, historical data related to trauma infections are collected from different medical institutions. Second, these data are integrated using the federated learning framework without directly exchanging the original data content to jointly construct a general infection risk prediction model. Then, based on this model, infection risk quantification parameters related to the dynamic sequence of biological parameters and the drug release rate curve are generated to provide support for personalized treatment.

[0087] Combined with similar cases in the patient's region, the trauma infection records of several surrounding hospitals are analyzed through the federated learning framework to formulate a personalized infection risk assessment report for the patient. This not only helps doctors better understand the specific situation of the patient but also provides a scientific basis for formulating preventive measures.

[0088] 104. Perform multi-source collaborative analysis on the dynamic sequence of the biological parameters, the drug release rate curve, and the infection risk quantification parameters, where the transmission delay parameter of the low-power wide-area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework.

[0089] In this step, multi-source collaborative analysis involves the process of comprehensively analyzing data from multiple sources, such as dynamic sequences of biological parameters, drug release rate curves, and quantified parameters of infection risk. The aim is to identify patterns and connections hidden in the data in order to more precisely adjust the treatment strategy.

[0090] The abnormal association threshold is a key indicator for defining the range of normal and abnormal physiological parameters, ensuring the accuracy of the early warning system.

[0091] In the embodiment of the present application, first, all available data sources are aggregated, including dynamic sequences of biological parameters, drug release rate curves, and quantified parameters of infection risk. Secondly, these data are mined through advanced data analysis tools to identify potential patterns and connections. Then, the treatment strategy is dynamically adjusted according to the analysis results, such as changing the drug release rate or updating the infection warning level, to cope with the changing situation.

[0092] As the patient's recovery process progresses, the intelligent dressing continuously provides the latest wound condition data. With the help of multi-source collaborative analysis, the medical team promptly detected early signs of infection and quickly took actions, such as increasing the local antibiotic dosage, to prevent the condition from deteriorating.

[0093] 105. Generate a trauma infection warning instruction and an emergency data sharing instruction according to the results of multi-source collaborative analysis. The emergency data sharing instruction pushes clinical decision-making auxiliary information to the designated emergency unit through a low-power wide-area network.

[0094] In this step, the trauma infection warning instruction is a set of guiding information used to notify medical staff of the upcoming infection risk.

[0095] The emergency data sharing instruction is to promote information circulation between different departments, ensuring that each participant can obtain the latest and most accurate patient information in order to make a quick response.

[0096] The clinical decision-making auxiliary information is a set of guiding suggestions generated based on the analysis results of the previous steps, aiming to help doctors make more informed treatment choices.

[0097] In the embodiment of the present application, first, specific warning and sharing instructions are generated based on all the analysis results obtained in the previous steps. Secondly, these instructions and related auxiliary information are pushed to the designated emergency unit through a low-power wide-area network. Then, the emergency unit immediately activates the corresponding emergency plan and prepares the necessary medical resources after receiving the information. The clinical decision-making auxiliary information plays a key role in this process, providing immediate treatment suggestions, while the abnormal association threshold ensures the accuracy of the warning.

[0098] Once the system detects an increase in the patient's infection risk, it immediately triggers the warning mechanism and sends an emergency notice to the emergency department. After receiving the message, the emergency department quickly organizes its forces, prepares the required drugs and equipment, and waits for the patient to arrive, greatly shortening the response time to emergencies. The clinical decision-making assistance information also prompts the doctor with the current best treatment plan, enhancing the trust between doctors and patients. This method not only improves the emergency handling ability but also enhances the overall treatment effect.

[0099] In summary, steps 101 to 105 form a closed-loop wound infection warning system by integrating core links such as real-time monitoring of intelligent dressings, drug release regulation, cross-institutional federated learning model construction, and multi-source data collaborative analysis. The intelligent dressing realizes dynamic acquisition and transmission of biological parameters, and the pore size regulation of the antibacterial coating ensures the spatio-temporal matching of drug release and wound status. The federated learning framework improves the generalization ability of infection risk prediction while protecting privacy. The multi-source collaborative analysis module realizes real-time optimization of treatment strategies through dynamic parameter adjustment. Finally, a full-process emergency treatment system covering data acquisition, model training, risk warning, and decision sharing is constructed, significantly enhancing the timeliness of wound infection warning and the scientificity of clinical decision-making.

[0100] To further improve the accurate prediction and real-time response ability of the infection risk at the wound site, this solution proposes a method based on multi-source collaborative analysis. By comprehensively analyzing the dynamic sequence of biological parameters, the drug release rate curve, and the infection risk quantification parameters, refined management during the wound treatment process is achieved. In some embodiments, in step 104, the multi-source collaborative analysis of the dynamic sequence of biological parameters, the drug release rate curve, and the infection risk quantification parameters is performed, where the transmission delay parameter of the low-power wide-area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework, including:

[0101] 201. Establish a delay compensation mechanism for the transmission delay parameter to the effective time window according to the correspondence between the transmission delay parameter of the low-power wide-area network and the effective time window of the drug release rate curve, and generate a dynamic adjustment factor;

[0102] In step 201, the transmission delay parameter of the low-power wide-area network refers to the time required for data to be transmitted from the intelligent dressing to the emergency clinical data collaboration platform. The effective time window of the drug release rate curve refers to the time period when the drug starts to take effect. According to the correspondence between the two, a delay compensation mechanism for the transmission delay parameter to the effective time window is established, and a dynamic adjustment factor is generated. This dynamic adjustment factor is used to compensate for the error in drug release time caused by transmission delay, ensuring that the drug can be released at the optimal time.

[0103] In the embodiments of the present application, first, through the research on a large amount of transmitted data, it is found that there are differences in transmission delays in different environments. Therefore, it is necessary to adjust the effective time window of the drug release rate curve according to specific transmission conditions. Specifically, collect the transmission delay data from different regions and analyze its distribution. Secondly, a mathematical model is established based on these data to simulate the impact of transmission delay on drug release. Then, use this model to calculate the optimal effective time window for a specific transmission delay. Finally, generate a dynamic adjustment factor to automatically adjust the time window of drug release in actual applications to ensure that the drug can be released on time and maximize the therapeutic effect.

[0104] 202. Perform phase synchronization processing on the dynamic adjustment factor and the periodic change trend of the biological parameter dynamic sequence to generate a synchronized biological dynamic sequence, and couple it with the dynamic adjustment factor to generate a biological dynamic coupling coefficient.

[0105] In step 202, the dynamic adjustment factor is generated according to the transmission delay parameter and is used to adjust the effective time window of the drug release rate curve. The synchronized biological dynamic sequence is the result of performing phase synchronization processing on the dynamic adjustment factor and the periodic change trend of the biological parameter dynamic sequence. The biological dynamic coupling coefficient is an index of the interaction strength between the synchronized biological dynamic sequence and the dynamic adjustment factor, reflecting the coordination degree between the two.

[0106] In the embodiments of the present application, first, obtain the biological parameter dynamic sequence of the patient, such as the change trends of temperature, humidity, etc. over time. Secondly, combine these data with the dynamic adjustment factor and perform phase synchronization processing to generate a synchronized biological dynamic sequence. The key to this step is to find a suitable synchronization algorithm so that the change trend of the biological parameter can perfectly match the time window of drug release. Then, calculate the biological dynamic coupling coefficient to evaluate the effect after synchronization. This not only helps to understand the relationship between biological parameters and drug release but also provides a basis for subsequent control strategies. Finally, verify the effectiveness of the synchronized biological dynamic sequence through experiments to ensure its stable operation in actual operations.

[0107] 203. Generate a collaborative control variable according to the linear superposition result of the infection risk quantification parameter and the biological dynamic coupling coefficient. The collaborative control variable dynamically adjusts the release gradient of the drug release rate curve and the termination boundary of the effective time window through a feedback link. The infection risk quantification parameter corrects the abnormal correlation threshold of the acid-base value and temperature fluctuation through the model weight of the federated learning framework.

[0108] In step 203, the collaborative control variable is a new control variable generated by linearly superimposing the infection risk quantification parameter and the biological dynamics coupling coefficient. The release gradient of the drug release rate curve and the termination boundary of the effective time window are dynamically adjusted through the feedback link. This process aims to optimize the drug release strategy and, at the same time, use the federated learning framework to correct the abnormal correlation threshold of the pH value and temperature fluctuations to improve the accuracy of infection warning.

[0109] In the embodiments of the present application, first, the weight ratio between the infection risk quantification parameter and the biological dynamics coupling coefficient is determined to form a collaborative control variable. Secondly, the collaborative control variable is applied to the feedback control system to adjust the release gradient of the drug release rate curve. This step requires accurate calculation of the specific values of each variable to ensure the stability of the system. Then, according to the latest dynamic sequence of biological parameters, the termination boundary of the effective time window is updated to ensure that the drug can exert the maximum effect within the most suitable time. Finally, through the federated learning framework, the abnormal correlation threshold of the pH value and temperature fluctuations is continuously optimized to improve the system's ability to identify infection risks.

[0110] 204. Nonlinearly constrain the collaborative control variable and the synchronized biological dynamics sequence to generate a release frequency peak for updating the drug release rate curve and a delay compensation threshold for the effective time window;

[0111] In step 204, the nonlinear constraint is a method of combining the collaborative control variable and the synchronized biological dynamics sequence, which is used to generate a release frequency peak for updating the drug release rate curve and a delay compensation threshold for the effective time window. This method can more precisely control the speed and timing of drug release, thereby improving the treatment effect.

[0112] In the embodiments of the present application, first, define the nonlinear constraint conditions to ensure that the interaction between the collaborative control variable and the synchronized biological dynamics sequence meets the expected goals. Secondly, based on these constraint conditions, calculate the release frequency peak of the drug release rate curve to achieve the optimal treatment effect. Then, according to the real-time monitored data, adjust the delay compensation threshold of the effective time window to ensure the accuracy of the drug release time point. Finally, through continuous testing and adjustment, verify the effectiveness of the nonlinear constraint to ensure that the entire system can operate stably in a complex medical environment.

[0113] 205. Iteratively match the delay compensation threshold with the real-time change amount of the transmission delay parameter to update the compensation weight of the dynamic adjustment factor and form a closed-loop dynamic adjustment link.

[0114] In step 205, the delay compensation threshold is generated based on the nonlinear constraint between the collaborative control variable and the synchronized biodynamic sequence, and is used to compensate for the drug release time error caused by transmission delay. This threshold is iteratively matched with the real-time change amount of the transmission delay parameter to update the compensation weight of the dynamic adjustment factor, forming a closed-loop dynamic adjustment link. This ensures that the best drug release effect can be maintained even when the network conditions change.

[0115] In the embodiments of the present application, first, an initial delay compensation threshold is set and matched with the current transmission delay parameter. Secondly, as the patient's state changes, the real-time change amount of the transmission delay parameter is continuously monitored, and the delay compensation threshold is adjusted accordingly. Then, based on the adjusted threshold, the compensation weight of the dynamic adjustment factor is updated to ensure that the drug release rate curve is always in the best state. Finally, through the closed-loop dynamic adjustment link, the performance of the entire system is continuously optimized to improve the success rate of treatment.

[0116] The following is a specific example:

[0117] A patient suffered severe abrasions on the leg due to a traffic accident. After undergoing debridement surgery in the hospital emergency room, the doctor covered the wound with a smart dressing. As the patient moved, the smart dressing continuously monitored the biological parameters of the wound area and transmitted this information to the hospital's data collaboration platform through a low-power wide-area network. Based on the transmission delay parameter, the system automatically generated a dynamic adjustment factor to adjust the effective time window of the drug release rate curve. Subsequently, a synchronized biodynamic sequence was generated through phase synchronization processing, and the biodynamic coupling coefficient was calculated. Next, the collaborative control variable was used to adjust the release gradient of the drug release rate curve and the termination boundary of the effective time window, and at the same time, the abnormal correlation threshold of the pH value and temperature fluctuation was corrected through the federated learning framework. Finally, a delay compensation threshold for updating the release frequency peak and effective time window of the drug release rate curve was generated through nonlinear constraints to ensure the best drug release effect.

[0118] To sum up, through steps 201 to 205, the multi-source collaborative analysis technology realizes the precise monitoring of the biological parameters of the trauma site, the fine regulation of the drug release rate, and the accurate prediction of the infection risk, significantly improving the effect and safety of trauma treatment. In particular, through the design of the dynamic adjustment factor and the closed-loop dynamic adjustment link, the problem caused by transmission delay is effectively solved, ensuring that the drug can exert the maximum effect at the best time, greatly improving the patient's recovery speed and treatment experience.

[0119] To further improve the accuracy and response speed of drug release at the trauma site, this solution proposes a non-linear constraint method based on collaborative control variables and synchronized biological dynamic sequences. By coupling and correlating the temporal change rate with the dynamic fluctuation characteristics, the treatment effect is ensured to be maximized. In some embodiments, the non-linear constraint of the collaborative control variables and the synchronized biological dynamic sequences in step 204 to generate the release frequency peak for updating the drug release rate curve and the delay compensation threshold of the effective time window includes:

[0120] 301. Couple and correlate the temporal change rate of the collaborative control variables with the dynamic fluctuation characteristics of the synchronized biological dynamic sequences to generate a set of dynamic coupling parameters;

[0121] In step 301, the collaborative control variables refer to various factors affecting the drug release rate, such as temperature, pH value, etc. The temporal change rate describes the change speed of these variables over time. The synchronized biological dynamic sequence refers to a data sequence synchronized according to the internal physiological rhythms of the organism (such as heartbeat, breathing). The dynamic fluctuation characteristics refer to the fluctuation patterns and rules in this sequence. The set of dynamic coupling parameters is a series of parameters generated by analyzing the interaction relationships between the above elements and is used to precisely control the drug release rate subsequently.

[0122] In the embodiments of this application, first, various environmental parameters affecting drug release and their change trends are collected. Then, high-precision sensors are used to monitor the patient's physiological signals in real time to construct a synchronized biological dynamic sequence. Then, data analysis techniques are applied to identify the dynamic fluctuation characteristics in these sequences and combine them with the temporal change rate to generate a set of dynamic coupling parameters. For example, in a case of treating cardiovascular diseases, the research team monitored the patient's heart rhythm and the change of the pH value in the body, and based on this, a preliminary set of dynamic coupling parameters was established.

[0123] 302. Perform multi-level parameter decomposition on the set of dynamic coupling parameters, and perform dynamic weighting on the multi-level parameter decomposition results through the time slice division results of the synchronized biological dynamic sequence to generate a set of decomposed parameters;

[0124] In step 302, the set of dynamic coupling parameters contains information at multiple levels, and its core features need to be extracted through multi-level parameter decomposition. The time slice division result is the result of dividing the synchronized biological dynamic sequence according to a specific time period. The dynamic weighting process is an algorithm that assigns different weights to the decomposed parameters according to the importance of the time slices to form a set of decomposed parameters. This step helps to more accurately reflect the drug release requirements in different time periods.

[0125] In the embodiments of the present application, first, the dynamic coupling parameter set is decomposed at multiple levels to extract the core features of each subset. Then, based on the time slice division result of the synchronized biological dynamic sequence, the dynamic weighted processing algorithm is used to weight these subsets. Finally, the weighted results are integrated to form the final decomposition parameter set. For example, in the aforementioned case, the researchers found that the physiological signals fluctuated greatly during certain time periods, so higher weights were given to the data in that time period, thereby optimizing the drug release strategy.

[0126] 303. Divide the synchronized biological dynamic sequence into a time slice sequence, extract the dynamic fluctuation feature vector of the time slice sequence, and perform a cross operation on the dynamic fluctuation feature vector and the decomposition parameter set to generate a modulation parameter set;

[0127] In step 303, the time slice sequence is a data segment arranged in chronological order extracted from the synchronized biological dynamic sequence. The dynamic fluctuation feature vector is a numerical representation describing the fluctuation characteristics of each time slice sequence. The cross operation refers to the process of combining the dynamic fluctuation feature vector and the decomposition parameter set to generate a modulation parameter set. This step aims to adjust the drug release rate according to the real-time changes of physiological signals.

[0128] In the embodiments of the present application, first, a time slice sequence is extracted from the synchronized biological dynamic sequence, and the dynamic fluctuation feature vector of each time slice is calculated. Then, the cross operation algorithm is applied to combine these vectors with the decomposition parameter set to generate a modulation parameter set. For example, in actual operation, the research team found that the patient's heart rate was relatively stable at night, so the drug release rate was adjusted accordingly to ensure the best therapeutic effect.

[0129] 304. Input the modulation parameter set into the constraint generator in the non-linear constraint model, and adjust the weight distribution of the modulation parameter set through an iterative feedback mechanism to generate an initial release frequency peak and an initial delay compensation threshold;

[0130] In step 304, the non-linear constraint model is a mathematical model used to describe the behavior of the system under given conditions. The constraint generator is a component of this model responsible for generating appropriate constraint conditions according to the input modulation parameter set. The iterative feedback mechanism is an optimization algorithm that repeatedly adjusts the weight distribution of the modulation parameter set until the optimal solution is reached. The initial release frequency peak and the initial delay compensation threshold are key parameters determined through this process.

[0131] In the embodiments of the present application, first, the modulation parameter set is input into the non-linear constraint model, and the constraint generator is started to generate preliminary constraint conditions. Then, an iterative feedback mechanism is applied to continuously adjust the weight distribution of the modulation parameter set until the optimal initial release frequency peak and delay compensation threshold are found. For example, in a specific case, after multiple iterations, the researchers successfully found the optimal release frequency and delay compensation settings that can not only ensure the drug efficacy but also reduce side effects.

[0132] 305. Jointly optimize the initial release frequency peak and the initial delay compensation threshold. By constraining the dynamic balance condition of the synchronized biodynamic sequence and the boundary condition of the cooperative control variable, correct the phase shift of the initial release frequency peak and the window length of the initial delay compensation threshold, and generate an optimized release frequency peak and delay compensation threshold parameter group.

[0133] In step 305, joint optimization refers to the process of simultaneously adjusting the initial release frequency peak and the initial delay compensation threshold. The dynamic balance condition is one of the key factors to ensure the stability of the drug release process. The boundary condition defines the effective range of the cooperative control variable. Correcting the phase shift and the window length is to make the drug release more in line with the physiological needs of the patient. The optimized parameter group is finally mapped to the drug release rate curve update process to achieve delay compensation.

[0134] In the embodiments of the present application, first, jointly optimize the initial release frequency peak and the delay compensation threshold to ensure that the dynamic balance condition and the boundary condition are met. Then, adjust the drug release rate curve according to the optimization result, especially for fine-tuning the delay compensation threshold within the effective time window. For example, in the management of a chronic disease, the doctor formulates a personalized drug release plan based on the optimized parameter group, significantly improving the treatment effect.

[0135] 306. Map the optimized release frequency peak and delay compensation threshold parameter group to the update process of the drug release rate curve to achieve the delay compensation threshold of the drug release rate curve within the effective time window.

[0136] In step 306, the optimized release frequency peak and delay compensation threshold parameter group is the optimal parameter combination obtained through the previous steps. The mapping process involves applying these parameters to the update of the drug release rate curve to achieve precise control of the drug release rate. The effective time window refers to a specific time period in the drug release rate curve, during which the delay compensation threshold is used to fine-tune the drug release rate to adapt to the actual needs of the patient.

[0137] In the embodiments of the present application, first, the optimized release frequency peak and the delay compensation threshold parameter group are applied to the update algorithm of the drug release rate curve. Then, according to the real-time physiological data of the patient, the drug release rate is dynamically adjusted to ensure the best therapeutic effect within the effective time window. For example, in a diabetes management case, doctors use this method to achieve precise control of the insulin release rate and effectively control blood sugar levels.

[0138] The following is a specific example:

[0139] A patient suffered severe abrasions on the leg due to a traffic accident. After undergoing debridement surgery in the hospital emergency room, the doctor covered the wound with a smart dressing. The system automatically generated a dynamic adjustment factor to adjust the effective time window of the drug release rate curve. Subsequently, by analyzing the cooperative control variables and the time series change rate, and combining with the synchronized biological dynamic sequence, a set of dynamic coupling parameters was generated. Then, multi-level parameter decomposition and dynamic weighting processing were performed to generate a set of decomposed parameters. The dynamic fluctuation feature vector of the time slice sequence was cross-operated with the set of decomposed parameters to generate a set of modulation parameters. Through iterative optimization of the non-linear constraint model, the optimized release frequency peak and the delay compensation threshold parameter group were finally determined and applied to the update of the drug release rate curve to ensure the best therapeutic effect. This not only improves the patient's recovery speed but also enhances the safety and effectiveness of the treatment.

[0140] In summary, through steps 301-306, the cooperative control variables, the time series change rate, and the synchronized biological dynamic sequence are coupled and associated, and a series of operations such as multi-level parameter decomposition, dynamic weighting processing, and cross-operation are performed on them. Finally, the optimized release frequency peak and the delay compensation threshold parameter group are generated. This method greatly improves the accuracy and efficiency of the drug release process, makes the drug release more in line with the physiological needs of individual patients, thereby effectively improving the therapeutic effect and reducing side effects. This method demonstrates the great potential of the combination of modern medical engineering and bioinformatics, provides strong support for personalized medicine, and helps to achieve more efficient and safe treatment goals.

[0141] In order to further optimize the accuracy and timeliness of drug release at the trauma site, this solution proposes a combined optimization method. By constraining the dynamic equilibrium conditions of the synchronized biological dynamic sequence and the boundary conditions of the co-control variables, the phase shift of the initial release frequency peak and the window length of the initial delay compensation threshold are corrected. In some embodiments, the combined optimization of the initial release frequency peak and the initial delay compensation threshold in step 305 is achieved by constraining the dynamic equilibrium conditions of the synchronized biological dynamic sequence and the boundary conditions of the co-control variables, correcting the phase shift of the initial release frequency peak and the window length of the initial delay compensation threshold, and generating an optimized release frequency peak and delay compensation threshold parameter set, including:

[0142] 401. By constraining the dynamic equilibrium conditions of the synchronized biological dynamic sequence, align the fluctuation change rate of the synchronized biological dynamic sequence with the biological rhythm parameters in the time domain to generate dynamic equilibrium constraint parameters;

[0143] In step 401, the dynamic equilibrium condition constraint of the synchronized biological dynamic sequence refers to the condition that ensures the stability of physiological rhythms (such as heartbeat and breathing) in the body. The fluctuation change rate describes the rate of change of these rhythms over time. The biological rhythm parameters are the numerical values used to characterize the characteristics of these rhythms. Time-domain alignment is the process of aligning these parameters with the actual measurement data to generate dynamic equilibrium constraint parameters. This step helps to accurately adjust the drug release rate to better meet the physiological needs of the patient.

[0144] In the embodiments of the present application, first, a high-precision sensor is used to continuously monitor the physiological signals of the patient to obtain a synchronized biological dynamic sequence. Then, data analysis techniques are used to calculate the fluctuation change rate and biological rhythm parameters of these sequences. Then, a time-domain alignment algorithm is applied to align these parameters with the actual measurement data to generate dynamic equilibrium constraint parameters. For example, in a case of treating cardiovascular diseases, the research team established preliminary dynamic equilibrium constraint parameters by analyzing the patient's heartbeat rhythm in detail to better adjust the drug release rate.

[0145] 402. Establish the boundary condition constraint rule of the co-control variable, and generate a boundary range including the phase deviation and the response window threshold according to the transmission path length and diffusion delay characteristics of the co-control variable in the thermoresponsive spraying structure;

[0146] In step 402, the boundary condition constraint rules for the collaborative control variables define the effective ranges of various factors (such as temperature, pH value) that affect the drug release rate. The transmission path length refers to the distance of these variables from the source to the target location. The diffusion delay characteristic describes the delay of these variables during the transmission process. The phase deviation and the response window threshold are boundary ranges generated based on the above parameters, which are used to define the adjustment range of the drug release rate.

[0147] In the embodiments of the present application, first, the transmission path length and its diffusion delay characteristic of the collaborative control variables are determined. Then, based on this information, boundary condition constraint rules are established to generate a boundary range including the phase deviation and the response window threshold. For example, in the previous case, the researchers found that there was a significant delay effect for some variables during the transmission process, so more strict boundary conditions were set for them to ensure the precise control of the drug release rate.

[0148] 403. Perform the joint optimization iteration of the initial release frequency peak and the initial delay compensation threshold, and based on the dynamic balance constraint parameters and the boundary range, establish a joint optimization objective for the phase synchronization error and the window coverage rate;

[0149] In step 403, the joint optimization iteration is a method to find the optimal solution by adjusting multiple parameters simultaneously. The dynamic balance constraint parameters and the boundary range are two key constraint conditions. The phase synchronization error and the window coverage rate are the objective functions of the optimization, which are used to measure the phase shift of the release frequency peak and the effectiveness of the delay compensation threshold respectively.

[0150] In the embodiments of the present application, first, the initial release frequency peak and the initial delay compensation threshold are set as the starting points. Then, based on the dynamic balance constraint parameters and the boundary range, a joint optimization objective for the phase synchronization error and the window coverage rate is established. Then, these two parameters are adjusted through multiple iterations until the best combination is found. For example, in actual operation, the research team finally found the best release frequency and delay compensation settings that can not only ensure the curative effect but also reduce the side effects by continuously adjusting the drug release rate.

[0151] 404. Correct the phase shift of the initial release frequency peak based on the joint optimization objective, introduce a phase correction factor according to the gradient descent direction of the phase synchronization error, perform waveform shaping on the time-domain distribution of the initial release frequency peak, and generate an optimized initial release frequency peak;

[0152] In step 404, the phase correction factor is a coefficient used to correct the phase shift of the initial release frequency peak. Waveform shaping is a process of adjusting the time-domain distribution of the initial release frequency peak, aiming to generate a more optimized release frequency peak. The gradient descent direction refers to the method of determining the adjustment direction by calculating the error gradient.

[0153] In the embodiments of the present application, first, a phase correction factor is introduced according to the gradient descent direction of the phase synchronization error. Then, this factor is applied to perform waveform shaping on the time-domain distribution of the initial release frequency peak to generate an optimized initial release frequency peak. For example, in a specific case, after multiple adjustments, the researchers successfully found the release frequency peak that can significantly improve the drug efficacy.

[0154] 405. Adjust the window length of the initial delay compensation threshold, calculate the time expansion amount based on the residual term of the window coverage rate, and combine the drug diffusion rate feedback of the thermal response spraying structure to perform non-linear compression compensation on the window length to generate an optimized set of delay compensation threshold parameters.

[0155] In step 405, the residual term of the window coverage rate is an index for evaluating the coverage range of the delay compensation threshold. The time expansion amount is the time adjustment amount calculated based on this index. Non-linear compression compensation is a method for finely adjusting the window length according to the drug diffusion rate feedback to generate an optimized set of delay compensation threshold parameters.

[0156] In the embodiments of the present application, first, calculate the residual term of the window coverage rate and determine the time expansion amount. Then, combine the drug diffusion rate feedback of the thermal response spraying structure to perform non-linear compression compensation on the window length to generate an optimized set of delay compensation threshold parameters. For example, in a diabetes management case, doctors used this method to achieve precise control of the insulin release rate and effectively control the blood sugar level.

[0157] The following is a specific example:

[0158] A patient suffered severe abrasions on the leg due to a traffic accident. After receiving treatment in the hospital emergency room, the doctor used an intelligent dressing to monitor the wound condition in real time. The system automatically generated a dynamic adjustment factor and generated a dynamic balance constraint parameter by analyzing the synchronized biodynamic sequence and the biological rhythm parameters. A boundary range was established based on the transmission path characteristics of the collaborative control variable. Subsequently, the system performed joint optimization iteration, adjusted the phase offset of the initial release frequency peak, and introduced a phase correction factor according to the phase synchronization error to optimize the waveform. At the same time, calculate the time expansion amount according to the residual term of the window coverage rate, and adjust the window length in combination with the drug diffusion rate feedback. Finally, an optimized set of release frequency peak and delay compensation threshold parameters is generated to ensure the best treatment effect and accelerate the patient's recovery speed.

[0159] By combining the dynamic equilibrium conditions of the synchronized biological dynamic sequence with the boundary conditions of the cooperative control variables through steps 401 to 405 and jointly optimizing them, an optimized release frequency peak and delay compensation threshold parameter set are finally generated. This method greatly improves the accuracy and efficiency of the drug release process, makes the drug release more in line with the physiological needs of individual patients, thereby effectively improving the treatment effect and reducing side effects. This method demonstrates the great potential of the combination of modern medical engineering and bioinformatics, provides strong support for personalized medicine, and helps to achieve more efficient and safe treatment goals.

[0160] To further improve the accuracy of predicting the risk of infection at the trauma site and the effectiveness of personalized treatment, this solution proposes a method based on the federated learning framework. A closed-loop feedback mechanism is used to generate infection risk quantification parameters associated with the dynamic sequence of biological parameters and the drug release rate curve in the risk quantification module. In some embodiments, in step 403, based on the trauma infection historical data set of medical institutions, a cross-institutional infection risk evolution model is constructed using the federated learning framework to generate infection risk quantification parameters associated with the dynamic sequence of the biological parameters and the drug release rate curve, including:

[0161] 501. Construct a local dynamic time series model based on the trauma infection historical data set of medical institutions, combine the local features related to the drug release rate curve, and generate a dynamic weight assignment that is passed as an intermediate parameter to the federated aggregation module;

[0162] In step 501, the local dynamic time series model is constructed based on the trauma infection historical data set of a single medical institution, aiming to capture the change trend of biological parameters at the trauma site within a specific time period. The dynamic weight assignment refers to assigning different weights to each local model according to the local features related to the drug release rate curve, so as to more accurately reflect the unique situation of each institution. These intermediate parameters are passed to the federated aggregation module for the construction of the subsequent global model.

[0163] In the embodiments of the present application, first, collect the trauma infection historical data set from each medical institution, including the basic information of patients, wound conditions, and changes in biological parameters during the treatment process. Secondly, construct a local dynamic time series model for the data set of each hospital to analyze the change law of biological parameters over time. Then, combine the local features related to the drug release rate curve, such as the influence of different pore size coatings on drug release, and calculate the dynamic weight assignment of each local model. Finally, pass these local models and their weight assignments as intermediate parameters to the federated aggregation module to provide basic data support for the construction of the global model.

[0164] 502. Integrate the local dynamic time series model in the federated aggregation module, construct a global time series evolution relationship according to the dynamic weight allocation, and generate a global evolution matrix by adjusting the association mode between the dynamic sequence of biological parameters and the drug release rate curve;

[0165] In step 502, the federated aggregation module is responsible for integrating the local dynamic time series models from different medical institutions, and generating a global time series evolution relationship and a global evolution matrix by adjusting the association mode between the dynamic sequence of biological parameters and the drug release rate curve. This process ensures that the global model can comprehensively consider the unique situations of each institution while maintaining overall consistency and accuracy.

[0166] In the embodiment of the present application, first, receive the local dynamic time series models and their dynamic weight allocations from each medical institution. Second, adjust the association mode between the dynamic sequence of biological parameters and the drug release rate curve according to these weight allocations to generate a global time series evolution relationship. Then, construct a global evolution matrix based on the global time series evolution relationship, and this matrix reflects the complex relationships between different biological parameters and between the matrix and the drug release rate. Finally, through continuous iterative optimization, ensure that the global model can reflect the uniqueness of each institution and maintain overall consistency, thereby providing a reliable basis for subsequent risk quantification.

[0167] 503. Transmit the global evolution matrix to the feature coupling module, generate feature coupling coefficients based on the global evolution matrix, and the feature coupling coefficients map the drug release rate curve to the dynamic sequence of biological parameters through multi-modal feature fusion to generate a feature association chain, and the feature association chain is transmitted to the dynamic optimization module as an intermediate parameter;

[0168] In step 503, the feature coupling coefficients are generated based on the global evolution matrix and are used for multi-modal feature fusion, that is, mapping the drug release rate curve to the dynamic sequence of biological parameters to generate a feature association chain. The feature association chain is transmitted to the dynamic optimization module as an intermediate parameter for further optimizing the drug release strategy.

[0169] In the embodiment of the present application, first, receive the global evolution matrix and generate feature coupling coefficients based on this matrix, and these coefficients reflect the interaction intensity between the drug release rate curve and the dynamic sequence of biological parameters. Second, use the feature coupling coefficients for multi-modal feature fusion to map the drug release rate curve to the dynamic sequence of biological parameters to generate a feature association chain. Then, verify the effectiveness of the feature association chain through experiments to ensure that it can operate stably in actual operations. Finally, transmit the feature association chain to the dynamic optimization module as an intermediate parameter to provide a basis for further optimizing the drug release strategy.

[0170] 504. Adjust the phase offset of the drug release rate curve according to the feature coupling coefficient in the dynamic optimization module to generate a dynamic constraint boundary, and transfer the dynamic constraint boundary to the risk quantification module as an intermediate parameter;

[0171] In step 504, the dynamic constraint boundary is generated in the dynamic optimization module by adjusting the phase offset of the drug release rate curve according to the feature coupling coefficient. The optimal time and dose range of drug release are defined and transferred to the risk quantification module as an intermediate parameter for finally generating the infection risk quantification parameter.

[0172] In the embodiment of the present application, first, receive the feature association chain and adjust the phase offset of the drug release rate curve according to the feature coupling coefficient. Second, based on the adjusted phase offset, calculate the dynamic constraint boundary to determine the optimal time and dose range of drug release. Then, verify the rationality and effectiveness of the dynamic constraint boundary through multiple simulation tests. Finally, transfer the dynamic constraint boundary to the risk quantification module as an intermediate parameter to provide a key input for generating the infection risk quantification parameter.

[0173] 505. Construct a cross-institutional infection risk evolution model using the federated learning framework, and generate an infection risk quantification parameter associated with the dynamic sequence of biological parameters and the drug release rate curve through a closed-loop feedback mechanism.

[0174] In step 505, the federated learning framework generates an infection risk quantification parameter associated with the dynamic sequence of biological parameters and the drug release rate curve through a closed-loop feedback mechanism. This method not only protects the data privacy of each medical institution but also effectively improves the accuracy of infection risk prediction.

[0175] In the embodiment of the present application, first, construct a cross-institutional infection risk evolution model in the federated learning framework based on all the intermediate parameters generated in the previous steps. Second, continuously optimize the model parameters through a closed-loop feedback mechanism to ensure that the model can accurately predict the possibility of an individual getting infected. Then, generate an infection risk quantification parameter associated with the dynamic sequence of biological parameters and the drug release rate curve to provide a scientific basis for doctors. Finally, verify the effect of the model through actual application to ensure that it can significantly improve the recovery speed and treatment safety of patients in clinical practice.

[0176] The following is a specific example:

[0177] A patient suffered severe abrasions on the leg due to a traffic accident. After undergoing debridement surgery in the hospital emergency room, the doctor covered the wound with a smart dressing. The system automatically generated a local dynamic time series model and calculated the dynamic weight allocation by combining the relevant features of the drug release rate curve. Subsequently, the federated aggregation module fused these local models to generate a global evolution matrix. Then, the feature coupling module used the global evolution matrix to generate a feature correlation chain and transmitted it to the dynamic optimization module. On this basis, the system adjusted the phase offset of the drug release rate curve to generate a dynamic constraint boundary. Finally, through the federated learning framework, a quantitative infection risk parameter associated with the dynamic sequence of biological parameters and the drug release rate curve was generated to guide the doctor to formulate the best treatment plan.

[0178] In summary, by implementing the solutions of steps 501 to 505, the accurate prediction of the infection risk at the trauma site is achieved through the federated learning framework. Combining the local dynamic time series model, the global evolution matrix, and the feature coupling technology, a quantitative infection risk parameter associated with the dynamic sequence of biological parameters and the drug release rate curve is generated. This not only improves the accuracy of infection risk prediction but also enhances the effectiveness of personalized treatment, significantly improving the patient's recovery speed and treatment experience.

[0179] To further improve the accuracy of predicting the infection risk at the trauma site, this solution proposes a method based on the global evolution matrix and the feature coupling coefficient. By fusing multi-modal features to generate a feature correlation chain with hierarchical dependence, the refined management of the treatment process at the trauma site is realized. In some embodiments, in step 503, the global evolution matrix is transmitted to the feature coupling module, and a feature coupling coefficient is generated based on the global evolution matrix. The feature coupling coefficient maps the drug release rate curve to the dynamic sequence of biological parameters through multi-modal feature fusion to generate a feature correlation chain, including:

[0180] 601. Transmit the global evolution matrix to the feature coupling module, and generate a feature coupling coefficient in the feature coupling module according to the internal structural relationship of the global evolution matrix;

[0181] In step 601, the global evolution matrix is a data structure containing information about the change of the system state over time. The feature coupling coefficient is a set of numerical values generated by analyzing the internal structural relationship of the global evolution matrix, which is used to describe the interaction strength between different variables. These coefficients are crucial for understanding the relationship between the drug release rate curve and the dynamic sequence of biological parameters.

[0182] In the embodiments of the present application, first, the global evolution matrix is transmitted to the feature coupling module. Then, in the feature coupling module, data analysis techniques are used to analyze the internal structural relationship of the global evolution matrix, and the feature coupling coefficient is calculated. Specifically, linear algebra and statistical methods are applied to decompose the matrix, extract key features, and quantify the interaction strength between them. For example, in a case of treating cardiovascular diseases, the research team constructed a preliminary global evolution matrix through a detailed analysis of the physiological signals in the patient's body, and generated a feature coupling coefficient based on this to better adjust the drug release rate.

[0183] 602. Perform a phase shift adjustment on the drug release rate curve based on the feature coupling coefficient. The adjusted drug release rate curve is synchronized with the dynamic sequence of biological parameters in time series to generate a dynamically associated time series sequence.

[0184] In step 602, the phase shift adjustment refers to finely adjusting the drug release rate curve according to the feature coupling coefficient to make it synchronized with the dynamic sequence of biological parameters in time series. The dynamically associated time series sequence is the time alignment result of the adjusted drug release rate curve and the dynamic sequence of biological parameters, which is used for subsequent multi-modal feature fusion.

[0185] In the embodiments of the present application, first, a phase shift adjustment is performed on the drug release rate curve based on the feature coupling coefficient. Then, time series analysis techniques are used to synchronize the adjusted drug release rate curve with the dynamic sequence of biological parameters to generate a dynamically associated time series sequence. For example, the researchers found that the physiological signals fluctuated greatly during certain time periods, so precise phase shift adjustments were made to the data during those periods to ensure that the drug release rate was highly consistent with the patient's physiological needs.

[0186] 603. Input the dynamically associated time series sequence into the feature association chain generation module. Through multi-modal feature fusion, map the drug release rate curve to the dynamic sequence of biological parameters, and iteratively optimize the feature coupling coefficient through a coupling feedback mechanism to generate a feature association chain with a hierarchical dependence relationship.

[0187] In step 603, the feature association chain generation module is an algorithm framework for mapping the drug release rate curve to the dynamic sequence of biological parameters and optimizing the feature coupling coefficient through a coupling feedback mechanism. Multi-modal feature fusion refers to combining the information of multiple data sources to generate a feature association chain with a hierarchical dependence relationship. This chain structure helps to more accurately reflect the complex relationship between the drug release rate and biological parameters.

[0188] In the embodiments of the present application, first, the dynamic association time series is input into the feature association chain generation module. Then, the multi-modal feature fusion technology is applied to map the drug release rate curve to the biological parameter dynamic sequence, and the feature coupling coefficient is iteratively optimized through the coupled feedback mechanism. The machine learning algorithm is used to continuously adjust the feature coupling coefficient until the optimal combination is found. For example, in actual operation, the research team finally found the optimal feature coupling coefficient setting that can not only ensure the curative effect but also reduce the side effects through multiple iterations of optimization, significantly improving the treatment effect.

[0189] The following is a specific example:

[0190] A patient suffered severe abrasions on the leg due to a traffic accident. After undergoing debridement surgery in the hospital emergency room, the doctor covered the wound with a smart dressing. The system automatically generated a global evolution matrix and transmitted it to the feature coupling module. Based on the internal structural relationship of the global evolution matrix, the system generated feature coupling coefficients and used these coefficients to adjust the phase shift of the drug release rate curve to synchronize it with the biological parameter dynamic sequence. Then, the dynamic association time series was input into the feature association chain generation module, and the drug release rate curve was mapped onto the biological parameter dynamic sequence through multi-modal feature fusion. Finally, the feature coupling coefficient was iteratively optimized through the coupled feedback mechanism, generating a feature association chain with a hierarchical dependence relationship, providing a scientific basis for subsequent personalized treatment, and significantly improving the patient's recovery speed and treatment effect.

[0191] In summary, steps 601 to 603 pass the global evolution matrix to the feature coupling module, generate feature coupling coefficients based on this, and then use these coefficients to adjust the phase shift of the drug release rate curve, finally generating a feature association chain with a hierarchical dependence relationship. This method greatly improves the synchronization and accuracy of the drug release process, makes the drug release more conform to the physiological needs of individual patients, thereby effectively improving the treatment effect and reducing side effects. This method demonstrates the great potential of the combination of modern medical engineering and bioinformatics, provides strong support for personalized medicine, and helps to achieve more efficient and safe treatment goals.

[0192] In order to further improve the accuracy of trauma infection warning and the efficiency of emergency data sharing, this solution proposes a method based on the results of multi-source collaborative analysis. The calibrated warning trigger threshold and push priority sequence are pushed to the designated emergency unit through the low-power wide-area network to ensure the best treatment effect. In some embodiments, in step 105, the trauma infection warning instruction and the emergency data sharing instruction are generated according to the results of multi-source collaborative analysis. The emergency data sharing instruction pushes clinical decision-making assistance information to the designated emergency unit through the low-power wide-area network, including:

[0193] 701. Generate a trauma infection risk level parameter based on the superposition relationship between the biological dynamic coupling coefficient and the infection risk quantification parameter in the multi-source collaborative analysis result, and generate a priority evaluation parameter in combination with the load status parameter of the emergency unit;

[0194] In step 701, the trauma infection risk level parameter is generated based on the superposition relationship between the biological dynamic coupling coefficient and the infection risk quantification parameter in the multi-source collaborative analysis result, and is used to evaluate the risk degree of a patient suffering from trauma infection. The priority evaluation parameter combines the load status parameter of the emergency unit, takes into account the current emergency resource allocation situation, and generates a priority score for emergency treatment for each patient. These parameters work together to help the medical team quickly identify and respond to high-risk cases.

[0195] In the embodiment of the present application, first, collect the biological dynamic coupling coefficient and the infection risk quantification parameter in the multi-source collaborative analysis result, and calculate the trauma infection risk level parameter. Secondly, combine the load status of the emergency unit (such as the current number of waiting patients, medical staff configuration, etc.) to evaluate the priority evaluation parameter of each patient. Then, input these parameters into the system to automatically generate a priority score for each patient. Finally, according to the scoring results, the system can quickly identify high-risk cases that need to be processed immediately, ensuring the most effective use of limited emergency resources.

[0196] 702. Generate a trauma infection warning instruction and an emergency data sharing instruction according to the multi-source collaborative analysis result, and construct an auxiliary information aggregation template based on the historical response efficiency of the priority evaluation parameter and the clinical decision-making assistance information;

[0197] In step 702, the trauma infection warning instruction is a guiding suggestion generated according to the multi-source collaborative analysis result, aiming to notify medical staff of the upcoming infection risk. The emergency data sharing instruction is to promote information circulation between different departments to ensure that each participant can obtain the latest and most accurate patient information. The auxiliary information aggregation template is constructed based on the historical response efficiency of the priority evaluation parameter and the clinical decision-making assistance information, optimizes the push order of the clinical decision-making assistance information, and improves the emergency handling ability.

[0198] In the embodiment of the present application, first, generate a trauma infection warning instruction and an emergency data sharing instruction based on the multi-source collaborative analysis result. Secondly, combine the priority evaluation parameter and the historical response efficiency data to construct an auxiliary information aggregation template and optimize the push order of the clinical decision-making assistance information. Then, send these instructions and templates to each relevant department to ensure that all participants can obtain the necessary information in a timely manner. Finally, verify the effectiveness of this method through practical applications to ensure efficient information transmission and response in a complex emergency environment.

[0199] 703. Dynamically match the trauma infection warning instruction with the label mapping relationship of the auxiliary information aggregation template to generate a decision matching degree index, and adjust the push priority sequence of the clinical decision-making auxiliary information through a low-power wide-area network according to the decision matching degree index;

[0200] In step 703, the decision matching degree index is generated by dynamically matching the trauma infection warning instruction with the label mapping relationship of the auxiliary information aggregation template, and is used to adjust the push priority sequence of the clinical decision-making auxiliary information. This process ensures that key information can be pushed to relevant medical staff in an orderly manner according to the urgency, improving the speed and accuracy of responding to emergencies.

[0201] In the embodiment of the present application, first, dynamically match the trauma infection warning instruction with the labels of the auxiliary information aggregation template to generate a decision matching degree index. Second, based on these indexes, adjust the push priority sequence of the clinical decision-making auxiliary information to ensure that the information of high-risk cases is conveyed to medical staff first. Then, the push priority sequence is updated in real time through a low-power wide-area network to ensure the timeliness and accuracy of information transmission. Finally, verify the effect of this mechanism through multiple simulation tests to ensure that it can operate stably in actual operation and improve the emergency treatment efficiency.

[0202] 704. Dynamically calibrate the trauma infection risk level parameter according to the deviation range between the decision matching degree index and the multi-source collaborative analysis result to generate a calibrated warning trigger threshold;

[0203] In step 704, the calibrated warning trigger threshold is dynamically calibrated according to the deviation range between the decision matching degree index and the multi-source collaborative analysis result, and is used to more accurately predict the trauma infection risk. This process ensures the sensitivity and specificity of the warning system, avoids over-alarming or missing alarms, and improves the reliability and practicability of the system.

[0204] In the embodiment of the present application, first, collect the deviation range between the decision matching degree index and the multi-source collaborative analysis result as the calibration basis. Second, dynamically calibrate the trauma infection risk level parameter based on these data to generate a calibrated warning trigger threshold. Then, through continuous iterative optimization, ensure that the warning trigger threshold can not only accurately reflect the actual situation of the patient, but also adapt to changes in different environments. Finally, apply the calibrated warning trigger threshold to the actual system, and verify its effectiveness through real cases to ensure that the system can provide reliable warning information in various situations.

[0205] 705. Push the clinical decision-making auxiliary information to the designated emergency unit through the low-power wide-area network with the calibrated warning trigger threshold and the push priority sequence.

[0206] In step 705, the calibrated early warning trigger threshold and the push priority sequence are used to push clinical decision-making assistance information to the designated emergency unit via a low-power wide-area network. This process ensures the efficient transmission of information, enabling the emergency unit to receive and process high-risk cases in a timely manner, and improving the overall medical response speed and quality.

[0207] In the embodiment of the present application, first, the calibrated early warning trigger threshold and the push priority sequence are integrated and prepared for transmission via a low-power wide-area network. Secondly, appropriate transmission times and frequencies are selected to ensure that the information can reach the designated emergency unit in a timely manner. Then, the clinical decision-making assistance information is pushed to the emergency unit via the low-power wide-area network to ensure that medical staff can quickly obtain the latest patient information and treatment suggestions. Finally, the effectiveness of this method is verified through practical applications to ensure that it can significantly improve the medical response speed and quality in the emergency environment.

[0208] The following is a specific example:

[0209] A patient suffered severe abrasions on the leg due to a traffic accident and received debridement surgery in the hospital emergency room. After that, the doctor covered the wound with a smart dressing. The system automatically generated trauma infection risk level parameters and combined them with the load status of the emergency unit to generate priority evaluation parameters. Subsequently, the system generated trauma infection early warning instructions and emergency data sharing instructions, and constructed an auxiliary information aggregation template. Then, a decision matching degree index was generated through dynamic matching to adjust the push priority sequence of the clinical decision-making assistance information. Then, the system dynamically calibrated the trauma infection risk level parameters according to the decision matching degree index to generate a calibrated early warning trigger threshold. Finally, the system pushed the calibrated early warning trigger threshold and the push priority sequence to the designated emergency unit via a low-power wide-area network to ensure the best treatment effect.

[0210] In summary, by adopting the above steps 701 to 705, through multi-source collaborative analysis and low-power wide-area network technology, accurate early warning of trauma infection risk and efficient sharing of emergency data are achieved. It not only improves the efficiency and accuracy of emergency treatment, but also enhances the communication and trust between doctors and patients. Especially in a complex emergency environment, this solution can significantly improve the medical response speed and quality, and provide more secure and efficient medical services for patients.

[0211] Figure 2 The following is a schematic structural diagram of an emergency clinical data real-time sharing system based on the Internet of Things provided by the embodiment of the present application. As Figure 2 shown, the device includes:

[0212] An acquisition module 21, which collects the dynamic sequence of biological parameters of the trauma site in real time through a smart dressing integrated with an antibacterial coating, and transmits the dynamic sequence of biological parameters to the emergency clinical data collaboration platform via a low-power wide-area network;

[0213] A screening module 22 screens a drug release rate curve according to the actual pore size of the antibacterial coating of the intelligent dressing. The pore size-drug sustained release mapping relationship in the antibacterial coating database defines the non-linear attenuation characteristics of the drug sustained release curve associated when the pore size meets a preset condition;

[0214] A construction module 23 constructs a cross-institutional infection risk evolution model based on the historical dataset of trauma infections in medical institutions by using a federated learning framework, and generates an infection risk quantification parameter associated with the dynamic sequence of biological parameters and the drug release rate curve;

[0215] An analysis module 24 performs multi-source collaborative analysis on the dynamic sequence of biological parameters, the drug release rate curve, and the infection risk quantification parameter. Among them, the transmission delay parameter of the low-power wide-area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework;

[0216] A generation module 25 generates a trauma infection warning instruction and an emergency data sharing instruction according to the multi-source collaborative analysis result. The emergency data sharing instruction pushes clinical decision-making auxiliary information to a designated emergency unit through a low-power wide-area network.

[0217] Figure 2 The described real-time emergency clinical data sharing system based on the Internet of Things can execute Figure 1 The described real-time emergency clinical data sharing method based on the Internet of Things in the illustrated embodiment. Its implementation principle and technical effects will not be elaborated. For the real-time emergency clinical data sharing system based on the Internet of Things in the above embodiment, the specific ways for each module and unit to execute operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0218] In a possible design, Figure 2 The real-time emergency clinical data sharing system based on the Internet of Things in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown. This computing device can include a storage component 31 and a processing component 32;

[0219] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32.

[0220] The processing component 32 is used for the Figure 1 real-time emergency clinical data sharing method based on the Internet of Things in the above

[0221] 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 methods. 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 methods.

[0222] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may 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.

[0223] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0224] 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.

[0225] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0226] 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.

[0227] 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 shown embodiment of a method for real-time sharing of emergency clinical data based on the Internet of Things.

[0228] Those skilled in the art can clearly understand that for the convenience and conciseness 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 described herein again.

[0229] 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 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 efforts.

[0230] 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. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can 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.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended 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 described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real-time sharing method for emergency clinical data based on the Internet of Things, characterized in that: include: The dynamic sequence of biological parameters of the wound site is collected in real time through the smart dressing with integrated antibacterial coating, and the dynamic sequence of biological parameters is transmitted to the emergency clinical data collaboration platform through the low-power wide area network; The drug release rate curve is screened according to the actual pore size of the antibacterial coating of the smart dressing, and the pore size-drug sustained release mapping relationship in the antibacterial coating database defines the nonlinear attenuation characteristics of the associated drug sustained release curve when the pore size meets the preset conditions; Based on the historical dataset of trauma infection in medical institutions, a federated learning framework is used to build a cross-institutional infection risk evolution model to generate quantitative infection risk parameters associated with the dynamic sequence of biological parameters and drug release rate curves; The biological parameter dynamic sequence, the drug release rate curve and the infection risk quantification parameter are subjected to multi-source collaborative analysis, wherein the transmission delay parameter of the low power wide area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework; A trauma infection warning instruction and an emergency data sharing instruction are generated based on the multi-source collaborative analysis results. The emergency data sharing instruction pushes clinical decision-making auxiliary information to a designated emergency unit via a low-power wide area network.

2. The method according to claim 1, characterized in that The multi-source collaborative analysis of the biological parameter dynamic sequence, the drug release rate curve and the infection risk quantification parameter is performed, wherein the transmission delay parameter of the low power wide area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework, including: According to the correspondence between the transmission delay parameter of the low power wide area network and the effective time window of the drug release rate curve, a delay compensation mechanism of the transmission delay parameter to the effective time window is established to generate a dynamic adjustment factor; Performing phase synchronization processing on the dynamic adjustment factor and the periodic change trend of the biological parameter dynamic sequence to generate a synchronized biological dynamic sequence, and coupling it with the dynamic adjustment factor to generate a biological dynamic coupling coefficient; According to the linear superposition result of the infection risk quantification parameter and the biodynamic coupling coefficient, a collaborative control variable is generated, and the collaborative control variable dynamically adjusts the release gradient of the drug release rate curve and the termination boundary of the effective time window through a feedback link. The infection risk quantification parameter corrects the abnormal correlation threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework; Nonlinearly constraining the synergistic control variable and the synchronized biodynamic sequence to generate a release frequency peak value for updating the drug release rate curve and a delay compensation threshold for the effective time window; The delay compensation threshold is iteratively matched with the real-time variation of the transmission delay parameter, and the compensation weight of the dynamic adjustment factor is updated to form a closed-loop dynamic adjustment link.

3. The method according to claim 2, characterized in that The step of subjecting the synergistic control variable to nonlinear constraints with the synchronized biodynamic sequence to generate a release frequency peak value for updating the drug release rate curve and a delay compensation threshold for the effective time window includes: Coupling and associating the time series change rate of the synergistic control variable with the dynamic fluctuation characteristics of the synchronized biological dynamic sequence to generate a set of dynamic coupling parameters; Performing multi-level parameter decomposition on the dynamic coupling parameter set, and dynamically weighting the multi-level parameter decomposition results according to the time slice division results of the synchronized biological dynamic sequence to generate a decomposition parameter set; Dividing the synchronized biological dynamic sequence into a time slice sequence, extracting a dynamic fluctuation feature vector of the time slice sequence, and performing a cross operation on the dynamic fluctuation feature vector and the decomposition parameter set to generate a modulation parameter set; Inputting the modulation parameter set into a constraint generator in a nonlinear constraint model, adjusting the weight distribution of the modulation parameter set through an iterative feedback mechanism, and generating an initial release frequency peak value and an initial delay compensation threshold; The initial release frequency peak and the initial delay compensation threshold are jointly optimized, and the phase offset of the initial release frequency peak and the window length of the initial delay compensation threshold are corrected by constraining the dynamic equilibrium condition of the synchronized biodynamic sequence and the boundary condition of the synergistic control variable to generate an optimized release frequency peak and delay compensation threshold parameter group; The optimized release frequency peak value and delay compensation threshold parameter group are mapped to the update process of the drug release rate curve to achieve the delay compensation threshold of the drug release rate curve in the effective time window.

4. The method according to claim 3, characterized in that The joint optimization of the initial release frequency peak value and the initial delay compensation threshold value, by constraining the dynamic equilibrium condition of the synchronized biodynamic sequence and the boundary condition of the synergistic control variable, correcting the phase offset of the initial release frequency peak value and the window length of the initial delay compensation threshold value, and generating an optimized release frequency peak value and delay compensation threshold parameter group, includes: By constraining the dynamic balance condition of the synchronized biological dynamic sequence, the fluctuation change rate of the synchronized biological dynamic sequence and the biological rhythm parameter are aligned in time domain to generate a dynamic balance constraint parameter; Establishing boundary condition constraint rules for the collaborative control variables, and generating a boundary range including a phase deviation and a response window threshold according to the transmission path length and diffusion delay characteristics of the collaborative control variables in the thermal response spray structure; Performing a joint optimization iteration of the initial release frequency peak and the initial delay compensation threshold, and establishing a joint optimization target of phase synchronization error and window coverage based on the dynamic balance constraint parameter and the boundary range; Based on the joint optimization objective, the phase offset of the initial release frequency peak is corrected, a phase correction factor is introduced according to the gradient descent direction of the phase synchronization error, and the time domain distribution of the initial release frequency peak is waveform-shaped to generate an optimized initial release frequency peak; The window length of the initial delay compensation threshold is adjusted, the time expansion amount is calculated based on the residual term of the window coverage, and the window length is nonlinearly compressed and compensated in combination with the drug diffusion rate feedback of the thermally responsive spray structure to generate an optimized delay compensation threshold parameter group.

5. The method according to claim 1, characterized in that The wound infection historical data set based on medical institutions adopts a federated learning framework to build a cross-institutional infection risk evolution model, and generates infection risk quantitative parameters associated with the biological parameter dynamic sequence and drug release rate curve, including: A local dynamic time series model is constructed based on the historical dataset of trauma infection in medical institutions, and local features related to drug release rate curves are combined to generate dynamic weight allocations that are passed to the federated aggregation module as intermediate parameters; The local dynamic time series model is integrated in the federated aggregation module, a global time series evolution relationship is constructed according to the dynamic weight distribution, and a global evolution matrix is ​​generated by adjusting the correlation mode between the dynamic sequence of the biological parameter and the drug release rate curve; The global evolution matrix is ​​transferred to a feature coupling module, and a feature coupling coefficient is generated based on the global evolution matrix. The feature coupling coefficient maps the drug release rate curve to the biological parameter dynamic sequence through multimodal feature fusion to generate a feature association chain, and the feature association chain is transferred to a dynamic optimization module as an intermediate parameter; In the dynamic optimization module, the phase offset of the drug release rate curve is adjusted according to the characteristic coupling coefficient to generate a dynamic constraint boundary, and the dynamic constraint boundary is transmitted to the risk quantification module as an intermediate parameter; A federated learning framework is used to construct a cross-institutional infection risk evolution model, and a closed-loop feedback mechanism is used to generate infection risk quantitative parameters associated with the dynamic sequence of biological parameters and the drug release rate curve.

6. The method according to claim 5, characterized in that The global evolution matrix is ​​transferred to a feature coupling module, a feature coupling coefficient is generated based on the global evolution matrix, and the feature coupling coefficient maps the drug release rate curve to the biological parameter dynamic sequence through multimodal feature fusion to generate a feature association chain, including: The global evolution matrix is ​​transferred to a characteristic coupling module, and a characteristic coupling coefficient is generated in the characteristic coupling module according to an internal structural relationship of the global evolution matrix; Performing phase shift adjustment on the drug release rate curve based on the characteristic coupling coefficient, wherein the adjusted drug release rate curve is synchronized with the biological parameter dynamic sequence in terms of time sequence, and a dynamic correlation time sequence is generated; The dynamic association time series is input into a feature association chain generation module, the drug release rate curve is mapped to the biological parameter dynamic sequence through multimodal feature fusion, and the feature coupling coefficient is iteratively optimized through a coupling feedback mechanism to generate a feature association chain with a hierarchical dependency.

7. The method according to claim 1, characterized in that The generating of a trauma infection warning instruction and an emergency data sharing instruction according to the multi-source collaborative analysis result, wherein the emergency data sharing instruction pushes clinical decision-making auxiliary information to a designated emergency unit via a low-power wide area network, includes: Based on the superposition relationship between the biodynamic coupling coefficient and the infection risk quantification parameter in the multi-source collaborative analysis results, the trauma infection risk level parameter is generated, and the priority assessment parameter is generated in combination with the load status parameter of the emergency unit; Generate a trauma infection warning instruction and an emergency data sharing instruction according to the multi-source collaborative analysis results, and construct an auxiliary information aggregation template based on the priority evaluation parameters and the historical response efficiency of the clinical decision-making auxiliary information; Dynamically matching the label mapping relationship between the trauma infection warning instruction and the auxiliary information aggregation template to generate a decision matching index, wherein the decision matching index adjusts the push priority sequence of the clinical decision auxiliary information through a low power wide area network; Dynamically calibrating the trauma infection risk level parameter according to the deviation range between the decision matching index and the multi-source collaborative analysis result to generate a calibrated warning trigger threshold; The calibrated early warning trigger threshold and the push priority sequence are used to push clinical decision-making assistance information to a designated emergency unit via the low-power wide area network.

8. A real-time sharing method for emergency clinical data based on the Internet of Things, characterized in that: include: The acquisition module collects the dynamic sequence of biological parameters of the wound site in real time through the smart dressing with integrated antibacterial coating, and transmits the dynamic sequence of biological parameters to the emergency clinical data collaboration platform through the low-power wide area network; A screening module, screening a drug release rate curve according to the actual pore size of the antibacterial coating of the smart dressing, wherein the pore size-drug sustained release mapping relationship in the antibacterial coating database defines a nonlinear attenuation characteristic of the associated drug sustained release curve when the pore size meets a preset condition; A construction module, based on the historical data set of trauma infection in medical institutions, uses a federated learning framework to build a cross-institutional infection risk evolution model to generate infection risk quantitative parameters associated with the dynamic sequence of biological parameters and the drug release rate curve; An analysis module performs multi-source collaborative analysis on the biological parameter dynamic sequence, the drug release rate curve and the infection risk quantification parameter, wherein the transmission delay parameter of the low power wide area network dynamically adjusts the effective time window of the drug release rate curve, and the infection risk quantification parameter corrects the abnormal correlation threshold of the pH value and temperature fluctuation through the model weight of the federated learning framework; A generation module generates trauma infection warning instructions and emergency data sharing instructions based on the multi-source collaborative analysis results. The emergency data sharing instructions push clinical decision-making auxiliary information to the designated emergency unit through a low-power wide area network.

9. A computing device, characterized in that It comprises 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 real-time sharing method of emergency clinical data based on the Internet of Things 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 real-time sharing method for emergency clinical data based on the Internet of Things as described in any one of claims 1 to 7 is implemented.

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