Intelligent temperature control system for high-capacity injection

Abstract

The invention belongs to the technical field of intelligent control, and discloses a high-capacity injection intelligent temperature control system. The system comprises a data acquisition module used for acquiring injection data; the data processing module is used for carrying out feature extraction on collected injection data to obtain injection features, and the feature modeling module is used for building a thermodynamic coupling equation of liquid medicine flow and taking the injection features as input of the thermodynamic coupling equation to obtain an optimal target temperature and an animal behavior mode in a future time period; the energy allocation module is used for dynamically allocating charging and discharging weights of the super capacitor and the lithium battery; the response temperature control module is used for performing behavior response type temperature control according to the animal behavior mode and a preset temperature control strategy; the limitation that traditional temperature control depends on an external power supply and static temperature control is broken through, and double challenges of unstable energy supply and dynamic thermal load matching are synchronously solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, more specifically, the present application relates to an intelligent temperature control system for large-volume injection. BACKGROUND

[0002] In the traditional direct injection scenario of large-volume injection, the temperature control of the drug solution has been in a "no intervention" or "extensive management" state for a long time. Generally, room temperature drug solution is directly used for infusion. However, there is a significant temperature difference between the drug solution and the animal body temperature, which may cause thermal shock reactions such as vasospasm and abnormal heart rate. Especially in large-volume rapid infusion, a large amount of low-temperature drug solution is input in a short time, which can cause a core body temperature drop of 2-3℃, inducing chills, metabolic disorders, and even hypothermia, prolonging the postoperative recovery time of animals, and increasing the incidence of complications. Therefore, it is urgent to upgrade from "passive infusion" to "active adjustment" through intelligent temperature control technology to ensure the safety, effectiveness, and physiological friendliness of large-volume injection in complex scenarios.

[0003] Chinese patent application No. CN118567419A discloses a vitamin injection production environment condition detection and adjustment system, which includes a sensor combination, a control system, and a communication interface. The sensor combination includes a first group of sensors for obtaining real-time temperature and humidity data of the production environment of the vitamin injection. The control system includes a first controller for receiving data from the first group of sensors, performing data analysis according to a preset temperature and humidity range, and generating adjustment instructions. The execution mechanism adjusts the temperature and humidity of the production environment according to the adjustment instructions. The communication interface is connected to the control system and communicates with external systems. The invention monitors the temperature and humidity of the production environment in real time through the sensor combination and automatically adjusts according to the preset parameter range to ensure that the production environment of the vitamin injection is always in the best state. This automatic monitoring and adjustment function reduces the need for manual intervention and improves production efficiency and stability.

[0004] Although the above method can meet most scenarios, research and practical application of the above method and existing technology have found that the above method and existing technology at least have the following defects:

[0005] In the animal health care scenario, the fluctuation of the infusion environment temperature causes frequent fluctuations in the dynamic heat load during drug infusion. This scenario generally lacks stable external power sources, such as no city power in the wild and limited power supply capacity in mobile scenarios, which makes it difficult for traditional temperature control systems to respond to heat load changes through continuous power supply and utilize environmental energy to achieve self-consistent power supply. The double challenges superimpose each other, causing a vicious cycle of temperature fluctuation, increased energy demand, power shortage, and temperature control failure.

[0006] In view of this, the present application proposes an intelligent temperature control system for large-volume injection to solve the above problems. SUMMARY

[0007] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purposes, the present application provides the following technical scheme: a large-capacity injection solution intelligent temperature control system, comprising:

[0008] A data acquisition module is configured to acquire injection data.

[0009] A data processing module is configured to extract features from the acquired injection data to obtain injection features.

[0010] A feature modeling module is configured to build a thermodynamic coupling equation of the drug solution flow, input the injection features into the thermodynamic coupling equation, and obtain optimal target temperatures and animal behavior patterns in future time periods.

[0011] An energy allocation module is configured to dynamically allocate charging and discharging weights of supercapacitors and lithium batteries according to biological energy dynamic features in the injection features.

[0012] A response temperature control module is configured to perform behavior-responsive temperature control according to the animal behavior patterns and a preset temperature control strategy, and in combination with the charging and discharging weights output by the energy allocation module.

[0013] Further, the injection data includes real-time temperature, biological energy data, behavior data, and drug solution data.

[0014] The method for obtaining real-time temperature includes:

[0015] A three-section composite pipeline is built, including a heating section, a buffer section, and a cooling section, a thermal insulation layer is arranged between the pipeline sections, the instantaneous temperature of the three-section composite pipeline is acquired in real time, the ambient temperature is acquired by a temperature sensor, and the instantaneous temperature and the ambient temperature are taken as the real-time temperature.

[0016] The method for obtaining biological energy data includes:

[0017] The voltage signal and current output by the piezoelectric film are acquired, the hot end temperature and cold end temperature acquired by the TEG module, and the current output by the TEG module are acquired, and the voltage signal and current output by the piezoelectric film, the hot end temperature and cold end temperature, and the current output by the TEG module are taken as the biological energy data.

[0018] The method for obtaining behavior data includes:

[0019] Three-dimensional acceleration data and heart rate data of the animal are acquired as the behavior data.

[0020] The method for obtaining drug solution data includes:

[0021] The flow rate of the drug solution is acquired by ultrasonic wave acquisition as the drug solution data.

[0022] Further, the injection features include bio-energy dynamic features, behavior coupling features, and heat flow interaction features;

[0023] The method for obtaining bio-energy dynamic features includes:

[0024] The bio-energy dynamic features include real-time power, real-time temperature difference, module output power, and temperature difference power generation fluctuation rate;

[0025] The real-time power is obtained according to the voltage signal and the current output by the piezoelectric film;

[0026] The real-time temperature difference is obtained according to the hot end temperature and the cold end temperature collected by the TEG module, the current output by the TEG module is obtained, and the module output power is obtained by combining the module constant and the real-time temperature difference;

[0027] The power signal output by the TEG module is monitored in real time, the power signal is subjected to FFT to obtain the main frequency component, the power standard deviation of the main frequency component is calculated, and the temperature difference power generation fluctuation rate is obtained.

[0028] Further, the method for obtaining the behavior coupling features includes:

[0029] Three-dimensional acceleration data of the animal is obtained, the data interval is divided and the occurrence probability of the three-dimensional acceleration data in each interval is counted, the Shannon entropy of the three-dimensional acceleration data is calculated according to the occurrence probability, and the activity entropy value is obtained;

[0030] Heart rate data of the animal is obtained, the standard deviation and the average heart rate of the heart rate data are analyzed, and the heart rate variation coefficient is calculated;

[0031] The temperature deviation is calculated, the covariance of the heart rate variation coefficient and the temperature deviation is calculated, and the stress response coefficient is obtained;

[0032] The method for obtaining the heat flow interaction features includes:

[0033] The heating section temperature change amount is measured, the heating power change rate is monitored, the ratio of the heating power change rate to the heating section temperature change amount is calculated, the dynamic thermal resistance is obtained, and the dynamic thermal resistance is taken as the transient thermal inertia;

[0034] The temperature of each point from the heating section to the venous end is obtained, the temperature decay rate is calculated, and the gradient dispersion index is obtained according to the temperature decay rate.

[0035] Further, the method for building the thermodynamic coupling equation includes:

[0036] The specific heat capacity of the liquid medicine is corrected by the transient thermal inertia, and the liquid medicine heat change amount is calculated based on the liquid medicine density, the corrected liquid medicine specific heat capacity, and the liquid medicine temperature change rate;

[0037] The product of the liquid medicine thermal conductivity coefficient and the diffusion term is calculated to obtain the conduction heat.

[0038] Obtain the temperature data of the measuring points arranged at intervals x in each dimension at time t, calculate the temperature change of adjacent measuring points and the ratio of adjacent measuring points, and obtain the temperature gradient in the current dimension;

[0039] Obtain the flow rate of the liquid medicine, calculate the product of the flow rate of the liquid medicine and the temperature gradient, and obtain the convective heat;

[0040] Measure the active temperature control device to obtain the heating power;

[0041] Combine the liquid medicine heat change, the conduction heat, the convective heat, the heating power and the disturbance heat to build a thermodynamic coupling equation of the liquid medicine flow.

[0042] Further, the method for obtaining the liquid medicine thermal conductivity coefficient comprises:

[0043] Place the liquid medicine between the preset hot plate and the preset cold plate, maintain the preset temperature gradient, measure the hot plate heating power, the hot plate area, the hot plate temperature, the cold plate temperature and the liquid medicine thickness, calculate the cold-hot temperature difference between the hot plate temperature and the cold plate temperature, calculate the product of the cold-hot temperature difference and the hot plate area, and then calculate the ratio of the product of the hot plate heating power and the liquid medicine thickness to the product of the cold-hot temperature difference and the hot plate area, to obtain the initial liquid medicine thermal conductivity coefficient, correct the initial liquid medicine thermal conductivity coefficient based on the thermoelectric power fluctuation rate, and obtain the liquid medicine thermal conductivity coefficient.

[0044] Further, the method for obtaining the diffusion term comprises:

[0045] Obtain the temperature data of the measuring points arranged at intervals x in each dimension at time t, calculate the difference between the cold-hot temperature difference of the adjacent position points of the previous interval and the cold-hot temperature difference of the adjacent position points of the next interval, obtain the temperature value, calculate the ratio of the temperature value to the square of the interval, obtain the second-order spatial derivative in the current dimension, accumulate the second-order spatial derivatives of different dimensions, obtain the initial diffusion term, correct the initial diffusion term based on the gradient dispersion index, and obtain the diffusion term.

[0046] The method for obtaining the disturbance heat comprises:

[0047] Obtain the stress response coefficient, the infusion tube heating power, the contact temperature difference between the surface temperature of the infusion tube and the animal body surface, and the contact area of the directly contacted part of the infusion tube and the animal body surface, calculate the product of the contact temperature difference and the contact area, calculate the ratio of the infusion tube heating power to the product of the contact temperature difference and the contact area obtained by calculation to obtain the convective heat transfer coefficient;

[0048] Obtain the animal vibration data, compare whether the animal vibration data exceeds the preset vibration threshold, and if it exceeds, mark the animal activity intensity coefficient as 1, otherwise as 0;

[0049] The difference between the animal body surface temperature and the liquid temperature is calculated, and the disturbance heat is calculated according to the product of the animal activity intensity coefficient, the convection heat transfer coefficient and the difference.

[0050] Further, the method for obtaining the optimal target temperature and the animal behavior pattern in the future period comprises:

[0051] The environmental temperature, the environmental humidity, the animal heart rate data and the historical temperature control data in the injection data are taken as the input of the load prediction model to obtain the optimal control amount and the animal behavior pattern in the preset period, and the control amount includes the heating power / cooling current; the optimal heating power is input into the thermodynamic coupling equation for correction;

[0052] The corrected thermodynamic coupling equation is solved to obtain the output temperature in the future period, and the rolling optimization objective function is constructed according to the output temperature, and the rolling optimization objective function is optimized in combination with the reinforcement strategy to obtain the optimal target temperature.

[0053] Further, the method for obtaining the optimal target temperature in combination with the reinforcement strategy to optimize the rolling optimization objective function comprises:

[0054] The objective function about the output temperature, the target temperature and the heating power / cooling current is built, and the rolling optimization objective function is obtained with the minimum objective function as the target; wherein the target temperature and the heating power / cooling current satisfy the preset constraint condition;

[0055] The state space is set, and the state vector is defined, including the liquid temperature, the temperature difference in the animal body surface and the composite pipeline, the environmental temperature, the behavior data and the historical control amount;

[0056] The action space is set, and the action is defined, including the adjustment range of the control amount;

[0057] The reward function is designed, the reward value is calculated, and the reward function is designed according to the rolling optimization objective function;

[0058] In each control period, the state in the future finite time domain is predicted by using the thermodynamic coupling equation, and the optimal control sequence is obtained by solving the rolling optimization objective function;

[0059] The objective function and the constraint condition are substituted into the preset optimizer to solve the optimal control sequence in the future time domain, only the first action in the optimal control sequence is executed, and the control amount is updated;

[0060] The current time state vector, the action, the reward value and the next time state vector are stored in the experience replay buffer;

[0061] Periodically sample data from the buffer, train the strategy network using an optimization algorithm, learn the control strategy that maximizes cumulative rewards, obtain the execution action according to the trained strategy network, input the execution action into the preset heating module / cooling module, and obtain the optimal target temperature.

[0062] Further, the method for dynamically allocating the charging and discharging weights of the super capacitor and the lithium battery comprises the following steps:

[0063] The real-time energy proportion of the real-time power to the module output power is calculated, the real-time energy proportion of the real-time power is combined with a charging response coefficient and a discharging response coefficient respectively, and the first charging weight and the first discharging weight are obtained by calculation respectively; the real-time energy proportion of the module output power is combined with a slow charging coefficient and a slow discharging coefficient respectively, and the second charging weight and the second discharging weight are obtained by calculation respectively, wherein each weight satisfies a preset weight constraint;

[0064] The charging and discharging of the super capacitor and the lithium battery are adjusted according to the first charging weight and the first discharging weight, and the second charging weight and the second discharging weight.

[0065] The technical effects and advantages of the intelligent temperature control system for large-volume injection solution of the present application are as follows:

[0066] The present application builds a coordinated control system of behavior, energy and heat flow by fusing biological energy collection, dynamic identification of animal behavior patterns, thermodynamic coupling modeling and intelligent energy management, establishes a self-power supply mechanism in the field without stable power supply by using the biological energy data collected by the piezoelectric film and TEG module, and breaks the bottleneck of insufficient power supply and limited temperature control energy; the temperature control strategy is driven by dynamic identification of animal behavior patterns, and the dynamic heat load demand caused by the temperature fluctuation of the infusion environment is accurately matched by combining the thermodynamic coupling equation and the rolling optimization; on this basis, the charging and discharging weights of the super capacitor and the lithium battery are dynamically allocated to realize the closed-loop adjustment of biological energy supply fluctuation-energy distribution response-temperature control power adaptation, which not only avoids the limitations of traditional temperature control relying on external power supply, but also breaks through the response lag of static temperature control to heat load changes, cuts off the vicious cycle chain of temperature fluctuation-increased energy demand-insufficient power supply-failed temperature control, and significantly improves the safety, energy efficiency ratio and environmental adaptability of large-volume injection solution infusion in animal health care scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 It is a schematic diagram of the intelligent temperature control system for large-volume injection solution of embodiment 1 of the present application;

[0068] Figure 2 It is a schematic diagram of the system data flow of the present application;

[0069] Figure 3 It is a schematic diagram of the data flow between part of the modules of the present application;

[0070] Figure 4 The intelligent temperature control system for the large-volume injection solution of Embodiment 2 is shown in the schematic diagram. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only 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 a person of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0072] Embodiment 1

[0073] Referring to FIGS. Figure 1 , Figure 2 The present embodiment provides an intelligent temperature control system for a large-volume injection solution, which comprises:

[0074] A data acquisition module is configured to acquire injection data, which includes real-time temperature, biological energy data, behavior data and drug solution data.

[0075] The method for obtaining real-time temperature comprises:

[0076] A three-section composite pipeline is built, which comprises a heating section, a buffer section and a cooling section. The heating section is configured to rapidly increase temperature, the buffer section is configured to stabilize temperature by using phase change materials, and the cooling section is configured to cool by using a semiconductor. A thermal insulation layer is arranged between the pipelines, which can be made of aerogel material. The instantaneous temperature of the three-section composite pipeline is acquired in real time by using a PT1000 array, and the ambient temperature is acquired by using a temperature sensor. The instantaneous temperature and the ambient temperature are taken as the real-time temperature.

[0077] By building the three-section composite pipeline comprising the heating section, the buffer section and the cooling section and arranging the thermal insulation layer, the instantaneous temperature of each section is acquired in real time by using the PT1000 array, and the ambient temperature is acquired by using the temperature sensor. This can provide high-precision and high-resolution real-time temperature distribution data for the intelligent temperature control of the large-volume injection solution. The temperature of the heating section is used to dynamically adjust the preheating power, the temperature of the buffer section is used to maintain the stability of the drug solution temperature, the temperature of the cooling section is used to cope with the end thermal disturbance, and the ambient temperature is used to correct the heat conduction model. These data are taken as the core input of the subsequent model, which can support the system to perceive the temperature gradient and distribution abnormalities in real time, dynamically adjust the heating power and the refrigeration current, and realize the accurate control of the drug solution temperature from preheating to the whole process of infusion. While ensuring the safety threshold of the animal vein, the energy consumption is optimized and the temperature uniformity is improved, so as to finally ensure the stable infusion of the large-volume injection solution at the target temperature.

[0078] The method for obtaining biological energy data comprises:

[0079] The voltage signal and current output by the piezoelectric film, the hot end temperature and cold end temperature collected by the TEG module, and the current output by the TEG module are obtained as bioenergy data.

[0080] The bioenergy data obtained by the piezoelectric film and the TEG module can provide self-consistent energy supply and dynamic management support for the intelligent temperature control system of the large-capacity injection liquid. The real-time monitored vibration energy data reflects the activity intensity of the animal, which is used to dynamically adjust the charge and discharge weight of the super capacitor and the lithium battery. The output power of the module is used as a stable energy support basis for temperature control requirements. At the same time, the bioenergy data is integrated into the subsequent model, so that the system can adaptively allocate energy according to the animal behavior pattern, maximize the use of renewable energy, reduce the dependence on external power supply, and improve the system reliability and energy efficiency ratio in extreme scenarios such as wild and mobile.

[0081] The method for obtaining behavior data includes:

[0082] The three-dimensional acceleration data and heart rate data of the animal collected by the three-dimensional acceleration sensor are used as behavior data.

[0083] The three-dimensional acceleration data and heart rate data of the animal collected by the three-dimensional acceleration sensor are used as behavior data, which can provide real-time analysis basis for the behavior mode of the intelligent temperature control system of the large-capacity injection liquid. The activity entropy value is generated by calculating the acceleration data through Shannon entropy, and the behavior randomness is quantified. The stress response level is evaluated through the coefficient of variation of the heart rate data. The two together drive the thermodynamic coupling equation to dynamically adjust the temperature control strategy, such as enabling the low-power cruise mode in a calm state, triggering instantaneous overclocking heating and starting the phase change material buffer when stressed or actively moving. The behavior data is also used to correct the thermal disturbance term in the thermodynamic coupling equation, and the thermal conductivity of the pipeline thermal insulation layer and the energy distribution weight are optimized in real time in combination with the environmental parameters, so as to reduce the risk of temperature overshoot caused by animal behavior and improve the environmental adaptability and biocompatibility of the temperature control system while ensuring that the liquid temperature is stable at a safe temperature threshold.

[0084] The method for obtaining liquid data includes:

[0085] The liquid flow rate is obtained as liquid data by ultrasonic wave acquisition.

[0086] The liquid flow rate data collected by ultrasonic waves can provide real-time quantitative basis for the heat convection characteristics of the intelligent temperature control system for large-volume injection solutions. The flow rate directly affects the residence time and heat exchange efficiency of the liquid in the heating section, buffer section, and cooling section. By substituting it into the convective heat of the thermodynamic coupling equation, the temperature field distribution prediction model can be dynamically corrected. For example, when the flow rate increases, the system automatically increases the heating power to compensate for the shortened heating time, or adjusts the refrigeration current to maintain the temperature stability of the cooling section. At the same time, the flow rate data is fused with behavior data and bio-energy data to support the real-time adjustment of the temperature control strategy by the multivariate optimization algorithm, avoiding temperature overshoot or lag caused by accelerated or decelerated liquid flow, and ensuring that the liquid temperature can accurately match the target temperature at different infusion rates.

[0087] The data processing module is used for feature extraction of the collected injection data to obtain injection features, including bio-energy dynamic features, behavior coupling features, and heat flow interaction features.

[0088] The method for obtaining bio-energy dynamic features includes:

[0089] The bio-energy dynamic features include real-time power, real-time temperature difference, module output power, and temperature difference power fluctuation rate.

[0090] The real-time power is calculated and obtained according to the voltage signal and current output by the piezoelectric film.

[0091] The real-time temperature difference is calculated and obtained according to the hot end temperature and cold end temperature collected by the TEG module, the current output by the TEG module is obtained, and the module output power is calculated and obtained by combining the module constant and the real-time temperature difference.

[0092] The power signal output by the TEG module is monitored in real time, the power signal is subjected to FFT to obtain the main frequency component, the power standard deviation of the main frequency component is calculated, and the temperature difference power fluctuation rate is obtained.

[0093] The method for obtaining bio-energy dynamic characteristics collects vibration energy and temperature difference power generation data in real time through piezoelectric film and TEG module, and generates real-time power, real-time temperature difference, module output power and temperature difference power generation fluctuation rate and other characteristics through calculation. The role of the method in intelligent temperature control of injection solution is to provide real-time bio-energy supply state data for the energy allocation module, and to realize self-consistent energy management in the scene without stable power supply by dynamically allocating the charging and discharging weight of super capacitor and lithium battery, such as adjusting the response speed of super capacitor according to real-time power, and optimizing the lithium battery slow charging strategy according to the temperature difference power generation fluctuation rate. At the same time, characteristics such as temperature difference power generation fluctuation rate can correct parameters such as drug liquid thermal conductivity in thermodynamic coupling equation, improve temperature prediction accuracy, and bio-energy data is associated with animal behavior patterns, such as vibration energy reflecting activity intensity, supporting the system to adjust the priority of temperature control strategy according to behavior dynamics, such as preferentially starting high-power discharge of super capacitor during intense activity. Ultimately, the energy dependence limitation of traditional temperature control is broken through, and the energy efficiency ratio and dynamic thermal load matching capability are simultaneously optimized.

[0094] The method for obtaining behavior coupling characteristics includes:

[0095] Obtain three-dimensional acceleration data of the animal, divide the data interval and count the occurrence probability of three-dimensional acceleration data in each interval, calculate the Shannon entropy of three-dimensional acceleration data according to the occurrence probability, and obtain the activity entropy value.

[0096] Obtain heart rate data of the animal, analyze the standard deviation and average heart rate of the heart rate data, and calculate the heart rate variation coefficient.

[0097] Calculate the temperature deviation, calculate the covariance of the heart rate variation coefficient and the temperature deviation, and obtain the stress response coefficient.

[0098] The behavior coupling characteristics can provide dynamic mapping basis for the behavior-temperature coupling relationship of the intelligent temperature control system of large-capacity injection solution. The activity entropy value directly drives the temperature control strategy classification, such as triggering the “dynamic response mode” when the activity entropy value is high, and increasing the heating power to 80%-120% of the nominal value to cope with the thermal disturbance caused by intense activity. The heart rate variation coefficient is used to identify the stress state of the animal and trigger the temperature compensation mechanism, such as increasing the temperature control accuracy when HRV>50ms. The stress response coefficient is used to guide the system to preferentially suppress temperature fluctuations that may exacerbate stress, such as reducing the risk of overcooling in the cooling section. The three types of characteristics are embedded in the thermodynamic coupling equation to realize real-time mapping from behavior mode to temperature control parameters. At the same time, the temperature of the drug liquid is stabilized at the safe threshold of the vein, the stress reaction of the animal caused by temperature discomfort is reduced, and the biocompatibility and intelligent adjustment ability of the temperature control system are improved.

[0099] The method for obtaining heat flow interaction characteristics includes:

[0100] The dynamic thermal resistance is obtained by measuring the heating section temperature change amount, monitoring the heating power change rate, and calculating the ratio of the heating power change rate to the heating section temperature change amount; the dynamic thermal resistance is taken as the transient thermal inertia;

[0101] The temperature attenuation rate is calculated according to the temperature attenuation rate, and the gradient dispersion index is obtained.

[0102] The heat flow interaction feature can provide a quantitative analysis tool for the heat flow transmission characteristics of the intelligent temperature control system of the large-capacity injection liquid, and the dynamic thermal resistance directly reflects the transient response capability of the system to the heating power adjustment, which is used to correct the heat conduction parameters in the thermodynamic coupling equation in real time, and improve the temperature field prediction accuracy; the gradient dispersion index quantifies the temperature uniformity of the drug liquid from the heating section to the infusion end, and supports the dynamic adjustment of the segmented heating power or the optimization of the distribution of the pipeline heat insulation layer; the two types of features are embedded in the heat flow anomaly detection algorithm, and when the dynamic thermal resistance suddenly changes or the gradient dispersion index exceeds the safety threshold, the local heating power / cooling adjustment or pipeline flow state optimization is automatically triggered to ensure that the temperature distribution of the whole process meets the safety requirements of the animal vein, and to reduce the risk of drug liquid thermal stability caused by uneven heat flow, such as protein denaturation.

[0103] The data flow between the feature modeling module, the energy allocation module and the response temperature control module can be referred to Figure 3 .

[0104] Feature modeling module: used for building a thermodynamic coupling equation of the drug liquid flow, taking the injection features as the input of the thermodynamic coupling equation, and obtaining the optimal target temperature and animal behavior mode in the future period; the method for building the thermodynamic coupling equation includes:

[0105] The drug liquid specific heat capacity is corrected by the transient thermal inertia, and the drug liquid heat change amount is calculated based on the drug liquid density, the corrected drug liquid specific heat capacity, and the drug liquid temperature change rate;

[0106] The drug liquid is placed between the preset hot plate and the preset cold plate, and a stable temperature gradient is maintained; the hot plate heating power, the hot plate area, the hot plate temperature, the cold plate temperature and the drug liquid thickness are measured; the cold-hot temperature difference between the hot plate temperature and the cold plate temperature is calculated; the product of the cold-hot temperature difference and the hot plate area is calculated; the ratio of the product of the hot plate heating power and the drug liquid thickness to the product of the cold-hot temperature difference and the hot plate area is calculated; the initial drug liquid thermal conductivity is obtained; the initial drug liquid thermal conductivity is corrected based on the thermoelectric power fluctuation rate, and the drug liquid thermal conductivity is obtained.

[0107] Obtain the temperature data of the measurement points arranged at intervals x in each dimension at time t, calculate the difference between the cold-hot temperature difference of the adjacent position points of the previous interval and the cold-hot temperature difference of the adjacent position points of the next interval, obtain the temperature value, calculate the ratio of the temperature value to the square of the interval, obtain the second-order spatial derivative in the direction of the current dimension, accumulate the second-order spatial derivatives of different dimensions to obtain the initial diffusion term, modify the initial diffusion term based on the gradient diffusion index to obtain the diffusion term;

[0108] Calculate the product of the liquid heat conduction coefficient and the diffusion term to obtain the conduction heat;

[0109] Obtain the temperature data of the measurement points arranged at intervals x in each dimension at time t, calculate the temperature change of the adjacent measurement points and the ratio of the adjacent measurement points, obtain the temperature gradient in the current dimension;

[0110] Obtain the liquid flow rate, calculate the product of the liquid flow rate and the temperature gradient to obtain the convective heat;

[0111] Measure the active temperature control device to obtain the heating power;

[0112] Obtain the stress response coefficient, the infusion tube heating power, the contact temperature difference between the infusion tube surface temperature and the animal body surface, and the contact area of the direct contact part of the infusion tube and the animal body surface, calculate the product of the contact temperature difference and the contact area, calculate the ratio of the infusion tube heating power to the product of the contact temperature difference and the contact area to obtain the convective heat transfer coefficient;

[0113] Obtain the animal vibration data, compare whether the animal vibration data exceeds the preset vibration threshold, and if it exceeds, mark the animal activity intensity coefficient as 1, otherwise as 0;

[0114] Calculate the difference between the animal body surface temperature and the liquid temperature, and calculate the disturbance heat according to the product of the animal activity intensity coefficient, the convective heat transfer coefficient and the difference;

[0115] Combine the liquid heat change, the conduction heat, the convective heat, the heating power and the disturbance heat to build a thermodynamic coupling equation for liquid flow.

[0116] By building a thermodynamic coupling equation, the transient thermal inertia, drug liquid thermal conductivity, diffusion term, convective heat, heating power and behavioral disturbance are integrated into a unified physical model, which can provide full-process thermal dynamic analysis and accurate control basis for intelligent temperature control of large-volume injection. Based on the transient thermal inertia, the specific heat capacity of the drug liquid is corrected, and the thermal conductivity and diffusion term of the drug liquid are calibrated by combining the thermoelectric power fluctuation rate and the gradient dispersion index, which can ensure that the model parameters adapt to the characteristics of the drug liquid (such as flow rate, thermal stability) and the environment (such as animal activity, temperature difference) in real time; The conduction heat, convective heat, heating power and disturbance heat are fused to accurately depict the heat transfer process of the drug liquid from the heating section to the vein end; The temperature field distribution and heat change output by the equation are directly used for the training of subsequent models, realizing dynamic adjustment of heating power / cooling parameters, such as adjusting power distribution according to convective heat transfer coefficient, suppressing abnormal heat disturbance, such as enhancing local temperature control when vibration exceeds the standard, ensuring that the temperature of the drug liquid is stable at the safe temperature threshold in complex scenarios, while optimizing energy consumption and reducing the risk of drug denaturation caused by uneven heat flow.

[0117] The method for obtaining the optimal target temperature and animal behavior pattern in the future period includes:

[0118] The environmental temperature, environmental humidity, animal heart rate data, and historical temperature control data in the injection data are used as the input of the load prediction model to obtain the optimal control amount and animal behavior pattern in the preset period, and the control amount includes heating power / cooling current; The optimal heating power is input into the thermodynamic coupling equation for correction;

[0119] The corrected thermodynamic coupling equation is solved to obtain the output temperature in the future period, and a rolling optimization objective function is constructed according to the output temperature, and the rolling optimization objective function is optimized by combining the reinforcement strategy to obtain the optimal target temperature.

[0120] The optimal target temperature in the future period can provide full-process prospective dynamic regulation and control capability for intelligent temperature control of large-volume injection. On the one hand, the load prediction model identifies the behavior pattern and environmental load in advance, drives the system to adjust the control amount in advance, and reduces the temperature response lag; On the other hand, based on the rolling optimization and reinforcement learning of the corrected thermodynamic coupling equation, the temperature prediction error is calibrated and the energy consumption distribution is optimized in real time, ensuring that the temperature of the drug liquid is stable at the safe temperature threshold when the environment mutates or the behavior switches, while the prospective strategy suppresses the thermal disturbance, improves the temperature control accuracy, drug thermal stability and system energy efficiency ratio in the scene of field first aid, mobile medical treatment, etc.

[0121] The method for obtaining the optimal target temperature by combining the reinforcement strategy to optimize the rolling optimization objective function includes:

[0122] A target function about output temperature, target temperature, heating power / cooling current is built, and a rolling optimization target function is obtained by minimizing the target function, wherein the target temperature, the heating power / cooling current all meet preset constraint conditions, such as the target temperature is not higher than an animal vein safety threshold, the heating power is not higher than a device rated power value, and the cooling current is not higher than a device current value;

[0123] A state space is set, and a state vector is defined, including a drug liquid temperature, a temperature difference in an animal body surface and a composite pipeline, an environment temperature, behavior data and a historical control amount, wherein the historical control amount is a heating power / cooling current at a previous moment;

[0124] An action space is set, and an action is defined, including an adjustment range of the control amount, such as an increase / decrease amplitude of the heating power or a change amount of the cooling current;

[0125] A reward function is designed, and a reward value is calculated, and the reward function is designed according to the rolling optimization target function;

[0126] In each control period, a state in a future finite time domain is predicted by using a thermodynamic coupling equation, and an optimal control sequence is obtained by solving the rolling optimization target function;

[0127] The target function and the constraint condition are substituted into a preset optimizer (such as a quadratic programming (QP) algorithm), an optimal control sequence in a future time domain is solved, only a first action in the optimal control sequence is executed, and a control amount is updated;

[0128] A current moment state vector, the action, the reward value and a next moment state vector are stored in an experience replay buffer;

[0129] Data is periodically sampled from the buffer, an optimization algorithm (such as a policy gradient or Q-learning) is used to train a policy network, a control policy maximizing cumulative reward is learned, an execution action is obtained according to the trained policy network, the execution action is input into a preset heating module / cooling module, and an optimal target temperature is obtained.

[0130] By building a target function containing output temperature, target temperature, heating power / cooling current, combining the definition of state space and action space, reward function design and rolling optimization algorithm, an adaptive and precise control framework under multiple constraints can be provided for intelligent temperature control of large-capacity injection solutions; the preset constraint conditions ensure that the temperature control process always meets the animal intravenous safety threshold and the rated parameters of the equipment, avoiding the risk of overheating and overloading; the state vector integrates the solution temperature, animal body and pipeline temperature difference, environmental parameters, behavior data and historical control amount, so that the system can capture the heat flow distribution, environmental disturbance and behavior mode changes in real time, providing multi-dimensional basis for dynamic decision-making; based on the thermodynamic coupling equation to predict the future state, the optimal control sequence is solved by the quadratic programming algorithm, combined with reinforcement learning to iteratively optimize the policy network from experience data, realizing the "prediction-optimization-execution" closed loop, for example, quickly increasing the heating power to the safety upper limit and suppressing temperature overshoot when the animal is stressed, while the reward function guides the system to balance temperature accuracy and energy consumption; only the first action of the optimal control sequence is executed in each control period and the experience replay buffer is updated, so that the system can continuously correct the control strategy based on the latest data to adapt to dynamic scenarios such as changes in solution flow rate and fluctuations in environmental temperature and humidity, ultimately achieving precise control of the solution temperature within the preset range while reducing energy consumption and improving the temperature control reliability and biocompatibility in complex scenarios such as field first aid and mobile medical care.

[0131] Energy allocation module: for dynamically allocating the charging and discharging weights of the supercapacitor and lithium battery according to the biological energy dynamic characteristics in the injection characteristics;

[0132] The method for dynamically allocating the charging and discharging weights of the supercapacitor and lithium battery comprises:

[0133] The real-time energy proportion of the real-time power and the module output power is calculated, and the real-time energy proportion of the real-time power is combined with a charging response coefficient and a discharging response coefficient, respectively, wherein the charging response coefficient and the discharging response coefficient are obtained by experiment fitting, and a first charging weight and a first discharging weight are calculated and obtained, respectively; the real-time energy proportion of the module output power is combined with a slow charging coefficient and a slow discharging coefficient, respectively, wherein the slow charging coefficient and the slow discharging coefficient are obtained by experiment fitting, and a second charging weight and a second discharging weight are calculated and obtained, respectively, wherein each weight satisfies a preset weight constraint, such as the sum of the first charging weight and the second charging weight being less than or equal to 1, and the sum of the first discharging weight and the second discharging weight being less than or equal to 1, to avoid overcharging and overdischarging;

[0134] The charging and discharging of the supercapacitor and lithium battery are adjusted according to the first charging weight and the first discharging weight, and the second charging weight and the second discharging weight.

[0135] The dynamic allocation of the charging and discharging weights of the super capacitor and the lithium battery can provide adaptive energy management support for intelligent temperature control of large-volume injection. The super capacitor preferentially absorbs high-frequency fluctuation energy of vibration energy through the first charging weight or the second discharging weight, and the lithium battery stably stores low-frequency energy of temperature difference power generation through the second charging weight or the second discharging weight, thereby improving the bioenergy utilization rate; the weight constraint avoids overcharging of the super capacitor or overdischarging of the lithium battery, and the combination of the slow charging coefficient and the slow discharging coefficient limits the charging and discharging rate, thereby prolonging the service life of the energy storage device; the weight allocation result directly affects the energy supply of the heating power and the refrigeration current, so that the system can maintain temperature control accuracy when the animal is stressed or the environmental temperature difference is stable, while reducing the dependence on external power supply, thereby ensuring the continuous temperature control capability and energy efficiency ratio in the field, mobile and other scenes.

[0136] The response control module is used for behavior responsive temperature control according to the animal behavior mode and the preset temperature control strategy, and in combination with the charging and discharging weights output by the energy allocation module;

[0137] In the process of behavior responsive temperature control according to the animal behavior mode and the preset temperature control strategy, according to the behavior mode of the animal, such as the calm mode, the intense activity and the stress mode, when in the calm mode, if the energy allocation module allocates the lithium battery high charging weight (such as the slow charging coefficient dominates) according to the temperature difference power generation fluctuation rate (low fluctuation), the temperature control strategy synchronously maintains the heating power at 30%-50% of the nominal value, preferentially utilizes the lithium battery to stably discharge power supply, and simultaneously stores the temperature difference power generation energy through the high lithium battery charging weight;

[0138] When in the intense activity, if the energy allocation module improves the super capacitor charging weight (such as the charging response coefficient triggers rapid absorption of vibration energy) based on the piezoelectric film real-time power (high value), the temperature control strategy adjusts the heating power to 80%-120% of the nominal value, and realizes instantaneous high-power output through the super capacitor high discharging weight (the discharging response coefficient takes effect), thereby matching the dynamic thermal disturbance demand, and simultaneously maintaining the super capacitor capacity by using vibration energy real-time charging;

[0139] When in the stress mode, if the energy allocation module removes the weight constraint (full power output mode) according to the bioenergy characteristics (such as mixed power supply of vibration energy and temperature difference power generation), the temperature control strategy enables the heating power of 150%-200% or forced refrigeration, supports the extreme temperature control demand through the sum of the super capacitor and the lithium battery weight being 1, and limits the lithium battery discharging depth through the slow discharging coefficient during the period, thereby avoiding overdischarge, forming a cooperative control mechanism of the behavior mode driven strategy type and the power boundary of the energy weight constraint.

[0140] The above method realizes precise coupling of behavior heat demand and biological energy supply by dynamically identifying animal behavior patterns, including calm mode, intense activity and stress mode, and matching the super capacitor and lithium battery charging and discharging strategy generated by the energy allocation module in real time, such as low-power energy supply of lithium battery and temperature difference power generation energy storage in calm mode, super capacitor priority response to vibration energy supply in intense activity, and full-power collaborative discharge in stress mode, which not only avoids the risk of energy supply interruption of traditional temperature control in the scene without power supply, but also optimizes the power output range of the temperature control device through weight constraint, such as limiting the upper limit of heating power and protecting the battery life, and synchronously improves the temperature control accuracy, system energy efficiency ratio and environmental adaptability in complex scenes, finally guarantees the safety and physiological friendliness of large-capacity injection infusion, and breaks through the dual technical bottlenecks of traditional static temperature control and dependence on external power supply.

[0141] Embodiment 2:

[0142] Referring to Figure 4 The embodiment provides an environment adaptive correction module applied to the embodiment 1, which is used for dynamically correcting system thermodynamic coupling equation parameters and control strategies based on environment parameters.

[0143] The method for dynamically correcting system thermodynamic coupling equation parameters and control strategies based on environment parameters comprises the following steps:

[0144] The wind speed is obtained, the convection coefficient when there is no wind is calibrated through a wind tunnel experiment, the nonlinear correction value of the wind speed on the convection coefficient of the pipe flow surface is calculated, the wind cooling correction factor is obtained, and the convection heat transfer coefficient is corrected based on the wind cooling correction factor.

[0145] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be limited by the protection scope of the claims.

[0146] Finally: the above is only a preferred embodiment of the present application and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. Large volume injection intelligent temperature control system, characterized by: include: Data acquisition module: used to collect injection data; Data processing module: used to extract features from the collected injection data and obtain injection features; Feature Modeling Module: This module is used to build a thermodynamic coupling equation for drug flow, using injection characteristics as input to the thermodynamic coupling equation to obtain the optimal target temperature and animal behavior pattern in the future period. Energy allocation module: used to dynamically allocate the charge and discharge weights of supercapacitors and lithium batteries according to the dynamic characteristics of bioenergy in the injection characteristics; Responsive temperature control module: used to perform behavior-responsive temperature control based on animal behavior patterns and preset temperature control strategies, combined with the charge and discharge weights output by the energy allocation module.

2. The intelligent temperature control system for large-volume injection according to claim 1, characterized in that: The injection data includes real-time temperature, bioenergy data, behavior data and drug solution data; Methods for obtaining real-time temperature include: Build a three-section composite pipeline, including a heating section, a buffer section, and a cooling section, and set a thermal isolation layer between the pipelines. Collect the instantaneous temperature of the three-section composite pipeline in real time, collect the ambient temperature through a temperature sensor, and use the instantaneous temperature and the ambient temperature as the real-time temperature; Methods for obtaining bioenergetic data include: Obtaining the voltage signal and current output by the piezoelectric film, the hot-end temperature and the cold-end temperature collected by the TEG module, and the current output by the TEG module, and using the voltage signal and current output by the piezoelectric film, the hot-end temperature and the cold-end temperature, and the current output by the TEG module as bioenergy data; Methods for obtaining behavioral data include: Collecting animal three-dimensional acceleration data and heart rate data as behavioral data; Methods for obtaining drug solution data include: Ultrasonic wave acquisition is used to obtain the liquid medicine flow rate as liquid medicine data.

3. The intelligent temperature control system for large-volume injection according to claim 2, characterized in that: The injection characteristics include bioenergy dynamic characteristics, behavioral coupling characteristics and thermal flow interaction characteristics; Methods for obtaining bioenergetic dynamic characteristics include: The dynamic characteristics of bioenergy include real-time power, real-time temperature difference, module output power and temperature difference power generation fluctuation rate; The real-time power is obtained by calculating the voltage signal and current output by the piezoelectric film; Based on the hot-end temperature and cold-end temperature collected by the TEG module, the real-time temperature difference is calculated to obtain the current output by the TEG module. The module output power is calculated by combining the module constant and the real-time temperature difference. Monitor the power signal output by the TEG module in real time, perform FFT on the power signal, obtain the main frequency component, calculate the power standard deviation of the main frequency component, and obtain the temperature difference power generation fluctuation rate.

4. The intelligent temperature control system for large-volume injection according to claim 3, characterized in that: The method for obtaining the behavioral coupling feature includes: Obtaining the animal's three-dimensional acceleration data, dividing the data into intervals and counting the occurrence probability of the three-dimensional acceleration data in each interval, calculating the Shannon entropy of the three-dimensional acceleration data based on the occurrence probability, and obtaining the activity entropy value; Obtain animal heart rate data, analyze the standard deviation and average heart rate of the heart rate data, and calculate the coefficient of variation of heart rate; Calculate the temperature deviation, calculate the covariance of the heart rate variability coefficient and the temperature deviation, and obtain the stress response coefficient; Methods for obtaining heat flow interaction features include: Measure the temperature change of the heating section, monitor the heating power change rate, calculate the ratio of the heating power change rate to the temperature change of the heating section, obtain the dynamic thermal resistance, and use the dynamic thermal resistance as the transient thermal inertia; The temperature of each point from the heating section to the venous end was obtained, the temperature decay rate was calculated, and the gradient diffusion index was obtained based on the temperature decay rate.

5. The intelligent temperature control system for large-volume injection according to claim 4, characterized in that: The method of constructing the thermodynamic coupling equation includes: The specific heat capacity of the liquid medicine is corrected by the transient thermal inertia, and the heat change of the liquid medicine is calculated based on the density of the liquid medicine, the corrected specific heat capacity of the liquid medicine, and the temperature change rate of the liquid medicine; Calculate the product of the thermal conductivity of the liquid and the diffusion term to obtain the conduction heat; Obtain the temperature data of the measurement points set at intervals x in each dimension of different spaces at time t, calculate the ratio of the temperature change of adjacent measurement points to the adjacent measurement points, and obtain the temperature gradient in the current dimension; Obtain the liquid flow rate, calculate the product of the liquid flow rate and the temperature gradient, and obtain the convection heat; Measure active temperature control equipment to obtain heating power; The thermodynamic coupling equation of the liquid medicine flow is constructed by combining the heat change of the liquid medicine, conduction heat, convection heat, heating power and disturbance heat.

6. The intelligent temperature control system for large-volume injection according to claim 5, characterized in that: The method for obtaining the thermal conductivity of the liquid medicine includes: The medicine liquid is placed between a preset hot plate and a preset cold plate, maintaining a preset temperature gradient, measuring the hot plate heating power, hot plate area, hot plate temperature, cold plate temperature and medicine liquid thickness, calculating the hot and cold temperature difference between the hot plate temperature and the cold plate temperature, calculating the product of the hot and cold temperature difference and the hot plate area, and then calculating the ratio of the product of the hot plate heating power and the medicine liquid thickness to the product of the hot and cold temperature difference and the hot plate area to obtain the initial medicine liquid thermal conductivity coefficient, and correcting the initial medicine liquid thermal conductivity coefficient based on the temperature difference power generation fluctuation rate to obtain the medicine liquid thermal conductivity coefficient.

7. The intelligent temperature control system for large-volume injection according to claim 5, characterized in that: The method for obtaining the diffusion term includes: Obtain the temperature data at time t for the measurement points set at intervals x in each dimension, calculate the difference between the hot and cold temperature difference of adjacent locations at the previous interval and the hot and cold temperature difference of adjacent locations at the next interval to obtain the temperature value, calculate the ratio of the temperature value to the square of the interval, obtain the second-order spatial derivative in the direction of the current dimension, accumulate the second-order spatial derivatives in different dimensions to obtain an initial diffusion term, and correct the initial diffusion term based on the gradient diffusion index to obtain a diffusion term; The method for obtaining the disturbance heat comprises: Obtaining the stress response coefficient, the heating power of the infusion tube, the contact temperature difference between the surface temperature of the infusion tube and the animal's body surface, and the contact area of ​​the portion of the infusion tube in direct contact with the animal's body surface, calculating the product of the contact temperature difference and the contact area, and calculating the ratio of the heating power of the infusion tube to the product of the calculated contact temperature difference and the contact area to obtain the convective heat transfer coefficient; Obtain animal vibration data and compare whether the animal vibration data exceeds a preset vibration threshold. If it exceeds, the animal activity intensity coefficient is marked as 1, otherwise it is marked as 0; The difference between the animal's body surface temperature and the liquid medicine temperature is calculated, and the disturbance heat is obtained by multiplying the animal's activity intensity coefficient, the convection heat transfer coefficient and the difference.

8. The intelligent temperature control system for large-volume injection according to claim 1, characterized in that: The method for obtaining the optimal target temperature and animal behavior pattern in the future period includes: The ambient temperature, humidity, animal heart rate data, and historical temperature control data from the injection data are used as inputs to the load forecasting model to obtain the optimal control variables and animal behavior patterns within a preset time period. The control variables include heating power and cooling current. The optimal heating power is input into the thermodynamic coupling equation for correction. The modified thermodynamic coupling equation is solved to obtain the output temperature in the future period. A rolling optimization objective function is constructed based on the output temperature. The rolling optimization objective function is optimized in combination with the enhancement strategy to obtain the optimal target temperature.

9. The intelligent temperature control system for large-volume injection according to claim 8, characterized in that: The method of optimizing the rolling optimization objective function in combination with the enhancement strategy to obtain the optimal target temperature includes: Build an objective function for output temperature, target temperature, heating power / cooling current, and obtain a rolling optimization objective function with the goal of minimizing the objective function; where the target temperature and heating power / cooling current all meet preset constraints; Set up the state space and define the state vector, including the liquid temperature, the temperature difference between the animal's body surface and the composite pipeline, the ambient temperature, the behavioral data, and the historical control quantity; Set the action space and define the action, including the adjustment range of the control amount; Design reward function, calculate reward value, and design reward function based on rolling optimization objective function; In each control cycle, the thermodynamic coupling equation is used to predict the state in the future finite time domain, and the rolling optimization objective function is solved to obtain the optimal control sequence; Substitute the objective function and constraints into the preset optimizer to solve the optimal control sequence in the future time domain, execute only the first action in the optimal control sequence, and update the control variable; Store the current state vector, action, reward value, and next state vector into the experience replay buffer; Data is regularly sampled from the buffer, and the policy network is trained using an optimization algorithm to learn a control strategy that maximizes the cumulative reward. Based on the trained policy network, an execution action is obtained. The execution action is input into the preset heating module / cooling module to obtain the optimal target temperature.

10. The intelligent temperature control system for large-volume injection according to claim 1, characterized in that: The method for dynamically allocating charge and discharge weights of supercapacitors and lithium batteries includes: Calculate the real-time energy ratio of the real-time power to the module output power, combine the real-time energy ratio of the real-time power with the charging response coefficient and the discharging response coefficient, and respectively calculate a first charging weight and a first discharging weight; combine the real-time energy ratio of the module output power with the slow charging coefficient and the slow discharging coefficient, and respectively calculate a second charging weight and a second discharging weight; The charging and discharging of the supercapacitor and the lithium battery are adjusted according to the first charging weight and the first discharging weight, the second charging weight and the second discharging weight.

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