An intelligent medical power supply management system for cascade utilization of power batteries

Abstract

The present application relates to the technical field of power supply management for medical equipment, and discloses an intelligent medical power supply management system for cascade utilization of power batteries, including: a power battery module, which is used to provide power output and monitor the operating status of the battery through charge state calculation, health state monitoring and output power calculation; a medical load module, each medical device has different power requirements and priorities; a data acquisition module, which is used to collect the operating data of the power battery module and the power requirements of the medical load module; an optimization control module, which is used to update the power supply strategy according to the data collected by the data acquisition module; and an intelligent control module, which updates the power supply strategy through a feedback mechanism. The present invention uses the power battery module, the medical load module, the data acquisition module, the optimization control module and the intelligent control module to work together. Compared with the problem that the existing single power supply scheme is difficult to adapt to complex load requirements, the efficiency and reliability of the cascade utilization battery are improved through intelligent optimization and dynamic control.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply management for medical equipment, and in particular to an intelligent medical power supply management system for cascade utilization of power batteries. Background Art

[0002] With the large-scale use of power batteries, especially in high-energy consumption fields such as electric vehicles, power batteries still retain a large residual capacity after retirement, which provides the possibility of cascade utilization. However, there are many challenges in using cascaded utilization batteries as power supply equipment in the medical field. Medical scenarios have extremely high requirements for the safety, stability and continuity of power supply, and existing technologies have certain shortcomings in these aspects.

[0003] First, the application of existing second-life batteries in medical power supply often lacks targeted design. Traditional battery management systems (BMS) mostly assume that the battery is in a brand new state, making it difficult to accurately assess the health status and actual output capacity of retired batteries. Battery aging causes capacity decay and performance fluctuations, which leads to inaccurate assessment of the battery's operating status in medical scenarios, which can easily lead to safety hazards such as power outages.

[0004] Secondly, medical equipment loads are complex and diverse, power demands vary greatly, and the priority demands of different devices vary significantly. However, existing power supply systems are usually based on uniform distribution and lack the ability to dynamically adjust priorities. When the power demand of high-priority equipment (such as life support systems) surges, existing technologies are difficult to respond quickly, resulting in unstable power supply to key equipment. At the same time, low-priority equipment may still occupy limited power resources, exacerbating the problem of inefficient resource allocation.

[0005] In addition, traditional medical power supply solutions have poor real-time adaptability to changes in load demand. Existing technologies mostly use fixed power allocation or simple threshold control methods, which makes it difficult to dynamically adjust the power supply strategy according to actual load demand and the remaining energy of the power battery. This method is prone to power supply delays or resource waste when faced with emergencies (such as a sudden increase in equipment load or a rapid decrease in battery capacity), affecting the stability and reliability of power supply.

[0006] Finally, the emergency response capability of the power supply system is still a weak link in the existing technology. In the case of limited power battery resources, the response to emergencies in the existing technology is usually lagging behind and lacks effective intelligent control strategies. When faced with complex changes in load demand, traditional systems are often unable to effectively distinguish between priority equipment and ordinary equipment, and cannot ensure the continuous power supply of key equipment, which can easily lead to medical risks. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides an intelligent medical power supply management system for the cascade utilization of power batteries, which solves the problems in the prior art of being unable to accurately evaluate the operating status of power batteries, lacking dynamic management of load priority, poor real-time adaptability to changes in load demand, and insufficient response in emergencies.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent medical power supply management system for cascade utilization of power batteries, comprising:

[0009] Power battery module, used to provide power output and monitor the battery's operating status through state of charge calculation, health status monitoring and output power calculation;

[0010] A medical load module, including multiple medical devices, each of which has different power requirements and priorities;

[0011] Data acquisition module, used to collect the operating data of the power battery module and the power demand of the medical load module;

[0012] An optimization control module, used to realize optimized power supply control of the medical load according to the data collected by the data collection module;

[0013] The intelligent control module is used to adjust the output of the power battery module according to the control strategy of optimizing the output of the control module and update the power supply strategy through the feedback mechanism.

[0014] Preferably, the power battery module comprises:

[0015] A state of charge calculation unit calculates the state of charge based on the effective capacity of the battery and the charge and discharge current, where the effective capacity is dynamically adjusted according to the battery health status;

[0016] The battery health status monitoring unit determines the health status by updating the ratio of the remaining battery capacity to the nominal capacity in real time;

[0017] The battery output power calculation unit calculates the real-time output power of the power battery based on the battery output voltage and current.

[0018] Preferably, the medical load module includes several medical devices, and the power demand of each medical device is determined by superposition of basic power and dynamic power demand. Each medical device is assigned a priority weight according to its importance, and the priority weight is dynamically adjusted according to the operating status of the device and the remaining energy of the power battery.

[0019] Preferably, the allocation of priority weights in the medical load module includes dynamically adjusted initial values:

[0020] The weight of high priority load is set to 1;

[0021] The weight of medium priority load is set to 0.7;

[0022] The weight of low priority loads is set to 0.3.

[0023] Preferably, the optimization control module realizes dynamic matching between the power battery output power and the power demand of the medical load by establishing an optimization objective function and operating constraints in combination with a dynamic optimization algorithm.

[0024] Preferably, the dynamic optimization algorithm comprises a gradient descent algorithm.

[0025] Preferably, the optimization objective function preferentially allocates power battery energy to high priority loads through dynamic mapping of load priority weights, medical load power requirements and power battery output power, and sets a penalty factor for the rate of change of the power supply to reduce power fluctuations.

[0026] Preferably, the operation constraints include the following:

[0027] The power battery output power does not exceed its maximum output power;

[0028] The power supply of medical equipment shall not be less than its minimum required power;

[0029] The remaining energy of the power battery is always greater than zero during the power supply process.

[0030] Preferably, the intelligent control module includes:

[0031] A real-time adjustment unit, used to adjust the power battery output power according to the output result of the optimization control module;

[0032] The status monitoring unit is used to monitor the operating status of the power battery and medical equipment, and feed back the monitoring results to the optimization control module;

[0033] The dynamic response unit is used to trigger the optimization control module to recalculate the power supply strategy when an emergency occurs during the power supply process.

[0034] Preferably, the intelligent control module dynamically adjusts the power supply strategy in an emergency situation, including:

[0035] When the power demand of high-priority devices surges, the power supply to low-priority devices is reduced;

[0036] When the remaining energy of the power battery is lower than a preset threshold, the power supply to non-critical equipment is limited.

[0037] The present invention provides an intelligent medical power supply management system for cascade utilization of power batteries. It has the following beneficial effects:

[0038] 1. The present invention achieves the technical effect of maximizing the residual value of power batteries and intelligent power supply distribution in medical scenarios through the coordinated operation of power battery modules, medical load modules, data acquisition modules, optimization control modules and intelligent control modules. Compared with the shortcomings of the prior art that a single power supply solution is difficult to adapt to the complex load requirements of medical equipment, the present invention solves the problems of low efficiency and poor reliability of second-use batteries in medical power supply through intelligent optimization and dynamic control.

[0039] 2. The present invention can dynamically monitor the operating status of the battery through the charge state calculation, battery health state monitoring and output power calculation unit in the power battery module, achieving the technical effect of accurately controlling the remaining capacity and output capacity of the power battery. Compared with the prior art solution that only judges the status by simple battery voltage changes, the present invention effectively overcomes the problems of inaccurate battery health state assessment and delayed charge state calculation, thereby improving the safety and stability of the system power supply.

[0040] 3. The present invention optimizes the dynamic optimization objective function and weight control strategy of the control module, prioritizes the power supply needs of high-priority devices, and introduces a penalty mechanism for the rate of change of power supply, thereby achieving the technical effect of reducing power fluctuations and improving system stability. Compared with the traditional power supply strategy in the prior art that does not consider dynamic priority adjustment, the present invention solves the problem of unstable power supply of high-priority devices caused by sudden load changes in medical scenarios.

[0041] 4. The present invention realizes rapid response to emergencies through the real-time adjustment unit, status monitoring unit and dynamic response unit in the intelligent control module, and achieves the technical effect of dynamically adjusting the power supply strategy and giving priority to the operation of key equipment. Compared with the technical solutions in the prior art that lack timely response to sudden load demands, the present invention significantly improves the power supply continuity of the system when the power demand of high-priority equipment surges or battery resources are insufficient, and effectively solves the problem that the medical power supply system is easily affected by external load fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In order to better understand the present invention, the above contents are described in detail below in conjunction with specific embodiments.

[0045] Please see attached Figure 1 An embodiment of the present invention provides an intelligent medical power supply management system for cascade utilization of power batteries, including: a power battery module, which is used to provide power output and monitor the operating status of the battery through charge state calculation, health state monitoring and output power calculation; a medical load module, which includes multiple medical devices, each of which has different power requirements and priorities; a data acquisition module, which is used to collect operating data of the power battery module and the power requirements of the medical load module; an optimization control module, which is used to achieve optimized power supply control of the medical load according to the data collected by the data acquisition module; and an intelligent control module, which is used to adjust the output of the power battery module according to the control strategy output by the optimization control module, and update the power supply strategy through a feedback mechanism.

[0046] In this embodiment, the power battery module can monitor the charge state, health state and output power of the power battery, and ensure its efficient and safe operation under the conditions of cascade utilization by accurately controlling the operating state of the power battery. At the same time, by dynamically adjusting the effective capacity and output parameters of the battery, the service life of the power battery can be maximized and its power supply performance can be improved.

[0047] Medical load module: This module implements dynamic load management of medical equipment by grading the importance of medical equipment and assigning corresponding priority weights. High-priority equipment (such as life support equipment) is given priority to obtain stable power supply to ensure its operational safety; at the same time, low-priority equipment can appropriately reduce power supply when resources are limited to optimize the overall power supply distribution.

[0048] Data acquisition module: real-time acquisition of the operating data of the power battery module (such as state of charge, battery health status, voltage and current, etc.) and the power requirements of the medical load module, fully reflecting the operating status of the system. The collected data can provide an accurate input basis for subsequent optimization control and ensure the efficiency and stability of the system operation.

[0049] Optimization control module: By constructing optimization objective functions and constraints, the output power of the power battery is dynamically adjusted to match the needs of medical loads based on real-time collected data. By giving priority to meeting the needs of high-priority medical equipment while suppressing power fluctuations, efficient use of power supply resources is achieved, the overall power supply strategy is optimized, and the stability and reliability of the system are improved.

[0050] Intelligent control module: It combines the power supply strategy generated by the optimization control module to adjust the output power of the power battery in real time to ensure the accuracy of power supply distribution. In addition, the intelligent control module has real-time monitoring and feedback capabilities. If an emergency occurs during the power supply process (such as a surge in power of high-priority equipment or insufficient power battery capacity), it can quickly adjust the power supply strategy to ensure the continuous operation of the system and prevent key equipment from shutting down due to insufficient power supply.

[0051] The power battery module includes:

[0052] The state of charge calculation unit calculates the state of charge through the effective capacity and charge and discharge current of the battery, where the effective capacity is dynamically adjusted according to the battery health status; the battery health status monitoring unit determines the health status by updating the ratio of the remaining battery capacity to the nominal capacity in real time; the battery output power calculation unit calculates the real-time output power of the power battery based on the battery output voltage and current.

[0053] In this embodiment, the power battery module includes a state of charge calculation unit for calculating the real-time state of charge (SOC) of the battery. The state of charge is the ratio of the remaining capacity of the battery to the total capacity, indicating the current charge level of the battery. In general, the SOC can be calculated by integrating the discharge current, and the specific formula is:

[0054]

[0055] Among them, SOC(t) is the state of charge at time t, and its value range is 0,1; SOC(t 0 ) is the state of charge at the initial moment; I(t) is the discharge current at time t, C eff is the effective capacity of the power battery; From the initial time t 0 The cumulative discharge amount of the battery up to the current time t, which represents the integral result of the current over time.

[0056] In one possible implementation, the effective capacity of the battery is dynamically adjusted according to the state of health. Specifically, the state of health (SOH) of the battery is used to characterize the aging degree of the battery and is defined as the ratio of the current maximum capacity of the battery to its nominal capacity. The formula is:

[0057]

[0058] Among them, C max is the current maximum capacity of the battery, C nom is the nominal capacity of the battery;

[0059] Generally, as the battery ages, the SOH gradually decreases, resulting in a decrease in effective capacity. By dynamically adjusting the effective capacity, the accuracy of the state of charge calculation can be improved.

[0060] In this embodiment, the power battery module also includes a battery health status monitoring unit for real-time monitoring of the battery's health status (SOH). Specifically, the health status monitoring unit dynamically updates the health status by comparing the ratio of the battery's remaining capacity to the nominal capacity and combining the battery's charge and discharge curve. As an option, the monitoring unit can establish a battery health prediction model by regularly measuring the battery's charge capacity change trend and combining historical data of parameters such as temperature and discharge rate.

[0061] In another implementation, the health status monitoring unit can also measure the internal resistance of the battery through an internal sensor, and estimate the SOH of the battery in combination with the temperature change characteristics. The internal resistance usually increases with battery aging, and by comparing the historical internal resistance data with the current internal resistance value, the health level of the battery can be indirectly determined.

[0062] In addition, in this embodiment, the power battery module further includes a battery output power calculation unit, which is used to calculate the output power of the power battery in real time according to the output voltage and current of the battery. Specifically, the output power formula of the battery is:

[0063] P output (t)=V(t)·I(t)

[0064] Among them, P output (t) is the output power of the battery at time t; V(t) is the output voltage of the battery at time t; I(t) is the output current of the battery at time t;

[0065] As an option, in order to improve the accuracy of output power calculation, the output power calculation unit can also monitor the fluctuation range of the output voltage in real time, and compensate for the transient changes of the output power in combination with the discharge dynamic characteristics. Specifically, the dynamic output voltage V(t) of the battery can be described by the following formula:

[0066]

[0067] Among them, V OC (SOC) is the open circuit voltage, which is nonlinearly related to the battery SOC; R int is the static internal resistance of the battery; R dyn is the dynamic internal resistance of the battery, describing the effect of the current change rate on the voltage; The output current change rate describes the rate of change of the battery output current over time. The larger the value, the faster the battery output current changes, which may cause dynamic voltage fluctuations.

[0068] In some embodiments, the output power calculation unit can combine historical power data and dynamic output power to predict the output power change trend in the short term in the future, and provide forward-looking data support for the optimization control module.

[0069] Through the above calculation and monitoring units, the power battery module can comprehensively manage the operating status of the battery and provide reliable data support for subsequent optimization control and intelligent adjustment. In actual operation, these units work together with the data acquisition module to achieve real-time status updates and accurate calculations, further ensuring the stability and efficiency of the system power supply.

[0070] The medical load module includes several medical devices. The power demand of each medical device is determined by the superposition of basic power and dynamic power demand. Each medical device is assigned a priority weight according to its importance, and the priority weight is dynamically adjusted according to the operating status of the device and the remaining energy of the power battery.

[0071] The allocation of priority weights in the medical load module includes dynamically adjusted initial values: the weight of high priority loads is set to 1; the weight of medium priority loads is set to 0.7; and the weight of low priority loads is set to 0.3.

[0072] In this embodiment, the medical load module includes several medical devices. The power demand of each medical device is determined by the superposition of its basic power demand and dynamic power demand. The basic power is the minimum power demand of the device in a stable operating state, and the dynamic power demand fluctuates with the change of the operating state of the device. For example, the basic power demand of life support equipment is always high, but in an emergency, its dynamic power demand may surge instantly. At this time, it is necessary to prioritize its power supply demand through the linkage of the load module and the optimization module. In general, the dynamic power demand can be determined by the following formula:

[0073] P demand,i (t) = P base,i +ΔP i (t)

[0074] Among them, P demand,i (t) is the total power demand of the ith device at time t; P base,i is the basic power requirement of the i-th device; ΔP i (t) is the dynamic power demand of the ith device at time t, which can be monitored and fed back in real time through the data acquisition module;

[0075] Specifically, the dynamic power demand ΔP i The change of (t) is directly related to the operating status of the equipment. For example, the monitoring equipment under normal conditions ΔP i (t) fluctuates less, while surgical equipment may rise rapidly when working at high load.

[0076] In one possible implementation, each medical device is assigned a priority weight according to its importance. The priority weight is used in the objective function of the optimization control module to affect the dynamic allocation of power supply resources. As an option, the initial priority weight allocation scheme is: the weight of high priority loads (such as life support equipment) is 1, the weight of medium priority loads (such as monitoring equipment) is 0.7, and the weight of low priority loads (such as recording equipment) is 0.3. The initial weight allocation scheme is preset based on the importance of the equipment, but it is not fixed, but can be dynamically adjusted according to the operating status of the equipment and the remaining energy of the power battery.

[0077] Specifically, the process of dynamic adjustment of priority weights can be described by the following formula:

[0078] w i (t) = w i,0 f(SOC,S i )

[0079] Among them, w i (t) is the dynamic priority weight of the u-th device at time t; w i,0 is the initial priority weight of the ith device; SOC is the real-time state of charge of the power battery; S i is the operating status of the ith device (e.g., workload, high frequency use, etc.); f(SOC, S i ) is a dynamic adjustment factor, which is a function of SOC and equipment operating status and is used to reflect the impact of power supply resource tension on the weight.

[0080] As an option, when SOC is lower than a preset threshold, f(SOC, S i ) can increase the weight of high-priority devices and reduce the weight of medium- and low-priority devices. For example, when the remaining energy of the power battery is insufficient, the low-priority device may be completely powered off to ensure the normal operation of the high-priority device.

[0081] The dynamic adjustment of priority weights is achieved through the linkage between the optimization control module and the intelligent control module. The optimization control module adjusts the power supply strategy according to the real-time weight, and the intelligent control module is responsible for the specific implementation to ensure the accurate distribution of power supply. Specifically, when the power demand of life support equipment surges, the system will automatically reduce the power allocation of the recording equipment and give priority to allocating limited power resources to key equipment. In other embodiments, the medical load module can also use a real-time feedback mechanism, combined with historical data and prediction models, to predict the trend of dynamic power demand. In this way, priority weights can be adjusted in advance to optimize the power supply distribution plan. For example, when the operating room equipment is about to start its schedule, its dynamic power demand will be increased in advance, and the optimization module will adjust the power supply plan in advance to avoid sudden power demand causing system fluctuations.

[0082] Therefore, the medical load module forms a close collaboration with the power battery module and the optimization control module through load demand modeling, priority weight allocation and dynamic adjustment mechanism, which can make full use of limited power battery resources and provide efficient and stable power supply support for medical scenarios. The design of this module can effectively cope with the challenges of load demand changes and resource constraints, and is particularly suitable for medical environments with high requirements for power supply continuity.

[0083] The optimization control module achieves dynamic matching between the power battery output power and the power demand of the medical load by establishing the optimization objective function and operating constraints and combining the dynamic optimization algorithm.

[0084] The optimization objective function prioritizes the allocation of power battery energy to high-priority loads through dynamic mapping of load priority weights, medical load power requirements, and power battery output power, and sets a penalty factor for the rate of change of power supply to reduce power fluctuations.

[0085] The operating constraints include the following: the output power of the power battery does not exceed its maximum output power; the power supply power of the medical equipment is not lower than its minimum required power; the remaining energy of the power battery is always greater than zero during the power supply process.

[0086] Dynamic optimization algorithms include gradient descent algorithms.

[0087] In this embodiment, the optimization control module is used to match the power output of the power battery with the power of the medical load. The matching is done by establishing the optimization objective function and the operating constraints, and then using the dynamic optimization algorithm to calculate the optimal power distribution plan. And by introducing mechanisms such as weight adjustment and change rate penalty, the overall operating efficiency of the system is improved, and the impact of power supply fluctuations on the normal operation of medical equipment is reduced.

[0088] In this embodiment, the optimization objective function dynamically adjusts the power supply strategy by comprehensively considering the load priority weight, medical load power demand and power battery output power, giving priority to high priority loads. Specifically, the optimization objective function can be defined as:

[0089]

[0090] Where N is the total number of medical devices; w i is the priority weight of the i-th device, which is dynamically adjusted by the medical load module; P demand,i (t) is the power requirement of the ith device at time t, P supply,i (t) is the power actually provided by the power battery to the i-th device at time t, and λ is a penalty factor used to constrain the rate of change of the power supply and reduce voltage fluctuations; is the rate of change of the power supply over time.

[0091] In a possible implementation, the first term of the optimization objective function is used to minimize the deviation between the supplied power and the required power, thereby meeting the load demand; the second term limits the variation range of the supplied power by introducing a penalty factor λ to avoid fluctuations caused by frequent adjustments.

[0092] As an option, the weight can be dynamically adjusted based on the remaining energy of the power battery and the operating status of the device. When the battery state of charge (SOC) drops to a certain threshold, the weight adjustment mechanism will prioritize the power supply of high-priority devices. For example, when the SOC drops below 30%, the weight of low-priority devices will be reduced to near zero, thereby allocating resources to high-priority devices.

[0093] In this embodiment, the optimization control module combines a set of operating constraints on the basis of realizing the objective function to ensure the safety and stability of system operation. Specifically, the operating constraints include the following:

[0094]

[0095] The above constraints are used to ensure that the power battery output power does not exceed its maximum output power P max ;

[0096] In addition, to ensure the basic power supply requirements of various medical devices, the operation constraints also include:

[0097] P supply,i (t)≥P min,i

[0098] Among them, P min,i is the minimum power requirement of the ith medical device.

[0099] To avoid excessive discharge of the battery, the operating constraints also include:

[0100] SOC(t)>SOC min

[0101] Among them, SOC(t) is the state of charge of the battery at time t; SOC min is the preset minimum safe state of charge threshold.

[0102] In one possible implementation, the optimization control module solves the optimal power supply strategy through a dynamic optimization algorithm, and the algorithm includes a gradient descent algorithm. Generally, the gradient descent algorithm gradually approaches the optimal solution of the objective function by iteratively adjusting the power supply. The specific steps are as follows:

[0103] First, initialize the power distribution according to the current system status;

[0104] Then, the gradient of the objective function is calculated, and the power supply is adjusted along the gradient direction;

[0105] Finally, the iterations are repeated until the value of the objective function converges.

[0106] In some embodiments, in order to improve the solution efficiency, the gradient descent algorithm can be combined with boundary constraints to ensure that the solution meets all operating constraints by correcting the direction and size of the gradient. For example, when the power allocation result is close to the maximum output power limit, the algorithm will automatically reduce the adjustment step size to avoid exceeding the limit range.

[0107] In another implementation, the optimization control module can also combine historical data and prediction models to identify the changing trend of load demand in advance. For example, by analyzing the rate of change of dynamic power demand, the power supply strategy can be adjusted in advance to optimize the system operation efficiency.

[0108] In summary, the optimization control module achieves efficient matching between power batteries and medical loads through the combination of objective functions, operating constraints and dynamic optimization algorithms, and provides a reliable power supply strategy for the medical power supply management system. This module works in coordination with the data acquisition module and the intelligent control module, and can effectively respond to changes in load demand and battery resource constraints, and improve the stability of medical equipment operation.

[0109] The intelligent control module includes:

[0110] A real-time adjustment unit, used to adjust the power battery output power according to the output result of the optimization control module;

[0111] The status monitoring unit is used to monitor the operating status of the power battery and medical equipment, and feed back the monitoring results to the optimization control module;

[0112] The dynamic response unit is used to trigger the optimization control module to recalculate the power supply strategy when an emergency occurs during the power supply process.

[0113] The intelligent control module dynamically adjusts the power supply strategy in emergency situations, including:

[0114] When the power demand of high-priority devices surges, the power supply to low-priority devices is reduced;

[0115] When the remaining energy of the power battery is lower than a preset threshold, the power supply to non-critical equipment is limited.

[0116] In this embodiment, the intelligent control module includes a real-time adjustment unit, which is used to dynamically adjust the output power of the power battery according to the output result of the optimization control module. Among them, the optimization control module generates a power supply strategy according to the load demand of the medical equipment and the power battery status, and the real-time adjustment unit converts this power supply strategy into specific power supply information. For example, when the output of the optimization control module is "reduce the power supply of low-priority equipment", the real-time adjustment unit adjusts the output parameters of the power battery by controlling the power electronic equipment to tilt resources toward high-priority equipment.

[0117] Specifically, the real-time adjustment unit can adjust the output power of the power battery at a millisecond response speed to adapt to the fluctuating load requirements of the medical equipment. Assume that the actual load requirement of the medical equipment is:

[0118]

[0119] Among them, P load (t) is the total power demand of the system at time t; P supply,i (t) is the power provided to the i-th device at time t.

[0120] The real-time adjustment unit will dynamically adjust the P of each device according to the optimization strategy. supply,i (t), ensuring that the total power demand meets the optimal allocation result.

[0121] In this embodiment, the intelligent control module also includes a status monitoring unit for real-time monitoring of the operating status of the power battery and the actual power demand of the medical device. In general, the operating status of the power battery includes parameters such as the state of charge (SOC), the state of health (SOH), and the output power, which are provided by the power battery module; the actual power demand of the medical device is provided by the medical load module. The status monitoring unit aggregates these data and feeds them back to the optimization control module for real-time updating of the power supply strategy. For example, when it is detected that the power battery SOC drops below a safety threshold (such as 20%), the status monitoring unit will immediately notify the optimization control module to take protective power supply measures.

[0122] In a possible implementation, the state monitoring unit can also perform trend analysis on the monitoring data. For example, by monitoring the rate of change of the power demand of the medical device, it can predict the sudden load demand that may occur in the future. This prediction result can serve as an important basis for optimizing the control module adjustment strategy.

[0123] The intelligent control module also includes a dynamic response unit, which is used to quickly trigger the optimization control module to recalculate the power supply strategy when an emergency occurs during the power supply process. Specifically, the emergency may include a surge in power demand for high-priority equipment and a rapid decrease in the remaining energy of the power battery. In this case, the dynamic response unit readjusts the power supply strategy to give priority to meeting the needs of key equipment. For example:

[0124] When the power demand of high-priority devices surges, the dynamic response unit will allocate resources to high-priority devices by reducing the power supply of low-priority devices. At this time, the priority weights in the optimization objective function will be dynamically adjusted.

[0125] When the remaining energy of the power battery is lower than the safety threshold (such as SOC is lower than 15%), the dynamic response unit will extend the operating time of high-priority devices by limiting the power supply of non-critical devices. For example, the restriction condition can be expressed as:

[0126] P supply,i (t) = 0 (when the device priority is low)

[0127] By limiting the power output of low-priority devices, more resources can be provided to high-priority devices.

[0128] In some embodiments, the dynamic response unit can also combine historical operation data to optimize the response mechanism. For example, when the load of the medical device changes frequently, the dynamic response unit can preset different emergency response schemes to reduce the response time and improve the efficiency of power supply strategy adjustment.

[0129] As an option, the intelligent control module can also optimize the power supply strategy through multiple iterations to ensure the overall stability of the system in emergency situations. For example, when the power of high-priority equipment surges and the power battery SOC decreases at the same time, the dynamic response unit will adjust the strategy multiple times to minimize the performance loss of other equipment while ensuring the power supply of high-priority equipment.

[0130] In summary, the intelligent control module ensures the efficient and stable operation of the system under dynamic load demand and limited resource conditions through the three functions of real-time adjustment, status monitoring and dynamic response. The design of this module can fully cope with the challenges of load changes and insufficient battery resources in medical scenarios, and work together with the optimization control module to provide important guarantees for the reliability and safety of the system.

[0131] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent medical power supply management system for cascade utilization of power batteries, characterized in that: include: Power battery module, used to provide power output and monitor the battery's operating status through state of charge calculation, health status monitoring and output power calculation; A medical load module, including multiple medical devices, each of which has different power requirements and priorities; Data acquisition module, used to collect the operating data of the power battery module and the power demand of the medical load module; An optimization control module, used to realize optimized power supply control of the medical load according to the data collected by the data collection module; The intelligent control module is used to adjust the output of the power battery module according to the control strategy of optimizing the output of the control module and update the power supply strategy through the feedback mechanism.

2. According to claim 1, the intelligent medical power supply management system for power battery recycling is characterized in that: The power battery module comprises: A state of charge calculation unit calculates the state of charge based on the effective capacity of the battery and the charge and discharge current, where the effective capacity is dynamically adjusted according to the battery health status; The battery health status monitoring unit determines the health status by updating the ratio of the remaining battery capacity to the nominal capacity in real time; The battery output power calculation unit calculates the real-time output power of the power battery based on the battery output voltage and current.

3. According to claim 1, the intelligent medical power supply management system for power battery recycling is characterized in that: The medical load module includes several medical devices. The power demand of each medical device is determined by superimposing the basic power and the dynamic power demand. Each medical device is assigned a priority weight according to its importance, and the priority weight is dynamically adjusted according to the operating status of the device and the remaining energy of the power battery.

4. According to claim 3, the intelligent medical power supply management system for power battery recycling is characterized in that: The allocation of priority weights in the medical load module includes dynamically adjusted initial values: The weight of high priority load is set to 1; The weight of medium priority load is set to 0.7; The weight of low priority loads is set to 0.

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5. According to claim 1, the intelligent medical power supply management system for power battery recycling is characterized in that: The optimization control module realizes dynamic matching between the power battery output power and the power demand of the medical load by establishing the optimization objective function and the operation constraint conditions and combining the dynamic optimization algorithm.

6. According to claim 5, the intelligent medical power supply management system for power battery recycling is characterized in that: The dynamic optimization algorithm includes a gradient descent algorithm.

7. The intelligent medical power supply management system for power battery recycling according to claim 5 is characterized in that: The optimization objective function preferentially allocates power battery energy to high-priority loads through dynamic mapping of load priority weights, medical load power requirements and power battery output power, and sets a penalty factor for the rate of change of the power supply to reduce power fluctuations.

8. The intelligent medical power supply management system for power battery recycling according to claim 7 is characterized in that: The operating constraints include the following: The power battery output power does not exceed its maximum output power; The power supply of medical equipment shall not be less than its minimum required power; The remaining energy of the power battery is always greater than zero during the power supply process.

9. The intelligent medical power supply management system for power battery recycling according to claim 1, characterized in that: The intelligent control module comprises: A real-time adjustment unit, used to adjust the power battery output power according to the output result of the optimization control module; The status monitoring unit is used to monitor the operating status of the power battery and medical equipment, and feed back the monitoring results to the optimization control module; The dynamic response unit is used to trigger the optimization control module to recalculate the power supply strategy when an emergency occurs during the power supply process.

10. The intelligent medical power supply management system for power battery recycling according to claim 9, characterized in that: The intelligent control module dynamically adjusts the power supply strategy in an emergency situation, including: When the power demand of high-priority devices surges, the power supply to low-priority devices is reduced; When the remaining energy of the power battery is lower than a preset threshold, the power supply to non-critical equipment is limited.

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