Charging self-adaptive control and adjustment method for intelligent medical cabin

Abstract

The invention discloses a self-adaptive charging control and adjustment method for an intelligent medical cabin, and relates to the technical field of charging adjustment. Through an energy management system of the intelligent medical cabin, firstly, the operation states of medical equipment in the cabin are monitored and classified, and operation equipment and non-operation equipment are distinguished; constructing an operation sequence and an important operation sequence according to the equipment weight; calculating the maximum charging and discharging power of the battery pack by combining the external charging power and the real-time charging power, the charge state and the health state of the battery pack, and determining a battery power interval; according to the total demand load power and the battery power interval, the output power of the intelligent medical cabin battery pack and the charging power of the external power supply are preliminarily regulated and controlled, the guarantee effect is verified by comparing the non-interruptible load power of the current control period with the non-interruptible load power of the next control period, and a notification is generated to inform an operator of the guarantee state. And stable power supply of important medical equipment is ensured.

Description

A method for adaptive charging control of a smart medical cabin Technical Field

[0001] This invention belongs to the field of charging regulation technology, specifically, it relates to a charging adaptive control and regulation method for an intelligent medical cabin. Background Technology

[0002] As an advanced facility integrating multiple medical devices, the intelligent medical cabin has gradually become an important part of the medical industry due to its portability.

[0003] Existing technologies for energy management and charging control in intelligent medical cabins typically employ relatively crude or static strategies, treating all medical equipment within the cabin as a single load for power supply, or simply switching between charging and discharging based on the total battery capacity. This lack of refined perception and dynamic coordination regarding equipment operating status, mission criticality, and real-time battery health reveals significant shortcomings when facing complex and ever-changing medical scenarios: They cannot intelligently identify which devices are operating and which are idle, nor can they differentiate the importance levels of medical equipment. Therefore, when external power supply fluctuates or battery capacity is limited, there is insufficient power supply guarantee for critical medical equipment such as those maintaining vital signs, posing a risk of interruption. Furthermore, existing battery management technologies are based on fixed nominal parameters, failing to fully consider the aging health of the battery pack and the dynamic limitations of real-time state of charge on the actual chargeable and dischargeable power range. This easily leads to overcharging, over-discharging, or overly optimistic or conservative assessments of actual available power, affecting battery life and power supply reliability. Consequently, operators often only respond passively after power problems occur, hindering early intervention and ensuring the stable execution of medical tasks.

[0004] To address the aforementioned problems, this invention proposes a charging adaptive control and adjustment method for an intelligent medical cabin. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a charging adaptive control and adjustment method for intelligent medical cabins, solving the problems of unreliable power supply and unintelligent energy distribution for critical medical equipment in intelligent medical cabins.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A method for adaptive charging control and adjustment of an intelligent medical cabin, the method comprising:

[0008] Step 1: Determine the operational status of the intelligent medical cabin, extract all medical equipment within the intelligent medical cabin, and separate the operational medical equipment from the non-operational medical equipment;

[0009] Based on a predefined list of equipment weights, a screening operation is performed on the operating medical equipment to construct a sequence of operating medical equipment and a sequence of important operating medical equipment.

[0010] Determine the sequence of critical operating medical equipment and the uninterrupted load power and total required load power associated with the sequence of operating medical equipment and time.

[0011] Step 2: Obtain the maximum external rechargeable power and the charging power and state of charge of the battery pack in the smart medical cabin at the corresponding time. Combine the real-time battery health status of the battery pack to calculate the maximum charging power and maximum discharging power, and determine the battery power range of the battery pack at the corresponding time.

[0012] Step 3: Based on the total demand load power and battery power range, adjust the output power of the smart medical cabin's battery pack and the charging power of the external power source in the next control cycle.

[0013] The protection effect is verified based on the uninterrupted load power in the current control cycle and the uninterrupted load power in the next control cycle. The protection effect status is determined, and a protection effect status notification is generated to notify the operators.

[0014] As a further aspect of the present invention, the specific method for constructing and running the medical device sequence in step one is as follows:

[0015] Determine the instantaneous power of all medical devices within the smart medical cabin based on the energy management system integrated into the smart medical cabin at the current moment;

[0016] Medical devices with zero instantaneous power are marked as non-operating medical devices;

[0017] Extract the device identifiers of all medical devices with non-zero instantaneous power and compare them with the device identifiers of medical devices in the current medical task schedule table, which includes the device identifiers of activated medical devices and the scheduled working time of the medical task.

[0018] Extract all medical devices that pass the comparison and include them in the set of operating medical devices; mark the remaining medical devices as non-operating medical devices.

[0019] Verify the total number of medical devices in the medical equipment set and the medical task plan;

[0020] If the total number of medical devices does not match, an audible and visual alarm will be triggered to alert the operator.

[0021] Conversely, continuous monitoring is necessary.

[0022] As a further aspect of the present invention, the specific method for constructing the sequence of operating medical devices and the sequence of important operating medical devices in step one is as follows:

[0023] Extract the set of operating medical devices, and determine the device weight of all medical devices in the set of operating medical devices based on the predefined device weight list table and medical task plan table. The device weight list table contains the medical device name corresponding to the device weight of all medical devices in the smart medical cabin.

[0024] The sequence of all medical devices in the set of running medical devices is sorted from largest to smallest according to the device weight value and denoted as E1, E2, ..., Ej, where j is the total number of medical devices in the set of running medical devices;

[0025] Retrieve medical devices in the sequence of running medical devices E1, E2, ..., Ej whose device weights are greater than a preset important device weight threshold, and sort them in descending order of device weight value as important running medical device sequences E1, E2, ..., Ek, where k represents the total number of medical devices whose device weights are greater than the preset important device weight threshold, and k ≤ j.

[0026] As a further aspect of the present invention, the specific method for determining the sequence of important operating medical equipment and the uninterrupted load power and total required load power associated with the sequence of operating medical equipment and time is as follows:

[0027] Determine the current time, denoted as t1;

[0028] Extract the load power of any medical device Ei in the sequence of running medical devices E1, E2, ..., Ej for m moments within a control cycle starting from the current time t1. Record the load power sequence of medical device Ei in chronological order. Here, the control cycle is a preset time period, m is the total number of moments within the control cycle, and i is the counting index, with a value range from 1 to j.

[0029] Determine the weighting weights for each of the m load powers, where the sum of the m weighting weights is 1 and all are greater than 0, and they increase sequentially in chronological order based on a preset linear ratio.

[0030] The load power sequence is weighted and summed, and denoted as the reference load power P_Ei of the medical device Ei in this control period;

[0031] Similarly, determine the reference load power of each medical device in the sequence of operating medical devices E1, E2, ..., Ej, to form the reference load power sequence P_E1, P_E2, ..., P_Ej;

[0032] Sum the baseline load power sequence and denote it as the total demand load power P_all;

[0033] Extract the first k reference load powers from the reference load power sequence and sum them up, denoted as the uninterruptible load power P_uni.

[0034] As a further aspect of the present invention, the specific method for determining the battery power range corresponding to the battery pack and the time in step two is as follows:

[0035] Obtain the maximum rechargeable power GM provided by the external power source;

[0036] And determine the charging power of the battery pack in the smart medical cabin at each moment in the control cycle at the current time t1, and construct the charging power sequence P_cd1, P_cd2, ..., P_cdm in chronological order;

[0037] The energy management system integrated into the intelligent medical cabin acquires the battery pack's state of charge sequence SOC1, SOC2, ..., SOCm and battery health state SOH during the control cycle. The battery health state SOH is denoted as 100% when it is in an ideal health state and 0% when it is at its lowest.

[0038] Calculate the current maximum charging power P_cd and maximum discharging power P_fd of the battery pack based on the battery health status (SOH):

[0039] P_cd=-[GM×(1-(100%-SOH) / 100%)], where the maximum charging power P_cd is a negative value, indicating charging;

[0040] P_fd=|P_cd×(1-(100%-SOH) / 100%)|, where the maximum discharge power P_fd is a positive value, indicating discharge;

[0041] Based on the charge state sequence SOC1, SOC2, ..., SOCm, the charge state SOCn at any time n within the control period is determined using the following method:

[0042] ADJ = (SOC_tar - SOCn) / SOC_range;

[0043] Determine the state of charge adjustment factor ADJ, SOC_tar is the preset target state of charge, SOC_range is the state of charge range, which is determined based on the battery pack type and is considered a known value, and n is the counting index, which ranges from 1 to m.

[0044] The battery power range corresponding to the battery pack and time is determined based on the state of charge adjustment coefficient ADJ, the maximum charging power P_cd, and the maximum discharging power P_fd, denoted as: P_acc=[P_min,P_max].

[0045] As a further aspect of the present invention, the specific method for determining the battery power range corresponding to the battery pack and the time in step two further includes:

[0046] When the state of charge (SOCn) is less than the target state of charge (SOC_tar), the maximum charging power P_min associated with the battery pack at time n is calculated using P_min=min(P_cd,P_cd×(1+ADJ)).

[0047] The maximum discharge power P_max of the battery associated with the battery pack at time n is calculated using P_max=min(P_fd,P_fd×(1-ADJ)).

[0048] When the state of charge (SOCn) is greater than the target state of charge (SOC_tar), the maximum charging power P_min associated with the battery pack at time n is calculated using P_min=max(P_cd,P_cd×(1+ADJ)).

[0049] The maximum discharge power P_max of the battery associated with the battery pack at time n is calculated using P_max=max(P_fd,P_fd×(1-ADJ)).

[0050] The maximum charging power P_min and the maximum discharging power P_max of the battery pack are used to construct the battery power range P_acc=[P_min,P_max] at time n.

[0051] As a further aspect of the present invention, in step three, the specific method for adjusting the output power of the intelligent medical cabin's battery pack and the charging power of the external power source within the next control cycle based on the total demand load power and the battery power range is as follows:

[0052] Extract the total demand load power P_all and compare it with the battery power range P_acc;

[0053] If P_all is greater than 0 and P_all belongs to [P_min, P_max], then the current state of charge of the battery pack is compared with the preset minimum state of charge safety threshold and the maximum state of charge safety threshold.

[0054] If the state of charge is between the minimum state of charge safety threshold and the maximum state of charge safety threshold, set the battery pack output power P_sc=P_all and the external power supply charging power P_sr=0 in the next control cycle.

[0055] If the state of charge is less than the minimum state of charge safety threshold, set the battery pack output power P_sc=P_min in the next control cycle, and the external power supply charging power P_sr=P_all+P_min.

[0056] If the state of charge is greater than the highest state of charge safety threshold, set the battery pack output power P_sc=P_all and the external power supply charging power P_sr=0 in the next control cycle.

[0057] Maintain the above settings and continue monitoring until the current medical task schedule is completed.

[0058] As a further aspect of the present invention, in step three, determining the protection effect status and generating a protection effect status notification, and notifying the operator, the specific method is as follows:

[0059] Real-time acquisition of the battery pack's output power P_sc and the external power supply's charging power P_sr;

[0060] At the start of the next control cycle, obtain the actual uninterruptible load power P_uni_real;

[0061] Compare the uninterruptible load power P_uni_real with the uninterruptible load power P_uni;

[0062] If P_uni_real≥P_uni, the power supply guarantee status of the important medical equipment sequence is determined to be successful;

[0063] If P_uni_real < P_uni, the power supply guarantee status of the critical operating medical equipment sequence is determined to be failed, an emergency alarm is triggered, the prepared backup emergency power supply is started to supply power to the medical equipment in the critical operating medical equipment sequence, a guarantee status notification associated with the power supply guarantee failure is generated, and the operators are notified to carry out maintenance.

[0064] The beneficial effects of this invention are:

[0065] (1) This invention intelligently identifies and screens key medical devices in operation and associates them with their uninterrupted load power in real time, thereby achieving precise matching and priority protection of power resources under the dynamic changes of external charging power and battery status; thus, it can adaptively adjust charging and discharging strategies based on the importance of the equipment, the health status of the battery and the real-time power demand, which not only ensures the continuous and stable operation of the core medical functions, but also effectively protects the battery life through power range regulation; at the same time, by verifying the protection effect and notifying the status, it enhances the overall reliability and the timeliness of human supervision, and improves the safety of the intelligent medical cabin in complex power environments;

[0066] (2) This invention achieves precise management and priority guarantee of energy in the medical cabin by dynamically constructing the sequence of operating equipment and weighting the sorting mechanism; it can identify and distinguish key medical equipment in real time, ensuring the absolute priority of high-weight equipment such as life support equipment in energy allocation, thereby significantly improving medical safety and reliability; at the same time, it automatically verifies the operating status of equipment through power monitoring and plan comparison, and alarms are triggered in time once an abnormality is detected, effectively preventing medical risks caused by equipment failure or malfunction; in addition, the load power determination method corresponding to the time sequence provides timely data support for energy scheduling, making power allocation more scientific and reasonable.

[0067] (3) This invention intelligently determines the battery power range by dynamically integrating the battery health status and real-time state of charge, thereby improving the accuracy and adaptability of energy management in the intelligent medical cabin; it calculates the maximum charge and discharge power based on the battery health status, effectively preventing overcharging or over-discharging of the battery, thus delaying battery aging and extending its service life; at the same time, it dynamically adjusts the power range through the state of charge adjustment coefficient to ensure that the battery status always approaches the target value, optimizes the charge and discharge strategy and improves energy utilization efficiency; in addition, it acquires external power and battery data in real time, constructs an adaptive power sequence, flexibly responds to changing conditions, enhances the adaptability of the intelligent medical cabin operation, and ensures continuous power supply safety.

[0068] (4) This invention achieves efficient energy distribution by dynamically comparing the total demand load with the battery power range and intelligently regulating the battery output and external power charging power, thus ensuring the continuity and reliability of power supply for important medical equipment; it ensures that the battery operates within a safe range through real-time monitoring and adjustment; and it immediately triggers the backup power supply and alarm mechanism to notify the operators to carry out maintenance once a power supply failure is detected, thereby enhancing the emergency response capability and operational stability of the medical cabin. Attached Figure Description

[0069] The invention will now be further described with reference to the accompanying drawings.

[0070] Figure 1 is a flowchart illustrating the method described in this invention;

[0071] Figure 2 is a flowchart illustrating the method described in Embodiment 2 of the present invention;

[0072] Figure 3 is a flowchart illustrating the method described in Embodiment 3 of the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Example 1

[0075] A charging adaptive control adjustment method for an intelligent medical cabin, as shown in Figure 1, includes the following:

[0076] This method is a charging adaptive control and adjustment method for an intelligent medical cabin. It provides an intelligent and dynamic energy management method, which aims to ensure that the critical medical equipment in the intelligent medical cabin never stops operating under stable power supply, mobile status, or unstable power supply environments such as in the field, while maximizing energy utilization efficiency and protecting battery health.

[0077] First, determine the operational status of the intelligent medical cabin, extract all medical equipment within the cabin, and separate operational and non-operational medical equipment. Then, based on a predefined equipment weight list, perform a filtering operation on the operational medical equipment to construct a sequence of operational medical equipment and a sequence of important operational medical equipment. Finally, determine the sequence of important operational medical equipment and the uninterrupted load power and total demand load power associated with each sequence of operational medical equipment at any given time. Specifically:

[0078] The intelligent medical cabin integrates an energy management system that uses pre-deployed sensors to monitor the load power of all medical devices in the cabin in real time and determine the running, standby, and off status of all medical devices in the cabin, thereby identifying running and non-running medical devices.

[0079] The equipment weight list is stored in a relational database pre-built by the operator. The equipment weight list stores the equipment weights of medical equipment corresponding to various medical task plan tables, including all medical equipment in the smart medical cabin.

[0080] The medical task plan table stores the equipment identifier of the medical devices and the planned working time of the medical tasks.

[0081] For example: Given a medical task plan, specifically for vital sign monitoring, the equipment weight list stores the medical devices associated with this medical task plan and their respective equipment weights as follows: ECG monitor, equipment weight 0.5; electronic thermometer, equipment weight 0.2; pulse oximeter, equipment weight 0.2; ventilator, equipment weight 0.1. Among them, the ECG monitor has the highest equipment weight of 0.5, indicating that it is the most important. The sum of the equipment weights of all medical devices in a medical task plan is always 1.

[0082] By determining the device weights of all medical devices in a medical task schedule and performing a separation operation based on the device weights, it is possible to identify the sequence of operating medical devices and the sequence of important operating medical devices.

[0083] Finally, based on the real-time load power of all medical devices in the sequence of critical operating medical devices, the uninterruptible load power is determined, and then based on the real-time load power of all medical devices in the sequence of operating medical devices, the total required load power is determined.

[0084] Next, the maximum rechargeable power supplied by the external power source and the charging power and state of charge of the battery pack in the smart medical cabin at the given time are obtained. The charging power of the battery pack at the given time represents a charging power preset by the operator and changes in real time under the influence of the current external power supply operation, while the state of charge represents the current charge of the battery pack.

[0085] Then, by combining the real-time battery health status of the battery pack, the maximum charging power and maximum discharging power are calculated to determine the battery power range corresponding to the time. The battery health status can be directly obtained through the energy management system integrated into the smart medical cabin. This is existing technology and will not be elaborated on in the solution. For details, please refer to the battery health status determination method of electric vehicle or smartphone battery packs.

[0086] Finally, based on the total required load power and battery power range, the output power of the smart medical cabin's battery pack and the charging power of the external power supply are adjusted in the next control cycle. The protection effect is verified based on the uninterrupted load power in the current control cycle and the uninterrupted load power in the next control cycle. The protection effect status is determined, and a protection effect status notification is generated to inform the operator. It should be noted that the operations described in this solution, which involve performing medical tasks based on the smart medical cabin, are all based on a medical task plan. That is, during the medical tasks in the current medical task plan, the number of medical devices activated is constant. Therefore, by initially determining the uninterrupted load power and the total load power, the required charging power of the smart medical cabin and the discharge power of the battery pack inside the smart medical cabin can be determined.

[0087] Example 2

[0088] This embodiment, based on embodiment 1, further discloses a method for determining the sequence of important operating medical devices and the uninterrupted load power and total required load power associated with the sequence of operating medical devices and time, as shown in Figure 2, specifically including the following:

[0089] First, as described in Example 1, it is necessary to first determine the sequence of operating medical devices, and then further differentiate the important sequences of operating medical devices, as follows:

[0090] First, when the intelligent medical cabin begins to execute the medical task plan predefined by the operator (medical personnel), the first moment after the intelligent medical cabin enters normal operation is determined and recorded as the current moment;

[0091] At the current moment, the instantaneous power of all medical devices in the intelligent medical cabin is determined through the energy management system integrated into the intelligent medical cabin. If the instantaneous power of a certain medical device is 0, it is marked as a non-operating medical device.

[0092] Next, the device identifiers of all medical devices with instantaneous power not equal to 0 are extracted. The device identifiers are bound to the medical devices themselves and are generally taken as the unique serial numbers of the medical devices (the unique serial numbers are determined when the medical devices leave the factory).

[0093] The system retrieves the device identifiers of all medical devices from the medical task plan table and compares them sequentially with the device identifiers of all medical devices whose instantaneous power is not zero. It then filters out all medical devices with instantaneous power not zero that pass the verification comparison (i.e., the device identifiers match) and adds them to a pre-built set of running medical devices. The remaining medical devices that fail the verification comparison are marked as non-running medical devices. The verification comparison can detect abnormal operation, such as unauthorized medical devices or medical devices that are missed in operation.

[0094] Next, extract the total number of medical devices in the centralized medical equipment operation and compare it with the total number of medical devices in the medical task plan table;

[0095] If the total number of medical devices is the same, it indicates that the smart medical cabin is operating normally and is under continuous monitoring.

[0096] If the total number of medical devices is not the same, it indicates that the intelligent medical cabin is malfunctioning and will issue an audible and visual alarm to remind the operator.

[0097] Next, the determined set of operating medical devices is further processed to construct the sequence of operating medical devices and the sequence of important operating medical devices, as follows:

[0098] First, the device identifiers of all medical devices in the medical task plan are determined, and the device weights of all medical devices in the medical device set are determined based on the device weight list. It should be noted that the device weight list includes the medical device names corresponding to the device weights of all medical devices in the smart medical cabin.

[0099] Then, sort all medical devices in the set of operating medical devices according to their weight values ​​from largest to smallest to obtain the sequence of operating medical devices, represented as: E1, E2, ..., Ej, where j represents the total number of medical devices in the set of operating medical devices.

[0100] The medical device sequence E1, E2, ..., Ej is sorted in descending order of device weight. Therefore, the higher the device weight, the more important the medical device, and the earlier it is in the sequence. For example, medical device E1 is more important than medical device E2.

[0101] Then, the operator obtains the weight threshold of important equipment preset by the actual situation, which is used to classify medical equipment into general medical equipment and important medical equipment. Important medical equipment generally includes medical equipment such as ventilators and defibrillators that are involved in life support or critical diagnosis and treatment, so power supply must be guaranteed, and therefore they are classified as important medical equipment.

[0102] Next, the device weights of all medical devices in the running medical device sequence E1, E2, ..., Ej are compared with the important device weight threshold, and medical devices with device weights greater than the preset important device weight threshold are selected and marked as important medical devices. The total number of important medical devices is counted and denoted as k, k≤j. In fact, the k important medical devices refer to the first k medical devices in the running medical device sequence E1, E2, ..., Ej.

[0103] Extract the first k medical devices from the sequence of operating medical devices E1, E2, ..., Ej to obtain the sequence of important operating medical devices E1, E2, ..., Ek.

[0104] Next, based on the identified critical operating medical equipment sequence and the operating medical equipment sequence, the uninterrupted load power and total required load power associated with each of these two sequences at time are further determined, as follows:

[0105] Get the current time and record it as t1;

[0106] Obtain the control cycle pre-built by the operator and determine the total number of moments within a control cycle, denoted as m. It should be noted that the control cycle is essentially a time period.

[0107] Then, any one medical device Ei is extracted from the sequence of running medical devices E1, E2, ..., Ej, and the current time t1 is taken as the start time of a control cycle. The load power corresponding to medical device Ei at each time in this control cycle is determined.

[0108] Finally, m load powers can be determined. The m load powers are sorted in chronological order to obtain the load power sequence associated with medical device Ei, where i is the counting index, with a value range from 1 to j.

[0109] Next, obtain the weighted weights preset by the operator for the power of m loads, for a total of m weighted weights. The m weighted weights increase sequentially according to the linear ratio preset by the operator. The sum of the m weighted weights is 1, and all of them are greater than 0.

[0110] The more recent the time, the larger the weighting value of the corresponding load power, indicating that the recent load power has a greater impact on the current decision. By increasing the weighting value, the importance of recent data is highlighted.

[0111] By combining the weighted values ​​of each of the m load powers, a weighted summation is performed on the m load powers to calculate the baseline load power of the medical device Ei in this control cycle, denoted as P_Ei.

[0112] By repeating the above steps, the same operation is performed on each medical device in the sequence of operating medical devices E1, E2, ..., Ej to obtain the reference load power associated with each medical device. The reference load power is then sorted according to the order of the sequence of operating medical devices E1, E2, ..., Ej and denoted as the reference load power sequence P_E1, P_E2, ..., P_Ej.

[0113] Next, the baseline load power sequence P_E1, P_E2, ..., P_Ej is summed, and the calculated result is denoted as the total demand load power associated with the operating medical equipment sequence E1, E2, ..., Ej, and recorded as P_all.

[0114] By summing the first k reference load powers in the reference load power sequence according to this method, the uninterruptible load power associated with the sequence of important operating medical equipment E1, E2, ..., Ek is obtained, denoted as P_uni.

[0115] Example 3

[0116] This embodiment further discloses a method for battery pack and battery power range corresponding to time, based on embodiment 2, as shown in Figure 3, specifically including the following:

[0117] First, obtain the maximum rechargeable power that the intelligent medical cabin can be supplied by an external power source during use, denoted as GM. Different external power sources can supply different maximum rechargeable power GM, and this value can be directly monitored and obtained by the energy management system.

[0118] Then, determine the charging power of the battery pack in the smart medical cabin at each moment in the control cycle at the current time t1. This charging power is the actual adaptive charging power of the smart medical cabin by the energy management system. There are a total of m charging powers. Sort the m charging powers in chronological order and denote them as the charging power sequence P_cd1, P_cd2, ..., P_cdm.

[0119] Then, based on the energy management system integrated in the smart medical cabin, the state of charge of the battery pack is acquired in real time during the control cycle and recorded as the state of charge sequence SOC1, SOC2, ..., SOCm in chronological order;

[0120] Simultaneously, the battery health status of the battery pack is determined and denoted as SOH. It should be noted that the battery health status SOH is recorded as 100% when it is in an ideal health state, and the minimum is 0%. When the battery health status of the battery pack drops below the battery health status threshold preset by the operator, it indicates that the battery pack needs to be replaced or repaired. By introducing the battery health status to analyze the charging power of the battery pack, the subsequent control strategy can adapt to battery aging, avoid adjusting old batteries to requirements beyond their physical limits, and effectively extend the life of the battery pack.

[0121] Then, the maximum charging power P_cd associated with the current battery pack under the current battery health state SOH and the maximum rechargeable power GM that the external power supply can provide is calculated by using P_cd=-[GM×(1-(100%-SOH) / 100%)].

[0122] The maximum discharge power P_fd is calculated using P_fd=|P_cd×(1-(100%-SOH) / 100%)|, where the maximum charging power P_cd is negative, indicating charging, and the maximum discharge power P_fd is positive, indicating discharging.

[0123] It should be explained that the upper limit of the absolute value of the maximum charging power P_cd is jointly determined by the battery health status SOH and the maximum rechargeable power GM that the external power supply can provide. The lower the battery health status SOH, the lower the upper limit of the charging power will be automatically reduced for aging batteries to prevent overcharging damage. The negative sign indicates the direction of the charging power input to the battery pack.

[0124] The maximum discharge power P_fd is linked to the maximum charging power P_cd and is also constrained by the battery health state (SOH). This ensures that the discharge power also decreases as the battery pack ages, thus protecting the battery. In this way, dynamic power limit protection based on the battery health state is achieved, ensuring the safe operation of the battery pack.

[0125] Extract the state of charge (SOC) at any time n in the sequence SOC1, SOC2, ..., SOCm. Based on the SOC, perform example processing and calculate the state of charge adjustment coefficient ADJ associated with the battery pack at time n using ADJ=(SOC_tar-SOCn) / SOC_range. Here, n is the counting index, ranging from 1 to m, and SOC_tar is the target state of charge preset by the operator. For example, the target state of charge of the battery pack is set to 65% to achieve the technical effect of balancing battery life and backup capacity.

[0126] (SOC_tar-SOCn) represents the deviation between the current state of charge of the battery pack and the target state of charge. The larger the deviation, the more urgent the need for adjustment.

[0127] SOC_range represents the state of charge range, which is determined based on the battery pack type and is considered a known value, such as a usable range from 20% to 90%. Normalization is performed based on the SOC_range to make the state of charge adjustment factor ADJ a dimensionless proportionality coefficient. When ADJ is less than 0, it indicates a high current state of charge, meaning it can be discharged and a higher discharge power is allowed. When ADJ is greater than 0, it indicates a low current state of charge, requiring charging, and the discharge power is reduced accordingly. When ADJ equals 0, it means the deviation between the current state of charge and the target state of charge is 0, and the current state remains unchanged.

[0128] Then, by combining the state-of-charge adjustment factor ADJ, the maximum charging power P_cb, and the maximum discharging power P_fb, the battery power range corresponding to the battery pack and the time is determined, specifically as follows:

[0129] When the state of charge (SOCn) is less than the target SOC_tar, the maximum charging power P_min associated with the battery pack at time n is calculated using P_min=min(P_cd,P_cd×(1+ADJ)). Here, P_cd×(1+ADJ) is a dynamic adjustment term. When the SOCn is low, ADJ is a positive number, i.e. (1+ADJ)>1. At this time, P_cd×(1+ADJ) will become a negative number with a larger absolute value. The max function will select the smaller of P_cd and P_cd×(1+ADJ), but it will not exceed the maximum charging power P_cd. Therefore, when the state of charge of the battery pack is insufficient, a larger charging power will be selected.

[0130] When the state of charge (SOCn) is greater than the target SOC_tar, the maximum charging power P_min associated with the battery pack at time n is calculated using P_min=max(P_cd,P_cd×(1+ADJ)). When the SOCn is high, ADJ is negative and (1+ADJ)<1. The dynamic adjustment term P_cd×(1-ADJ) will become a negative number with a small absolute value. Therefore, the min function selects P_cd×(1-ADJ), which means that the charging power is reduced when the SOCn is high.

[0131] When the state of charge (SOCn) is less than the target state of charge (SOC_tar), the maximum discharge power P_max associated with the battery pack at time n is calculated using P_max=min(P_fd,P_fd×(1-ADJ)). When the state of charge (SOCn) is low, ADJ is positive and (1-ADJ) < 1. The dynamic adjustment term P_fd×(1-ADJ) becomes smaller, and the min function selects P_fd×(1-ADJ) to reduce the discharge power.

[0132] When the state of charge (SOCn) is greater than the target state of charge (SOC_tar), P_max = max(P_fd, P_fd × (1-ADJ)) calculates the maximum discharge power P_max associated with the battery pack at time n. When the state of charge (SOCn) is high, ADJ is negative and (1-ADJ) > 1. The dynamic adjustment term P_fd × (1-ADJ) increases, and the max function selects P_fd × (1-ADJ) to increase the discharge power, which does not exceed P_fd.

[0133] Finally, the maximum discharge power P_min and the maximum charging power P_max of the battery are calculated together, and the battery power range P_acc=[P_min,P_max] at time n is constructed. Therefore, the battery power range P_acc=[P_min,P_max] is not a fixed range, but a dynamic range.

[0134] Example 4

[0135] This embodiment, based on embodiment 3, further discloses a method for regulating the output power of the battery pack of the intelligent medical cabin and the charging power of the external power supply in the next control cycle, and determining the state of the protection effect, specifically including the following:

[0136] Based on the results obtained in the above embodiments, this embodiment regulates the output power of the battery pack and the charging power of the external power supply in the next control cycle, and further determines the protection effect status. The ultimate goal is to optimize the use of the battery pack and the external power grid while meeting the power needs of all medical equipment, and to ensure the safety and reliability of power supply.

[0137] First, obtain the total required load power P_all and the battery power range P_acc, and then verify them;

[0138] If P_all is greater than 0 and belongs to [P_min, P_max];

[0139] The current state of charge (SOC) of the battery pack is obtained and compared with the minimum and maximum SOC safety thresholds preset by the operator. If the current SOC is between the two, the battery pack output power P_sc = P_all is set for the next control cycle, and the external power supply charging power P_sr = 0 is set simultaneously, indicating that no charging is required. The battery pack can independently handle the total demand load power, reducing the impact of simultaneous charging and discharging on the battery pack's durability.

[0140] If the charge level is less than the minimum safe state of charge threshold, it means that the battery pack's current state of charge is low and needs to be charged. Therefore, the battery pack output power P_sc=P_min is adjusted in the next control cycle to indicate that charging is being performed, and the charging power of the external power supply P_sr=P_min+P_all is adjusted so that the external power supply can simultaneously handle the charging power of the battery pack and the total load power required by the medical equipment.

[0141] If the current state of charge is greater than the maximum safe threshold, it means that the battery pack is currently in a high state of charge. Therefore, it is necessary to adjust the output power of the battery pack P_sc=P_all in the next control cycle and set the charging power of the external power supply P_sr=0, which means that the battery pack does not need to be charged.

[0142] Maintain the above settings and continue monitoring until the current medical task schedule is completed.

[0143] Next, the output power P_sc of the battery pack and the charging power P_sr of the external power supply are obtained in real time, and at the beginning of the next control cycle, the actual uninterruptible load power associated with the smart medical cabin is obtained, denoted as P_uni_real.

[0144] Compare the determined uninterruptible load power P_uni_real with the uninterruptible load power P_uni (the theoretical uninterruptible load power);

[0145] If P_uni_real≥P_uni, it means that the actual power supply to the critical medical equipment meets or exceeds the expected demand, and the power supply guarantee effect of the critical operating medical equipment sequence is judged as successful.

[0146] If P_uni_real < P_uni, it indicates that the actual power supply to the critical medical equipment is insufficient. Since the power supply to the critical medical equipment sequence cannot be interrupted or lower than the theoretical power supply to prevent serious medical accidents, the power supply guarantee status of the critical medical equipment sequence is determined to be a failure. At the same time, an emergency alarm is triggered, the backup emergency power supply prepared by the intelligent medical cabin is activated, and the medical equipment in the critical medical equipment sequence is immediately powered individually. Simultaneously, a guarantee status notification associated with the power supply guarantee failure is generated to notify the operators to carry out timely maintenance.

[0147] All data in the formulas described above have been calculated with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0148] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0149] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A charging adaptive control and adjustment method for an intelligent medical cabin, characterized in that, The method includes: Step 1, determining the operating status of the intelligent medical cabin, extracting all medical devices within the intelligent medical cabin, and separating operating medical devices from non-operating medical devices; performing a screening operation on operating medical devices based on a predefined device weight list to construct a sequence of operating medical devices and a sequence of important operating medical devices; determining the important operating medical device sequence and the uninterrupted load power and total demand load power associated with the operating medical device sequence at a given time; Step 2, obtaining the maximum external rechargeable power and the charging power and state of charge of the battery pack within the intelligent medical cabin at a given time, calculating the maximum charging power and maximum discharging power in conjunction with the real-time battery health status of the battery pack, and determining the battery power range corresponding to the battery pack at a given time; Step 3, adjusting the output power of the battery pack and the charging power of the external power source within the intelligent medical cabin in the next control cycle based on the total demand load power and the battery power range; verifying the protection effect based on the uninterrupted load power in the current control cycle and the uninterrupted load power in the next control cycle, determining the protection effect status, and generating a protection effect status notification to notify the operator.

2. The method according to claim 1, characterized in that, In step one, the specific method for constructing the sequence of operating medical devices is as follows: Determine the current moment and, based on the energy management system integrated into the intelligent medical cabin, determine the instantaneous power of all medical devices within the intelligent medical cabin; mark medical devices with zero instantaneous power as non-operating medical devices; extract the device identifiers of all medical devices with non-zero instantaneous power and compare them with the device identifiers of medical devices in the current medical task plan table, where the medical task plan table includes the device identifiers of activated medical devices and the planned working time of the medical tasks; extract all medical devices that pass the comparison and include them in the set of operating medical devices, marking the remaining medical devices as non-operating medical devices; verify the total number of medical devices in the set of operating medical devices against the total number of medical devices in the medical task plan table; if the total number of medical devices does not match, issue an audible and visual alarm to remind the operator; otherwise, continue monitoring.

3. The method according to claim 2, characterized in that, In step one, the specific method for constructing the sequence of operating medical equipment and the sequence of important operating medical equipment is as follows: Extract the set of operating medical equipment; determine the equipment weights of all medical equipment in the set based on a predefined equipment weight list and a medical task plan table. The equipment weight list contains the names of the medical equipment corresponding to the equipment weights of all medical equipment in the intelligent medical cabin. Sort all medical equipment in the set of operating medical equipment from largest to smallest according to their equipment weight values ​​and denote them as the sequence of operating medical equipment E1, E2, ..., Ej, where j is the total number of medical equipment in the set of operating medical equipment. Obtain the medical equipment in the sequence of operating medical equipment E1, E2, ..., Ej whose equipment weights are greater than a preset threshold for important equipment weights, and sort them from largest to smallest according to their equipment weight values ​​and denote them as the sequence of important operating medical equipment E1, E2, ..., Ek, where k represents the total number of medical equipment whose equipment weights are greater than the preset threshold for important equipment weights, and k ≤ j.

4. The method according to claim 3, characterized in that, In step one, the specific method for determining the sequence of important operating medical equipment and the uninterrupted load power and total required load power associated with the sequence of operating medical equipment and time is as follows: Determine the current time, denoted as t1; extract the load power corresponding to m times within a control cycle starting from the current time t1 for any medical equipment Ei in the sequence of operating medical equipment E1, E2, ..., Ej, and record them in chronological order as the load power sequence of medical equipment Ei, where the control cycle is a preset time period, m is the total number of times within the control cycle, and i is the counting index, with a value range from 1 to j; determine the increment of each of the m load powers. The weights are calculated as follows: m weights sum to 1, and all are greater than 0, increasing sequentially according to a preset linear ratio. The load power sequence is weighted and summed, denoted as the baseline load power P_Ei of medical device Ei within this control cycle. Similarly, the baseline load power of each medical device in the sequence E1, E2, ..., Ej is determined, forming the baseline load power sequence P_E1, P_E2, ..., P_Ej. The baseline load power sequence is summed, denoted as the total demand load power P_all. The first k baseline load powers in the baseline load power sequence are extracted and summed, denoted as the uninterruptible load power P_uni.

5. The method according to claim 4, characterized in that, In step two, the specific method for determining the battery power range corresponding to the battery pack at a given time is as follows: Obtain the maximum rechargeable power GM provided by the external power source; determine the charging power corresponding to each moment of the control cycle within the current time t1 of the battery pack in the intelligent medical cabin, and construct a charging power sequence P_cd1, P_cd2, ..., P_cdm in chronological order; obtain the state of charge sequence SOC1, SOC2, ..., SOCm of the battery pack within the control cycle based on the energy management system integrated into the intelligent medical cabin, as well as the battery health state SOH, where the battery health state SOH is 100% when it is in an ideal health state, and 0% at its lowest; calculate the current maximum charging power P_cd and maximum discharging power P_fd of the battery pack based on the battery health state SOH: P_cd = -[GM × (1 - (100% - SOH) / 100%)], where the maximum charging power... The charging rate P_cd is negative, indicating charging; P_fd = |P_cd × (1 - (100% - SOH) / 100%)|, where the maximum discharge power P_fd is positive, indicating discharge; based on the state of charge sequence SOC1, SOC2, ..., SOCm, the state of charge SOCn at any time n within the control period is determined using: ADJ = (SOC_tar - SOCn) / SOC_range; the state of charge adjustment coefficient ADJ is determined, where SOC_tar is the preset target state of charge, SOC_range is the state of charge range, determined based on the battery pack type and considered a known value, and n is the counting index, ranging from 1 to m; based on the state of charge adjustment coefficient ADJ, the maximum charging power P_cd, and the maximum discharge power P_fd, the battery power range corresponding to the time is determined, denoted as: P_acc = [P_min, P_max].

6. The method according to claim 5, characterized in that, In step two, the specific method for determining the battery power range corresponding to the battery pack and time further includes: when the state of charge (SOCn) is less than the target state of charge (SOC_tar), the maximum charging power P_min associated with the battery pack at time n is calculated using P_min=min(P_cd,P_cd×(1+ADJ)); the maximum discharging power P_max associated with the battery pack at time n is calculated using P_max=min(P_fd,P_fd×(1-ADJ)); when the state of charge (SOCn) is greater than the target state of charge (SOC_tar), the maximum charging power P_min associated with the battery pack at time n is calculated using P_max=min(P_fd,P_fd×(1-ADJ)). When SOC_tar is reached, the maximum charging power P_min associated with the battery pack at time n is calculated using P_min=max(P_cd,P_cd×(1+ADJ)); the maximum discharging power P_max associated with the battery pack at time n is calculated using P_max=max(P_fd,P_fd×(1-ADJ)); and the battery power range P_acc=[P_min,P_max] of the battery pack at time n is constructed by combining the maximum charging power P_min and the maximum discharging power P_max.

7. The method according to claim 6, characterized in that, In step three, the specific method for adjusting the output power of the smart medical cabin's battery pack and the charging power of the external power supply in the next control cycle based on the total demand load power and the battery power range is as follows: Extract the total demand load power P_all and compare it with the battery power range P_acc; if P_all is greater than 0 and P_all belongs to [P_min, P_max], then obtain the current state of charge of the battery pack and compare it with the preset minimum state of charge safety threshold and maximum state of charge safety threshold; if the state of charge is between the minimum state of charge safety threshold and the maximum state of charge safety threshold, set the next... Within one control cycle, the battery pack output power P_sc = P_all, and the external power supply charging power P_sr = 0. If the state of charge is less than the minimum state of charge safety threshold, the battery pack output power P_sc = P_min and the external power supply charging power P_sr = P_all + P_min are set for the next control cycle. If the state of charge is greater than the maximum state of charge safety threshold, the battery pack output power P_sc = P_all and the external power supply charging power P_sr = 0 are set for the next control cycle. The above settings are maintained and continuous monitoring is performed until the current medical task schedule is completed.

8. The method according to claim 7, characterized in that, In step three, the protection effect status is determined and a protection effect status notification is generated. The specific way to notify the operator is as follows: the output power P_sc of the battery pack and the charging power P_sr of the external power supply are obtained in real time; at the beginning of the next control cycle, the actual uninterruptible load power P_uni_real is obtained. The uninterruptible load power P_uni_real is compared with the uninterruptible load power P_uni. If P_uni_real ≥ P_uni, the power supply guarantee status of the important operating medical equipment sequence is determined to be successful. If P_uni_real < P_uni, the power supply guarantee status of the important operating medical equipment sequence is determined to be unsuccessful, an emergency alarm is triggered, the prepared backup emergency power supply is started to supply power to the medical equipment in the important operating medical equipment sequence, a guarantee status notification associated with the failure of the power supply guarantee status is generated, and the operator is notified to carry out maintenance.

Images