Emergency all-in-one machine power supply management method and equipment oriented to extreme environment and medium
By interacting with the functional modules of the emergency rescue device through the main control unit, a power supply topology and operating condition iteration characteristics are constructed, which solves the problem of power management response lag in emergency rescue equipment under extreme environments, realizes seamless power switching and energy balance, and ensures the continuous availability of the equipment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-17
- Publication Date
- 2026-03-27
AI Technical Summary
Existing power management solutions for emergency medical equipment are slow to respond and lack flexibility when faced with complex and dynamic load changes and environmental disturbances, making it difficult to achieve a dynamic balance between energy efficiency, performance, and thermal safety.
By interacting with the emergency medical device's functional modules, the main control unit analyzes the power demand characteristics, constructs the module's power supply topology, monitors the grid power supply and built-in battery in real time, determines the main and backup power switching balance control strategy, and performs dynamic management based on the iterative characteristics of operating conditions to achieve seamless power switching and energy balance.
To achieve seamless power switching in extreme environments, prevent equipment from losing power or experiencing voltage collapse, rationally allocate limited power, and ensure the continuous availability and intelligent operation of equipment.
Smart Images
Figure CN121749475A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power electronics intelligent control technology, and in particular to a power management method, device and medium for an integrated emergency rescue machine for extreme environments. Background Technology
[0002] As emergency rescue scenarios become increasingly complex and diverse, the requirements for the power supply and heat dissipation control systems of portable emergency rescue equipment are becoming increasingly stringent. Existing power management solutions have numerous limitations, typically featuring fixed functions, fixed output voltage ranges, and a lack of intelligent control capabilities. Switching between mains power and backup batteries is based on hard switching using comparators, resulting in jitter and slow response. Heat dissipation relies on single-point adjustment of fan speeds at fixed speeds, leading to slow system response and high energy consumption. This makes them inflexible in dealing with complex, dynamic, and unpredictable load changes and environmental disturbances, making it difficult to achieve a globally optimal dynamic balance among conflicting objectives such as energy efficiency, performance, and thermal safety.
[0003] It should be noted that the information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention
[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a power management method, device, and medium for emergency rescue integrated machines in extreme environments. It solves the problems of sluggish power management response and low flexibility in complex situations found in existing technologies, achieving intelligent continuous operation under complex working conditions and ensuring the continuous availability of emergency rescue equipment. The specific technical solution is as follows:
[0005] According to a first aspect of the present invention, a power management method for an integrated emergency rescue device for extreme environments is provided, the method comprising:
[0006] Upon receiving the emergency equipment activation command, the main control unit interacts with the functional modules of the emergency integrated machine to analyze the power supply requirements of the functional modules and construct the module power supply topology.
[0007] The main control unit is used to perform real-time detection of grid power and built-in battery to obtain a balanced control strategy for main and backup power switching.
[0008] The set of functional modules is traversed to perform iterative interactive analysis of working conditions in a time sequence, and the set of working condition iterative features is determined.
[0009] By combining the main and backup power switching equalization control strategy, the operating condition iteration feature set, and the module power supply topology, a target dynamic management scheme is obtained.
[0010] In one implementation, the main control unit performs real-time detection of the grid power supply and built-in battery status to obtain a main / backup power switching balancing control strategy, and also performs the following processing:
[0011] The main control unit is used to detect the presence of grid power. If grid power is present, the voltage data of the system voltage processed by the buck-boost voltage converter within a preset detection window is monitored to obtain a voltage monitoring data sequence.
[0012] By traversing the voltage monitoring data sequence and comparing it with the rated voltage, voltage error and voltage change rate analysis are performed to obtain voltage error sequence and voltage change rate sequence;
[0013] The mean drift of the voltage error sequence and the voltage change rate sequence are identified respectively to determine the target voltage error and the target voltage change rate.
[0014] The control strategy is analyzed according to the preset decision classification table to obtain the main and backup power switching equalization control strategy.
[0015] In one implementation, mean drift identification is performed on the voltage error sequence and the voltage change rate sequence respectively to determine the target voltage error and the target voltage change rate, and the following processing is also performed:
[0016] Calculate the mean of the voltage error sequence to obtain the mean voltage error;
[0017] Using the average voltage error as the initial drift center, and according to the preset drift bandwidth, the initial drift center is iterated multiple times in the voltage error sequence until the preset stopping requirement is met, thereby determining the target drift center;
[0018] The target drift center is taken as the target voltage error;
[0019] The mean of the voltage change rate sequence is calculated, and the drift of the calculation result is identified to obtain the target voltage change rate.
[0020] In one implementation, the set of functional modules is traversed to perform iterative interactive analysis of operating conditions to determine the set of iterative operating condition features, and the following processing is also performed:
[0021] The functional module set is monitored for operation status according to the preset operating condition indicator set to obtain the operating condition indicator set sequence set, wherein the preset operating condition indicator set includes operating indicators, temperature indicators and heat dissipation indicators.
[0022] The set of operating condition index sequence groups is traversed to perform operating condition feature analysis, thereby obtaining a set of operating condition feature sequences.
[0023] Extract the first working condition feature sequence from the set of working condition feature sequences, perform working condition time-series iterative interaction analysis on the first working condition feature sequence, determine the first working condition iterative feature, and add the first working condition iterative feature to the set of working condition iterative features.
[0024] In one implementation, a first working condition feature sequence is extracted from the set of working condition feature sequences; the first working condition feature sequence is subjected to working condition temporal iterative interaction analysis to determine the first working condition iterative features; and the following processing is also performed:
[0025] The similarity between the first working condition feature and the second working condition feature in the first working condition feature sequence is calculated, and the first adjacency temporal iteration matrix is constructed based on the calculation results.
[0026] The first adjacency temporal iterative matrix is used to perform temporal iterative interactive analysis on the second working condition features to obtain the stage working condition iterative features.
[0027] Similarly, based on the stage working condition iterative features, the third working condition feature of the first working condition feature sequence is subjected to working condition temporal iterative interactive analysis, and the stage working condition iterative features are updated according to the analysis results. Based on the updated stage working condition iterative features, the subsequent working condition features are subjected to working condition temporal iterative interactive analysis until the last position is reached, thus obtaining the first working condition iterative features.
[0028] In one implementation, the similarity between the first working condition feature and the second working condition feature in the first working condition feature sequence is calculated, and a first adjacency temporal iterative matrix is constructed based on the calculation result. The following processing is also performed:
[0029] The similarity of the same type of sub-features in the first and second working condition features is calculated respectively to obtain the calculation results, wherein the calculation results include the sub-feature similarity set;
[0030] The sub-feature similarity set is normalized and matrixed to obtain the first adjacency temporal iteration matrix.
[0031] In one implementation, by combining the main / backup power switching equalization control strategy, the operating condition iteration feature set, and the module power supply topology for management, a target dynamic management scheme is obtained, and the following processing is also performed:
[0032] Based on the set of iterative operating conditions and the power supply topology of the module, the main and backup power switching of the functional module is identified, and a set of identification results is obtained.
[0033] The recognition result set is balanced according to a preset priority order to obtain a functional module balanced control strategy.
[0034] The target dynamic management scheme is obtained by integrating the main and backup power switching equalization control strategy and the functional module equalization control strategy.
[0035] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, the memory storing executable instructions, wherein when the processor executes the executable instructions stored in the memory, it implements any step of the first aspect disclosed in this application.
[0036] A third aspect of this application discloses a computer-readable storage medium storing a computer program for performing any step of the first aspect of this application.
[0037] Beneficial effects of the embodiments of the present invention:
[0038] In the solution provided by this invention, by receiving the emergency medical device activation command, the main control unit interacts with the functional modules of the emergency medical device to analyze the power supply demand characteristics of the functional modules, construct the module power supply topology, and then uses the main control unit to perform real-time detection of the grid power supply and built-in battery to obtain a main / backup power switching and balancing control strategy. Then, it traverses the functional module set for iterative interactive analysis of operating conditions to determine the set of iterative operating conditions characteristics. By combining the main / backup power switching and balancing control strategy, the set of iterative operating conditions characteristics, and the module power supply topology, a target dynamic management scheme is obtained. This achieves seamless power switching in extreme environments, preventing equipment power failure or voltage collapse. Through priority scheduling and energy balancing, limited electrical energy is allocated most rationally. Real-time adjustment of heat dissipation and power output based on iterative operating conditions characteristics prevents local overheating, achieving intelligent continuous operation under complex conditions and ensuring the continuous availability of the emergency medical device. Of course, implementing any product or method of this invention does not necessarily require achieving all of the above advantages simultaneously. Attached Figure Description
[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0040] Figure 1 A schematic diagram of the power management method for an integrated emergency rescue device for extreme environments provided by the present invention is shown.
[0041] Figure 2 An internal structural diagram of the electronic device provided by the present invention is shown.
[0042] Explanation of reference numerals in the attached diagram: Bus 500, Receiver 501, Processor 502, Transmitter 503, Memory 504, Bus Interface 505. Detailed Implementation
[0043] To facilitate understanding of the present invention, a more complete description of the invention will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein; rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the invention.
[0044] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.
[0045] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.
[0046] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.
[0047] The present invention provides a power management method, device and medium for emergency rescue integrated machine for extreme environments, which is used to solve the problems of slow response and low flexibility of power management in complex situations in the prior art.
[0048] Example 1: See Figure 1 The flowchart of the power management method for an integrated emergency rescue device for extreme environments provided in this embodiment of the invention includes:
[0049] A1: Receives the emergency equipment activation command, interacts with the functional modules of the emergency integrated machine through the main control unit, analyzes the power supply requirements of the functional modules, and constructs the module power supply topology.
[0050] In one possible embodiment, the emergency medical device is a portable emergency medical device, which includes many functional modules such as respiratory therapy, drug infusion, automatic defibrillation, vital sign monitoring, and heat dissipation system. In order to reliably manage the power supply during the operation of the device, it is necessary to first perform interactive analysis on the above-mentioned functional modules through the active unit to determine the power supply requirements of different functional modules.
[0051] Preferably, the emergency equipment activation command refers to the activation signal issued by the user or the superior control system when the integrated emergency device begins operation, used to wake up the various modules inside the integrated emergency device. Based on low power consumption and the specific operational requirements of each module, the operating voltage settings for each module component are different. For example, the turbine fan in the respiratory module requires 24V DC, meaning the respiratory module's power supply cannot be lower than 24V. Conversely, the automatic defibrillator module has no electronic components requiring voltage higher than 9V, therefore its power supply cannot be lower than 9V. Therefore, each functional module uses a different power supply voltage: the respiratory therapy module uses 24V DC, the medication infusion module uses 9V DC, the automatic defibrillator module uses 9V DC, the vital signs monitoring module uses 12V DC, the cooling fan uses 24V DC, and the main control unit system uses 5V DC.
[0052] By capturing the power supply requirements of devices in different functional modules, such as whether they require DC / AC power and the required power supply voltage, and treating each functional module as a node, the corresponding power supply requirements are identified on the corresponding node. Combined with the location of different functional modules and their connection relationships with the power system, the module power supply topology is constructed. In other words, the module power supply topology is a structural model that describes the connection relationships and power supply paths between each module and the power system from both physical and logical perspectives.
[0053] By clearly defining the power supply requirements of each functional module, a basic framework is provided for subsequent dynamic power management, laying the data and structural foundation for subsequent power switching and energy balance control, and enabling orderly startup and power supply planning of equipment in complex environments.
[0054] A2: Utilize the main control unit to perform real-time detection of grid power and built-in battery to obtain a balanced control strategy for main and backup power switching.
[0055] Furthermore, by utilizing the main control unit to perform real-time detection of the grid power supply and built-in battery status to obtain a main / backup power switching balancing control strategy, step A2 in this embodiment may further include:
[0056] The main control unit is used to detect the presence of grid power. If grid power is present, the voltage data of the system voltage processed by the buck-boost voltage converter within a preset detection window is monitored to obtain a voltage monitoring data sequence.
[0057] By traversing the voltage monitoring data sequence and comparing it with the rated voltage, voltage error and voltage change rate analysis are performed to obtain voltage error sequence and voltage change rate sequence;
[0058] The mean drift of the voltage error sequence and the voltage change rate sequence are identified respectively to determine the target voltage error and the target voltage change rate.
[0059] The control strategy is analyzed according to the preset decision classification table to obtain the main and backup power switching equalization control strategy.
[0060] Furthermore, mean drift identification is performed on the voltage error sequence and voltage change rate sequence respectively to determine the target voltage error and target voltage change rate. In this embodiment, step A2 may further include:
[0061] Calculate the mean of the voltage error sequence to obtain the mean voltage error;
[0062] Using the average voltage error as the initial drift center, and according to the preset drift bandwidth, the initial drift center is iterated multiple times in the voltage error sequence until the preset stopping requirement is met, thereby determining the target drift center;
[0063] The target drift center is taken as the target voltage error;
[0064] The mean of the voltage change rate sequence is calculated, and the drift of the calculation result is identified to obtain the target voltage change rate.
[0065] In one possible embodiment, the emergency medical device has two power supply methods: the first is AC 220V grid power, and the second is two built-in DC 16.8V lithium batteries with a capacity of 6600mAh. It should be noted that when the grid power fails, it can seamlessly switch to battery power. Since the voltage of each module in the system, such as the 24V for the respiratory module, 9V for the defibrillator module, and 12V for the monitoring module, is obtained from the system voltage of 16.8V through a step-up / step-down voltage converter, regardless of whether the system is powered by grid power or the built-in batteries, the voltage of each module is obtained from the system voltage of 16.8V through a step-up / step-down voltage converter. Therefore, DC 16.8V is defined as the rated voltage of the device's system voltage. Grid power refers to external AC power, such as mains power or emergency power vehicle power, which provides stable power to the device through rectification and voltage regulation modules. Built-in batteries refer to the DC lithium battery pack built into the emergency medical device, generally including two DC lithium batteries, as a backup power source. A step-up / step-down voltage converter is a power electronic module used to convert the voltage of the built-in batteries or mains power into different DC voltage levels required by each module.
[0066] The main control unit first checks if mains power input is available. If it is, it indicates that mains power is available, and then monitors the system voltage output by the step-up / step-down voltage converter, forming a voltage monitoring sequence over a period of time. This sequence is then compared with the device's rated voltage to obtain the voltage error and its trend. The preset detection window is a time period pre-set by those skilled in the art. The voltage monitoring data sequence refers to the voltage data acquired within a continuous sampling time window. Voltage error represents the deviation between the actual voltage and the rated voltage, while the voltage change rate reflects the speed of voltage fluctuation.
[0067] Preferably, the voltage value at each time point in the voltage monitoring data sequence is calculated to differ from the rated voltage to obtain a voltage error sequence. The voltage change rate sequence is obtained by calculating the fluctuation variance of the data subsequences every 10 milliseconds in the voltage monitoring data sequence. Since the obtained sequences represent the voltage performance within a preset detection window, to capture the most common scenarios within this window, mean drift identification is performed on both the voltage error sequence and the voltage change rate sequence to determine the target voltage error and the target voltage change rate.
[0068] Specifically, the preset drift bandwidth is a neighborhood scale pre-defined by those skilled in the art, and can be characterized by data values. The mean voltage error is set as the initial drift center. Based on the preset drift bandwidth, a set of samples falling within the neighborhood of the voltage error sequence is selected. The weighted center of the samples within the neighborhood is used, with the weight increasing as the distance decreases, to update the drift center until a preset stopping requirement is met. Stopping occurs if two consecutive center changes are less than a preset threshold, the maximum number of iterations is reached, or the time budget is exhausted. The converged target drift center is taken as the target voltage error. Similarly, based on the same principle as obtaining the target voltage error, the mean of the voltage change rate sequence is calculated, and drift identification is performed on the calculation results to obtain the target voltage change rate.
[0069] Preferably, a preset voltage error and voltage change rate classification table is obtained, as shown in Table 1.
[0070] Table 1. Classification of Preset Voltage Error and Voltage Change Rate
[0071] Category Fast fall (DF) Slow fall (DS) Stable (S) Slow rise (IS) Fast rise (IF) Voltage error ＜-0.5 ≥-0.5 0 ≤0.5 ＞0.5 Category Fast fall (DF) Slow fall (DS) Stable (S) Slow rise (IS) Fast rise (IF) Rate of voltage change ＜-0.5 ≥-0.5 0 ≤0.5 ＞0.5
[0072] Based on the 25 combinations of 5 voltage error categories and 5 voltage change rates, a control decision is made according to the preset decision classification table shown in Table 2. Based on the output results and preset rules, the main and backup power switching equalization control strategy is obtained.
[0073] Table 2 Pre-defined decision classification table
[0074] Voltage error Rate of voltage change Output NL DF 100 NL DS 75 NL IS 50 NL IF 25 NL PF 0 NS DF 75 NS DS 50 NS IS 25 NS IF 25 NS PF 0 Z DF 75 Z DS 50 Z IS 25 Z IF 25 Z PF 25 PS DF 50 PS DS 25 PS IS 25 PS IF 25 PS PF 50 PL DF 25 PL DS 25 PL IS 50 PL IF 75 PL PF 100
[0075] The default rules are: if the output is <0, keep the main power on; if the output is 0~25, it is recommended to keep the main power on; if the output is 25~50, prepare to switch; if the output is 50~75, it is recommended to switch; if the output is 75~100, switch immediately.
[0076] A3: Traverse the set of functional modules to perform iterative interactive analysis of working conditions and determine the set of iterative features of working conditions;
[0077] Furthermore, by traversing the set of functional modules to perform iterative interactive analysis of operating conditions and determining the set of iterative operating condition features, step A3 in this embodiment may further include:
[0078] The functional module set is monitored for operation status according to the preset operating condition indicator set to obtain the operating condition indicator set sequence set, wherein the preset operating condition indicator set includes operating indicators, temperature indicators and heat dissipation indicators.
[0079] The set of operating condition index sequence groups is traversed to perform operating condition feature analysis, thereby obtaining a set of operating condition feature sequences.
[0080] Extract the first working condition feature sequence from the set of working condition feature sequences, perform working condition time-series iterative interaction analysis on the first working condition feature sequence, determine the first working condition iterative feature, and add the first working condition iterative feature to the set of working condition iterative features.
[0081] Furthermore, extracting a first working condition feature sequence from the set of working condition feature sequences, performing a working condition time-series iterative interactive analysis on the first working condition feature sequence, and determining the first working condition iterative features, step A3 in this embodiment may further include:
[0082] The similarity between the first working condition feature and the second working condition feature in the first working condition feature sequence is calculated, and the first adjacency temporal iteration matrix is constructed based on the calculation results.
[0083] The first adjacency temporal iterative matrix is used to perform temporal iterative interactive analysis on the second working condition features to obtain the stage working condition iterative features.
[0084] Similarly, based on the stage working condition iterative features, the third working condition feature of the first working condition feature sequence is subjected to working condition temporal iterative interactive analysis, and the stage working condition iterative features are updated according to the analysis results. Based on the updated stage working condition iterative features, the subsequent working condition features are subjected to working condition temporal iterative interactive analysis until the last position is reached, thus obtaining the first working condition iterative features.
[0085] Furthermore, similarity calculation is performed on the first working condition feature and the second working condition feature in the first working condition feature sequence, and a first adjacency temporal iterative matrix is constructed based on the calculation results. Step A3 in this embodiment may further include:
[0086] The similarity of the same type of sub-features in the first and second working condition features is calculated respectively to obtain the calculation results, wherein the calculation results include the sub-feature similarity set;
[0087] The sub-feature similarity set is normalized and matrixed to obtain the first adjacency temporal iteration matrix.
[0088] It should be noted that the operating condition index set is a set of quantitative indicators used to describe the current operating status of the module, including operating indicators such as output power, current, voltage, and speed; temperature indicators such as the core and casing temperatures; and heat dissipation indicators such as fan speed and heat flux. The operating condition feature sequence set is a sequence of operating status features of multiple modules collected in chronological order. The main control unit collects real-time data from the sensors of each module at a fixed sampling period, such as 1s to 5s, to obtain the operating condition index set sequence. Then, it iterates through the operating condition index set sequence and performs operating condition feature analysis from dimensions such as average value, variance, rate of change, peak and valley values, and thermal gradient to obtain the operating condition feature sequence set.
[0089] Then, the similarity between the first and second working condition features in the first working condition feature sequence is calculated, and the calculation results are normalized to obtain a calculation result including a set of sub-feature similarities. The sub-feature similarity set is normalized using a logistic regression function, and the result is added to an initially empty matrix to obtain a first adjacency temporal iterative matrix. Each element in the matrix represents the similarity degree of each feature dimension. Then, a graph convolutional network is used to perform convolutional interaction analysis on the first adjacency temporal iterative matrix and the second working condition feature to obtain stage working condition iterative features. Similarly, based on the stage working condition iterative features, the third working condition feature of the next position is subjected to working condition temporal iterative interaction analysis, continuing until the end of the sequence, forming the first working condition iterative feature of this module throughout the entire operating cycle. This iterative feature is added to the working condition iterative feature set for multi-module comprehensive analysis.
[0090] For example, the emergency rescue device simultaneously operates the respiratory therapy module and the defibrillation module. The system collects data showing that the respiratory module's power is increasing, its temperature is rising rapidly, and its heat dissipation efficiency is decreasing. Through time-series iterative analysis, the similarity matrix shows that the correlation between power and temperature characteristics is strengthening, while the correlation between heat dissipation characteristics is weakening. Based on this, the main control unit determines that the system is about to enter a high-heat risk zone, triggering dynamic frequency reduction and fan speed increase, while delaying high-energy charging of the defibrillation module. The entire process achieves predictive coordination between multiple modules, avoiding thermal overload and power interruption.
[0091] A4: By combining the main and backup power switching equalization control strategy, the operating condition iteration feature set, and the module power supply topology, a target dynamic management scheme is obtained.
[0092] Furthermore, by combining the main / backup power switching equalization control strategy, the operating condition iteration feature set, and the module power supply topology for management, a target dynamic management scheme is obtained. In this embodiment, step A4 may further include:
[0093] Based on the set of iterative operating conditions and the power supply topology of the module, the main and backup power switching of the functional module is identified, and a set of identification results is obtained.
[0094] The recognition result set is balanced according to a preset priority order to obtain a functional module balanced control strategy.
[0095] The target dynamic management scheme is obtained by integrating the main and backup power switching equalization control strategy and the functional module equalization control strategy.
[0096] It should be noted that the preset priority order is: Automated Defibrillator module > Respiratory Therapy module > Medication Infusion module > Vital Signs Monitoring module. Operating condition status information for each functional module is extracted from the operating condition iteration feature set, such as power change trends, temperature change trends, heat dissipation efficiency, and load dynamics. The operating condition iteration features are compared with the corresponding requirements in the module power supply topology to identify whether each module is currently in a steady state, overload state, or risk state. If a module is detected to be in an overload state or voltage fluctuation range, the main control unit performs primary / backup power switching identification based on the power supply status, grid power voltage stability, battery capacity, and temperature margin. For example, when the grid power voltage deviates from the rated range and the fluctuation rate exceeds a set threshold, high-priority modules are switched to battery power; when the battery discharge rate exceeds the safety limit or the temperature rises abnormally, non-critical modules are switched back to grid power or temporarily limited in power. The above judgment results are recorded as an identification result set, which includes the current power supply type, switching status, power level, and sustainable operating time assessment for each module.
[0097] For example, in a high-temperature outdoor environment, if the grid power input becomes unstable due to generator fluctuations, and the system detects a continuous increase in the voltage error of the defibrillator module and an increase in the rate of temperature characteristic change, the main control unit immediately performs a master-slave switch, transferring the defibrillator module to battery power. Simultaneously, if the monitoring module is identified as being in a low-power steady state, grid power supply is maintained. This process ensures continuous power supply to critical modules, preventing them from being affected by voltage fluctuations.
[0098] The main control unit iterates through the identification result set according to priority and performs power equalization processing under energy constraints. The equalization processing mainly includes: dynamically adjusting the power upper limit of each module based on the remaining battery capacity and grid power stability to ensure stable power supply to critical modules; predicting future power demands based on module operating condition iteration trends and adjusting the energy allocation cycle in advance; and reducing power or adjusting heat dissipation strategies for high-temperature modules to prevent overheating and power supply anomalies. The equalization results are output as a functional module equalization control strategy, which includes the power allocation ratio, switching sequence, and power usage mode for each module.
[0099] The main / backup power switching balancing control strategy and the functional module balancing control strategy are compared. When the two strategies make different operational suggestions for the same module, such as one requiring switching to battery while the other requires maintaining mains power, arbitration is conducted through priority weighting to ensure task continuity. Every fixed period, such as 5 seconds or 30 seconds, the operating conditions and power status are reassessed, and the management scheme is automatically adjusted.
[0100] Example 2: Figure 2 The diagram shown is a schematic representation of the structure of an exemplary electronic device of this application. Figure 2 In this document, the bus architecture is represented by bus 500. Bus 500 may include any number of interconnected buses and bridges, and bus 500 connects various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 505 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium.
[0101] The memory 504, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the power management method for the emergency rescue integrated machine in extreme environments in this embodiment of the application. The processor 502 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 504, thereby realizing the aforementioned power management method for the emergency rescue integrated machine in extreme environments.
[0102] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
[0103] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.
Claims
1. A power management method for an integrated emergency rescue device for extreme environments, characterized in that, The method includes: Upon receiving the emergency equipment activation command, the main control unit interacts with the functional modules of the emergency integrated machine to analyze the power supply requirements of the functional modules and construct the module power supply topology. The main control unit is used to perform real-time detection of grid power and built-in battery to obtain a balanced control strategy for main and backup power switching. The set of functional modules is traversed to perform iterative interactive analysis of working conditions in a time sequence, and the set of working condition iterative features is determined. By combining the main and backup power switching equalization control strategy, the operating condition iteration feature set, and the module power supply topology, a target dynamic management scheme is obtained.
2. The power management method for an integrated emergency rescue device for extreme environments as described in claim 1, characterized in that, The main control unit performs real-time monitoring of the grid power supply and built-in battery status to obtain a primary / backup power switching balancing control strategy, including: The main control unit is used to detect the presence of grid power. If grid power is present, the voltage data of the system voltage processed by the buck-boost voltage converter within a preset detection window is monitored to obtain a voltage monitoring data sequence. By traversing the voltage monitoring data sequence and comparing it with the rated voltage, voltage error and voltage change rate analysis are performed to obtain voltage error sequence and voltage change rate sequence; The mean drift of the voltage error sequence and the voltage change rate sequence are identified respectively to determine the target voltage error and the target voltage change rate. The control strategy is analyzed according to the preset decision classification table to obtain the main and backup power switching equalization control strategy.
3. The power management method for an integrated emergency rescue device for extreme environments as described in claim 2, characterized in that, Mean drift identification is performed on the voltage error sequence and voltage change rate sequence respectively to determine the target voltage error and target voltage change rate, including: Calculate the mean of the voltage error sequence to obtain the mean voltage error; Using the average voltage error as the initial drift center, and according to the preset drift bandwidth, the initial drift center is iterated multiple times in the voltage error sequence until the preset stopping requirement is met, thereby determining the target drift center; The target drift center is taken as the target voltage error; The mean of the voltage change rate sequence is calculated, and the drift of the calculation result is identified to obtain the target voltage change rate.
4. The power management method for an integrated emergency rescue device for extreme environments as described in claim 1, characterized in that, The functional module set is traversed to perform iterative interactive analysis of operating conditions to determine the set of iterative operating condition features, including: The functional module set is monitored for operation status according to the preset operating condition indicator set to obtain the operating condition indicator set sequence set, wherein the preset operating condition indicator set includes operating indicators, temperature indicators and heat dissipation indicators. The set of operating condition index sequence groups is traversed to perform operating condition feature analysis, thereby obtaining a set of operating condition feature sequences. Extract the first working condition feature sequence from the set of working condition feature sequences, perform working condition time-series iterative interaction analysis on the first working condition feature sequence, determine the first working condition iterative feature, and add the first working condition iterative feature to the set of working condition iterative features.
5. The power management method for an integrated emergency rescue device for extreme environments as described in claim 4, characterized in that, Extract the first working condition feature sequence from the set of working condition feature sequences, perform a working condition time-series iterative interactive analysis on the first working condition feature sequence, and determine the iterative features of the first working condition, including: The similarity between the first working condition feature and the second working condition feature in the first working condition feature sequence is calculated, and the first adjacency temporal iteration matrix is constructed based on the calculation results. The first adjacency temporal iterative matrix is used to perform temporal iterative interactive analysis on the second working condition features to obtain the stage working condition iterative features. Similarly, based on the stage working condition iterative features, the third working condition feature of the first working condition feature sequence is subjected to working condition temporal iterative interactive analysis, and the stage working condition iterative features are updated according to the analysis results. Based on the updated stage working condition iterative features, the subsequent working condition features are subjected to working condition temporal iterative interactive analysis until the last position is reached, thus obtaining the first working condition iterative features.
6. The power management method for an integrated emergency rescue device for extreme environments as described in claim 5, characterized in that, The similarity between the first working condition feature and the second working condition feature in the first working condition feature sequence is calculated, and a first adjacency temporal iterative matrix is constructed based on the calculation results, including: The similarity of the same type of sub-features in the first and second working condition features is calculated respectively to obtain the calculation results, wherein the calculation results include the sub-feature similarity set; The sub-feature similarity set is normalized and matrixed to obtain the first adjacency temporal iteration matrix.
7. The power management method for an integrated emergency rescue device for extreme environments as described in claim 1, characterized in that, By combining the main / backup power switching equalization control strategy, the operating condition iteration feature set, and the module power supply topology, a target dynamic management scheme is obtained, including: Based on the set of iterative operating conditions and the power supply topology of the module, the main and backup power switching of the functional module is identified, and a set of identification results is obtained. The recognition result set is balanced according to a preset priority order to obtain a functional module balanced control strategy. The target dynamic management scheme is obtained by integrating the main and backup power switching equalization control strategy and the functional module equalization control strategy.
8. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the power management method for an emergency rescue device for extreme environments as described in any one of claims 1-7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the power management method for emergency rescue integrated machines for extreme environments as described in any one of claims 1-7.