An emergency power supply and an emergency power supply method thereof

Abstract

This application belongs to the field of power supply technology and discloses an emergency power supply and its emergency power supply method. The method includes: collecting the remaining battery power, ambient temperature, input and output power data, and solar power data of the emergency power supply, and receiving a load priority list to generate an initial power supply parameter dataset; generating a power allocation strategy through a dynamic allocation algorithm; adaptively compensating the allocation strategy based on the ambient temperature and solar power data to generate an optimized charging and discharging strategy; switching the port on / off state according to the optimized strategy, monitoring changes in port load demand to update the strategy; and activating a fault handling mechanism when abnormal battery temperature or power interruption is detected, cutting off non-critical loads and switching to a backup power supply, pushing real-time status alarms and processing logs, and receiving user feedback to correct the strategy. This application achieves efficient power supply and stable operation of the emergency power supply in complex scenarios through dynamic power allocation, environmental compensation, and closed-loop fault handling.

Description

Technical Field

[0001] This application relates to the field of power supply technology, and in particular to an emergency power supply and its emergency power supply method. Background Technology

[0002] Emergency power supplies, as core equipment to ensure the continuous operation of critical equipment in the event of power outages, are widely used in natural disaster relief, industrial site operations, medical emergency care, and outdoor activities. Especially in complex and ever-changing emergency environments (such as earthquake-stricken areas and field base stations), power supply equipment must have high-capacity energy storage, rapid response capabilities, and environmental adaptability to ensure the stable operation of high-priority loads such as communication equipment and medical instruments.

[0003] Existing emergency power supply technologies mostly employ fixed priority allocation or static charging / discharging strategies, such as allocating power based on preset load levels or relying on a single charging mode (e.g., mains power / solar power) to supplement electricity. While these solutions can provide basic power supply, they have significant drawbacks when faced with dynamic load fluctuations and sudden changes in environmental conditions: the lack of a real-time adaptive power allocation and charging / discharging optimization mechanism means the system cannot dynamically adjust its strategy based on battery status, changes in load demand, and fluctuations in external energy input. This can easily lead to problems such as power outages for critical equipment, battery overload, or low energy utilization. For example, when load demand suddenly increases or solar input drops sharply, traditional methods struggle to redistribute power or switch charging modes in a timely manner, resulting in decreased power supply stability and making it difficult to meet the stringent requirements of complex emergency scenarios.

[0004] Therefore, it is necessary to address the technical problem of unstable power resource allocation in existing technologies under emergency scenarios involving dynamic load fluctuations, changing environmental conditions, and sudden failures. Summary of the Invention

[0005] To address the aforementioned problems, this application provides an emergency power supply and an emergency power supply method thereof.

[0006] In view of the above, the first aspect of this application provides an emergency power supply method for an emergency power source, comprising:

[0007] Collect data on the remaining battery power, ambient temperature, input and output power, and solar power of the emergency power supply, and receive a preset load priority list to generate an initial power supply parameter dataset.

[0008] Based on the initial power supply parameter dataset, a power allocation strategy is generated through a dynamic allocation algorithm;

[0009] Based on the ambient temperature and the solar power data, the power distribution strategy is adaptively compensated, and the charging mode and output power distribution ratio are dynamically adjusted to generate an optimized charging and discharging strategy.

[0010] The intelligent relay is controlled to switch the on / off state of the power supply port according to the optimized charging and discharging strategy, and the port load demand changes are monitored to update the power distribution strategy.

[0011] When an abnormal battery temperature or power interruption is detected, the fault handling mechanism is activated, non-critical loads are disconnected and switched to backup power, real-time status alarms and processing logs are pushed, and user feedback is received to correct the power distribution strategy.

[0012] Optionally, the system collects the remaining battery power, ambient temperature, input / output power data, and solar power data of the emergency power supply, and receives a preset load priority list to generate an initial power supply parameter dataset, including:

[0013] Collect emergency power supply battery remaining power, ambient temperature, input and output power data and solar power data at a preset frequency;

[0014] The system receives a list of load priorities input by the user through an interactive interface, verifies the format and completeness of the list, and generates a standardized priority configuration file.

[0015] The remaining battery power data, ambient temperature, input / output power data, and solar power data are time-aligned with the priority configuration file, and after removing outliers, they are integrated into a structured data package according to a preset data template to obtain the initial power supply parameter dataset.

[0016] Optionally, the step of generating a power allocation strategy based on the initial power supply parameter dataset using a dynamic allocation algorithm includes:

[0017] A multi-objective optimization model is constructed based on the load priority list, and the partition ratio of the critical load protection zone and the non-critical load floating zone is determined through the multi-objective optimization model.

[0018] The available total energy pool is calculated based on the remaining battery power and total battery energy. Based on the partition ratio, the available total energy pool is divided into a critical load protection zone and a non-critical load floating zone according to the load priority level through a dynamic allocation algorithm.

[0019] For the critical load protection zone, the goal is to minimize the sum of the differences between the allocated power and the minimum power requirement of all devices, and to ensure that the minimum power requirement of the devices corresponding to the critical load protection zone is fully met, and to reserve emergency redundancy power, thereby generating the critical load allocation result.

[0020] For the non-critical load floating zone, the power adjustment range of the non-critical load floating zone is dynamically calculated by a fuzzy logic controller, taking into account the battery remaining power decay rate and the ambient temperature change rate. This generates the upper and lower limits of the power adjustment of non-critical equipment and obtains the non-critical load floating rules.

[0021] The critical load allocation results are fused with the non-critical load floating rules to generate an initial power allocation strategy.

[0022] Optionally, the step of fusing the critical load allocation results with the non-critical load floating rules to generate an initial power allocation strategy further includes:

[0023] The feasibility of the initial power allocation strategy under the target scenario is verified through Monte Carlo simulation. If the simulation fails, the process is returned to redistribute the total available energy pool.

[0024] Optionally, the step of adaptively compensating the power distribution strategy based on the ambient temperature and the solar power data, dynamically adjusting the charging mode and output power distribution ratio, and generating an optimized charging and discharging strategy includes:

[0025] By collecting the ambient temperature in real time and combining it with the relationship curve between historical ambient temperature and battery charging and discharging efficiency, the decay trend of battery charging and discharging efficiency within a future preset time period is predicted.

[0026] The maximum allowable power threshold of the charging mode is corrected based on the predicted battery charging and discharging efficiency decay trend and the temperature compensation coefficient calculated based on the ambient temperature.

[0027] Calculate the photovoltaic power fluctuation rate based on the solar power data, and dynamically adjust the charging mode and solar charging weight ratio based on the photovoltaic power fluctuation rate.

[0028] A fuzzy controller is used to optimize the output power allocation ratio in the power allocation strategy based on the temperature compensation coefficient and the solar charging weight ratio, so as to obtain the power allocation correction parameters.

[0029] The power allocation correction parameters are input into the target optimization model for solution, generating an optimized charging and discharging strategy.

[0030] Optionally, the control of the intelligent relay to switch the on / off state of the power supply port according to the optimized charging and discharging strategy, and to monitor changes in port load demand to update the power distribution strategy, includes:

[0031] The optimized charging and discharging strategy is decoded into an executable instruction set, and the on / off timing, expansion trigger threshold, and load monitoring cycle of the power supply port are extracted.

[0032] According to the on / off timing of the power supply port, the intelligent relay drive circuit is controlled to switch the on / off state of the power supply port in priority order.

[0033] If the remaining battery power is lower than the capacity expansion trigger threshold, an encrypted handshake signal is sent to the pre-connected capacity expansion battery pack. After verifying the identity, the battery pack parallel circuit is activated, and the capacity expansion battery pack is dynamically connected to increase the total energy storage capacity.

[0034] The power data of each port load is collected based on the load monitoring cycle, and the power offset is calculated based on the power data of the port load and the expected power in the power distribution strategy.

[0035] If the absolute value of the power offset exceeds the preset offset threshold multiple times consecutively, a strategy update signal is generated, and the power distribution strategy is updated according to the power offset and the access status of the expanded battery pack.

[0036] Optionally, when an abnormal battery temperature or power interruption is detected, a fault handling mechanism is activated to cut off non-critical loads and switch to backup power, while simultaneously pushing real-time status alarms and processing logs, and receiving user feedback to correct the power distribution strategy, including:

[0037] When an abnormal battery temperature or power interruption is detected, a three-level fault classification mechanism is triggered, generating an emergency response instruction set based on the fault type and severity.

[0038] Based on the emergency response instruction set, the intelligent relays of non-critical load power supply ports are disconnected first, and diesel generators or energy storage battery packs are selected as backup power sources according to the availability weight of backup power.

[0039] The system pushes fault types, handling measures, and current load power supply status in real time through an interactive interface, generates encrypted processing logs and synchronizes them to the cloud server, and simultaneously receives policy correction instructions or priority adjustment requests from users to update the power distribution policy.

[0040] Optionally, the method for calculating the availability weight of the backup power supply includes:

[0041] The availability weight of the backup power supply is obtained by weighting and summing the remaining capacity and response speed of the backup power supply using preset weighting coefficients.

[0042] The second aspect of this application provides an emergency power supply, which is controlled using an emergency power supply method according to any one of the emergency power supplies described in the first aspect. The emergency power supply includes:

[0043] The sensor module includes a power monitoring sensor, an ambient temperature sensor, and a power sensor deployed in the battery pack's input / output ports;

[0044] The data processing module integrates a multi-core processor and a dynamic allocation algorithm unit, and is used to generate and optimize power allocation strategies based on the data collected by the sensor module and the load priority list.

[0045] The execution module, including intelligent relays and load monitoring circuits, is used to execute charging and discharging strategies to switch the on / off state of the power supply port and monitor changes in port load demand.

[0046] The interactive module is equipped with a high-definition touch screen and a cloud communication interface.

[0047] Optionally, the emergency power supply also includes a housing assembly, the inside of which is provided a battery module;

[0048] The outer wall of the housing assembly is provided with a power output module and an expansion interface module, and the interaction module is integrated on one side of the expansion interface module.

[0049] The housing assembly is equipped with casters and a drive assembly for moving the casters.

[0050] As can be seen from the above technical solutions, this application has the following advantages:

[0051] This application provides an emergency power supply method for an emergency power source. It generates an initial power supply parameter dataset by collecting data on remaining battery power, ambient temperature, input / output power, and solar power, and receiving a preset load priority list. Based on this dataset, a dynamic allocation algorithm is run, combining real-time power consumption and load priorities to generate a power allocation strategy that ensures continuous power supply to critical equipment and avoids system overload. Subsequently, the power allocation strategy is adaptively compensated based on changes in ambient temperature and fluctuations in solar input power, dynamically adjusting the charging / discharging mode and power allocation ratio to adapt to the environment, extend battery life, and improve energy efficiency. The optimized charging / discharging strategy controls intelligent relays to switch the on / off state of power supply ports, while continuously monitoring load changes to update the strategy. When abnormal temperature or power interruption is detected, non-critical loads are immediately disconnected and a backup power source is switched on. Alarm logs are pushed through an interactive interface, and user feedback is received, ensuring rapid response in emergency scenarios and achieving closed-loop optimization. This method, through intelligent dynamic power allocation, adaptive environmental compensation, and closed-loop fault handling, achieves efficient power supply and stable operation of the emergency power source in complex scenarios. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A schematic flowchart illustrating an emergency power supply method for an emergency power source provided in an embodiment of this application;

[0054] Figure 2 A schematic diagram of an emergency power supply provided in an embodiment of this application;

[0055] Figure 3 This is another structural schematic diagram of an emergency power supply provided in an embodiment of this application;

[0056] Figure 4 Provided for the embodiments of this application Figure 3 A front view schematic diagram of the emergency power supply structure in the diagram;

[0057] Among them, 210: sensor module; 220: data processing module; 230: execution module; 240: interaction module; 10: housing assembly; 21: power output module; 22: expansion interface module; 30: moving wheel; 40: high-definition touch screen. Detailed Implementation

[0058] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0059] For easier understanding, please refer to Figure 1 This application provides an emergency power supply method for an emergency power source, comprising:

[0060] S1. Collect the remaining battery power, ambient temperature, input and output power data and solar power data of the emergency power supply, and receive the preset load priority list to generate an initial power supply parameter dataset.

[0061] Sensor modules configured in the emergency power supply can collect real-time data on remaining battery charge, ambient temperature, input / output power, and solar power, and integrate this data with a pre-defined load priority list to generate an initial power supply parameter dataset. This step provides the data foundation for subsequent strategies: remaining battery charge (SOC) reflects the current energy storage status, ambient temperature assesses the risk of battery charge / discharge efficiency degradation, and input / output power data quantifies energy input and load demand. Combining this with high-precision sensors in the emergency power supply (such as power monitoring sensors and industrial waterproof sockets) ensures the real-time nature and reliability of data acquisition, laying the core input conditions for dynamic power allocation.

[0062] S2. Based on the initial power supply parameter dataset, a power allocation strategy is generated through a dynamic allocation algorithm;

[0063] Based on the initial dataset, a dynamic allocation algorithm is run through the data processing module to generate a power allocation strategy by combining real-time battery power and load priority. This step uses a multi-objective optimization model (such as power supply stability and battery life) to divide the energy allocation rules between critical and non-critical loads. The emergency power supply's lithium iron phosphate battery (60Ah / 105Ah high capacity) and 5000W rated power output capability support the algorithm to flexibly adjust the power limit of non-critical loads (such as lighting equipment) while ensuring the full-power operation of critical equipment (such as communication base stations), achieving dual control of efficient resource utilization and system overload risk.

[0064] S3. Based on ambient temperature and solar power data, adaptive compensation is performed on the power distribution strategy, and the charging mode and output power distribution ratio are dynamically adjusted to generate an optimized charging and discharging strategy.

[0065] The data processing module adaptively compensates for power distribution strategies based on changes in ambient temperature and fluctuations in solar input. For example, when solar input drops sharply, the system dynamically switches between fast and slow charging modes and adjusts the charging power threshold using a temperature compensation coefficient. The emergency power supply supports solar input (16V-150V / 15A) and fast charging (2500W). A fuzzy logic controller optimizes the charge-discharge ratio to ensure battery life is not damaged in high-temperature environments while improving energy efficiency.

[0066] S4. Control the intelligent relay to switch the on / off state of the power supply port according to the optimized charging and discharging strategy, and monitor changes in port load demand to update the power distribution strategy.

[0067] The execution module controls the intelligent relay to switch power supply ports according to the optimized charging and discharging strategy. When a sudden increase in load demand causes the power level to fall below the expansion threshold, the encrypted verification process is automatically activated to connect to an external battery pack. The emergency power supply is equipped with intelligent battery expansion interfaces (up to 10) and an IPX4 protective shell. A closed-loop verification unit ensures accurate port switching, and combined with real-time load monitoring (such as fluctuations in Type-C / USB output power), it achieves dynamic strategy updates and seamless expansion of battery life.

[0068] S5. When an abnormal battery temperature or power interruption is detected, the fault handling mechanism is activated, non-critical loads are cut off and switched to backup power, while real-time status alarms and processing logs are pushed, and user feedback is received to correct the power distribution strategy.

[0069] Upon detecting abnormal temperatures or power outages, a three-tiered fault classification mechanism is activated: non-critical loads are disconnected, backup power (such as diesel generators or energy storage battery packs) is switched on, and encrypted alarm logs are pushed through the interactive interface. The emergency power supply's EPS function (millisecond-level switching) and circuit breakers ensure continuous power supply to critical equipment; high-definition displays and cloud interfaces allow users to modify strategies in real time (such as adjusting priorities), forming a closed-loop link between fault response and strategy optimization, significantly improving the system's fault tolerance in emergency scenarios.

[0070] This application provides an emergency power supply method for an emergency power source. It generates an initial power supply parameter dataset by collecting data on remaining battery power, ambient temperature, input / output power, and solar power, and receiving a preset load priority list. Based on this dataset, a dynamic allocation algorithm is run, combining real-time power consumption and load priorities to generate a power allocation strategy that ensures continuous power supply to critical equipment and avoids system overload. Subsequently, the power allocation strategy is adaptively compensated based on changes in ambient temperature and fluctuations in solar input power, dynamically adjusting the charging / discharging mode and power allocation ratio to adapt to the environment, extend battery life, and improve energy efficiency. The optimized charging / discharging strategy controls intelligent relays to switch the on / off state of power supply ports, while continuously monitoring load changes to update the strategy. When abnormal temperature or power interruption is detected, non-critical loads are immediately disconnected and a backup power source is switched on. Alarm logs are pushed through an interactive interface, and user feedback is received, ensuring rapid response in emergency scenarios and achieving closed-loop optimization. This method, through intelligent dynamic power allocation, adaptive environmental compensation, and closed-loop fault handling, achieves efficient power supply and stable operation of the emergency power source in complex scenarios.

[0071] In this embodiment of the application, step S1 specifically includes the following steps:

[0072] S11. Collect the remaining battery power, ambient temperature, input and output power data and solar power data of the emergency power supply according to the preset frequency;

[0073] Sensor modules, including power monitoring sensors, temperature sensors, and power sensors, are deployed within the battery pack, input / output ports, and housing of the emergency power supply. These modules communicate with the central control module via a data bus. By modularly deploying multiple types of sensors, a distributed data acquisition network is constructed. Power monitoring sensors, temperature sensors, and power sensors are configured within the battery pack, input / output ports, and housing, covering the core monitoring nodes of the energy storage system. A data bus (such as CAN or RS485) enables high-speed communication between the sensors and the central control module, ensuring the integrity and interference resistance of real-time data transmission.

[0074] Initialize the sensor module, setting the power monitoring sensor to collect remaining battery power at a first frequency (e.g., once per second), the temperature sensor to collect ambient temperature at a second frequency (e.g., once every 30 seconds), and the power sensor to monitor real-time input / output power data and solar power data of each load at the input port. Differentiate the sensor sampling frequencies to balance data accuracy and system resource consumption. Power monitoring uses high-frequency sampling (1Hz) to capture rapid charging and discharging fluctuations, temperature monitoring uses low-frequency sampling (0.033Hz) to match thermal inertia characteristics, and the power sensor tracks in real-time to ensure dynamic load response.

[0075] It should be noted that the frequency optimization logic sets the sampling frequency threshold based on battery chemical characteristics (such as the slope of the voltage-capacity curve of lithium iron phosphate batteries) to avoid data redundancy caused by high-frequency sampling. Abnormal sampling suppression eliminates transient interference signals through filtering algorithms (such as moving average filtering) to ensure the stability of temperature data.

[0076] S12. Receive the load priority list input by the user through the interactive interface, verify the format and completeness of the load priority list, and generate a standardized priority configuration file.

[0077] The system receives a user-defined load priority list via an interactive interface (such as a touchscreen or host computer software). The load priority list includes device type, priority level, and minimum power requirement. A verification module validates the data format (such as JSON / XML) and logical integrity of the load priority list (e.g., no priority level conflicts, power requirement not exceeding system limits), and generates a standardized configuration file for subsequent algorithm calls.

[0078] S13. Align the battery remaining power data, ambient temperature, input and output power data, solar power data with the priority configuration file in terms of timing, and after removing outliers, integrate them into a structured data package according to the preset data template to obtain the initial power supply parameter dataset.

[0079] Multi-source heterogeneous data (such as battery power data and temperature data) is aligned using timestamps, and outliers (such as transient power spikes) are removed using a sliding window algorithm. Structured data packets are encapsulated according to preset templates (such as key-value pairs or matrix forms) for easy parsing by the central processing unit.

[0080] It should be noted that the timing alignment method is based on a hardware clock synchronization signal to ensure that the time base error of the sensor data is less than 10ms. Anomaly detection uses a dynamic threshold (e.g., power data exceeding 120% of the rated value for 3 seconds is considered abnormal), combined with historical data trend analysis (e.g., exponential smoothing) to identify invalid data.

[0081] Furthermore, data collection weights can be dynamically adjusted based on historical battery state of health (SOH) data (such as capacity decay curves) and ambient temperature. For example, in high-temperature environments, the weight coefficient of temperature data can be increased to prioritize triggering heat dissipation strategies, and the power sampling frequency can be increased during battery aging to compensate for capacity estimation errors.

[0082] It should be noted that the weight calculation model uses fuzzy logic or linear regression to establish a temperature-electricity sampling weight mapping relationship.

[0083] The SOH assessment method uses coulomb counting combined with open-circuit voltage (OCV) calibration to periodically update battery health parameters.

[0084] Adaptive trigger condition: When the ambient temperature exceeds 35℃, the weight of temperature data will be automatically increased to 0.7 (default 0.5) to strengthen the influence factor of temperature control decision.

[0085] In this embodiment of the application, step S2 specifically includes the following steps:

[0086] S21. Construct a multi-objective optimization model based on the load priority list. Determine the partition ratio of the critical load protection zone and the non-critical load floating zone through the multi-objective optimization model. The role of the multi-objective optimization model is to provide a quantitative decision basis for the division of critical and non-critical zones. When dividing the critical load protection zone and the non-critical load floating zone, the partition ratio needs to be dynamically adjusted based on the objectives optimized by the multi-objective optimization model (power supply stability, battery life, priority satisfaction rate). Specifically, the partition ratio of the critical load protection zone is the partition ratio of the critical load protection zone output by the multi-objective optimization model, and the partition ratio of the non-critical load floating zone is 1-α1.

[0087] The initial power supply parameter dataset is preprocessed by data cleaning (such as normalization and noise reduction) to transform it into a computable format. Then, a multi-objective optimization model is constructed based on the load priority level. Specifically, the multi-objective optimization model is mathematically represented as: Min{1 / Stab, -Duration, 1 / Pr}. The ε-constraint method is used to transform the multi-objective into a single objective. The solution process is divided into two stages: (1) a fixed priority satisfaction rate ≥ 95% is used as a hard constraint, and (2) the optimal solution of power supply stability and battery life is optimized using linear weighting within the feasible region.

[0088] With load priority as the core constraint, power supply stability (Stab, voltage fluctuation rate ≤5%), battery life (estimated based on energy pool / total load power) and priority satisfaction rate (Pr, the proportion of key equipment power supply compliance) are defined as optimization objectives, forming a multi-dimensional decision space.

[0089] S22. Calculate the available total energy pool based on the remaining battery power and total battery energy. Based on the partition ratio, divide the available total energy pool into critical load protection area and non-critical load floating area according to the load priority level through a dynamic allocation algorithm.

[0090] The available total energy pool E_available is calculated based on the remaining battery charge (SOC) and the total battery energy, where E_available = SOC × total battery capacity - reserved emergency energy (typically 10% of the total battery capacity). A dynamic allocation algorithm divides the available total energy pool E_available into a critical load protection zone and a non-critical load floating zone according to load priority. The critical load protection zone accounts for α1, and the non-critical load floating zone accounts for 1 - α1.

[0091] Critical load protection zone: Priority 1 device (i.e., the highest priority device), energy consumption E_critical = Σ (priority 1 device power × expected running time); where, the expected running time is the time that the load needs to be continuously powered as set by the user, and the power of priority 1 device can be adjusted according to the proportion α1 of the critical load protection zone.

[0092] Non-critical load floating zone: Remaining energy E_floating = E_available - E_critical, which is allocated to non-critical equipment as needed.

[0093] Dynamic partitioning of the energy pool: When E_critical > E_available, a degradation strategy is triggered (such as temporarily reducing the power of some priority 1 devices to the minimum power requirement).

[0094] Fault rollback mechanism: When partitioning fails, it automatically switches to full critical mode (only priority 1 devices are guaranteed).

[0095] S23. For the critical load protection area, the goal is to minimize the difference between the allocated power and the minimum power requirement of all devices, and to ensure that the minimum power requirement of the devices corresponding to the critical load protection area is fully met, and to reserve emergency redundancy power, and generate the critical load allocation result.

[0096] A linear programming model is used for the equipment in the critical load protection area. The optimal power allocation is solved using the objective function Min(Σ|P_allocated-P_demand|), where P_allocated is the allocated power and P_demand is the minimum power requirement of the equipment. The objective function aims to minimize the sum of the differences between the allocated power and the minimum power requirement of all equipment in the critical area. Constraints include: the allocated power of each equipment ≥ its minimum power requirement, the total allocated power ≤ E_critical / preset endurance threshold (where the preset endurance threshold is the minimum operating time requirement guaranteed by the system, which is a safety threshold set internally by the system, distinct from the expected operating time), and the reserved redundancy power ≥ 15% of the total critical load power. The critical load allocation result is generated, which includes the power allocation scheme {P_allocated, i} for each equipment, where i is the i-th equipment in the critical load protection area.

[0097] S24. For the non-critical load floating zone, the power adjustment range of the non-critical load floating zone is dynamically calculated by the fuzzy logic controller, taking into account the battery remaining power decay rate and the ambient temperature change rate, to generate the upper and lower limits of the power adjustment of non-critical equipment and obtain the non-critical load floating rules.

[0098] Design a fuzzy logic controller with input variables including: the rate of decay of remaining battery power (ΔSOC / Δt), the rate of change of ambient temperature (ΔT / Δt), and the percentage of remaining energy in the non-critical load floating zone (E_floating / E_available), where T is the current ambient temperature.

[0099] The output variable is the power adjustment range [P_min, P_max] of the non-critical load floating zone. The power limit (including upper and lower limits) of non-critical equipment is dynamically adjusted by a fuzzy rule base (such as "if ΔSOC / Δt is high and E_floating ratio is low, then P_max is reduced by 20%).

[0100] S25. Integrate the critical load allocation results with the non-critical load floating rules to generate an initial power allocation strategy.

[0101] The critical load allocation results are integrated with the non-critical load floating rules into a complete strategy, resulting in an initialized power allocation strategy.

[0102] The feasibility of the initial power allocation strategy under a preset scenario (defined as power outages ≤ 3 times / 24 hours) can be verified by randomly generating combinations of parameters such as load fluctuations and temperature changes using Monte Carlo simulation (at least 1000 iterations). If the simulation failure rate is > 5%, return to S22 to redistribute the available total energy pool.

[0103] If the verification passes, the verified power distribution strategy is encoded into an executable instruction set. This set includes: the on / off timing of power supply ports (e.g., relay action time points), power limit values ​​(P_min / P_max), and expansion trigger thresholds. The executable instruction set is then output to the instruction queue of the execution module, and the effective timestamp and identifier of the power distribution strategy are updated synchronously. The executable instruction set is written to the queue after being sorted by execution priority, and the timestamp ensures timing consistency for multi-device collaborative operation.

[0104] In this embodiment of the application, step S3 specifically includes the following steps:

[0105] S31. By collecting ambient temperature in real time and combining the relationship curve between historical ambient temperature and battery charging and discharging efficiency, predict the decay trend of battery charging and discharging efficiency within a future preset time period.

[0106] By collecting ambient temperature data in real time and combining it with historical ambient temperature-battery charge / discharge efficiency curves (such as the Arrhenius model), the change in battery charge / discharge efficiency η within a preset time period (such as the next 30 minutes) can be predicted. For example, when the ambient temperature rises to 45℃, a quadratic function model fitted based on historical data can be used to predict the extent of efficiency degradation caused by the increase in battery internal resistance (such as an 8% decrease in efficiency).

[0107] It should be noted that the model training method uses piecewise linear regression to fit the relationship between temperature and battery charging and discharging efficiency, dividing the data into intervals of 5°C to improve prediction accuracy.

[0108] S32. The maximum allowable power threshold of the charging mode is corrected based on the predicted degradation trend of battery charging and discharging efficiency and the temperature compensation coefficient calculated based on ambient temperature.

[0109] The dynamic deviation value ΔE between the remaining battery power and the load demand can be calculated based on the current remaining battery power and the current power allocation strategy. ΔE = Current remaining battery power - (Total load demand × Expected running time). The dynamic deviation value ΔE quantifies the gap between the current energy reserves and the expected total load demand, and is an indicator that the current power supply can meet the total demand of all loads (total load demand) over a future period (expected running time). A negative dynamic deviation value ΔE indicates a shortage, while a positive dynamic deviation value indicates a surplus.

[0110] The temperature compensation coefficient α2 is calculated based on the real-time ambient temperature T, α2 = 1 - |T - 25℃| × 0.005; the predicted battery charge and discharge efficiency η is calculated based on the predicted battery charge and discharge efficiency decay trend; the maximum allowable power threshold P_max of the charging mode is corrected based on the predicted battery charge and discharge efficiency η and the temperature compensation coefficient α2 to prevent battery overload under high / low temperature conditions, and the adjusted maximum allowable power threshold P_max_new = P_max × α2 × η.

[0111] S33. Calculate the photovoltaic power fluctuation rate based on the solar power data, and dynamically adjust the charging mode and solar charging weight ratio based on the photovoltaic power fluctuation rate.

[0112] Photovoltaic power volatility β = standard deviation of solar power (power sampling value in the last 5 minutes) / average solar power; construct a photovoltaic power volatility-charging priority mapping table. If the photovoltaic power volatility exceeds a preset threshold, dynamically switch between fast charging and slow charging modes and adjust the weight ratio of solar charging.

[0113] β < 10%: Fast charging mode is prioritized, with solar charging accounting for 80% of the weight.

[0114] 10%≤β<30%: Fast charging + slow charging hybrid mode, with solar charging accounting for 60%.

[0115] β≥30%: Only slow charging mode is enabled, solar charging accounts for 40% of the total power, and mains power replenishment is activated.

[0116] S34. A fuzzy controller is used to optimize the output power allocation ratio in the power allocation strategy based on the temperature compensation coefficient and the weight ratio of solar charging, so as to obtain the power allocation correction parameters.

[0117] Construct a fuzzy controller with two inputs and one output:

[0118] Input 1: Temperature compensation coefficient α2 (fuzzy set: low, medium, high).

[0119] Input 2: Solar charging weight ratio γ (fuzzy set: weak, medium, strong).

[0120] Output: Power allocation correction parameter δ (range -20% to +20%), used to adjust the upper limit of non-critical load power.

[0121] Example of a fuzzy rule: IF α2=high AND γ=weak THEN δ=-15% (reduce non-critical load power to prioritize heat dissipation).

[0122] S35. Input the power allocation correction parameters into the target optimization model for solution to generate the optimized charging and discharging strategy.

[0123] Input the power allocation correction parameter δ into the multi-objective optimization model and update the constraints:

[0124] Critical load power requirement: P_critical = original value × (1+δ). Wherein, the original value is the allocated power P_allocated for the critical load generated in step S23.

[0125] Charging mode switching point: When the weight ratio of solar charging γ is less than 50%, the mains charging power is allowed to be increased to 80% of the rated value.

[0126] The NSGA-II algorithm is used to solve the Pareto front of the multi-objective optimization model, and the optimal solution that balances efficiency and stability is selected. The power allocation correction parameter δ is input into the multi-objective optimization model to resolve the charging mode switching point and output power allocation ratio, generating an optimized charging and discharging strategy that includes dynamic compensation rules.

[0127] In this embodiment of the application, step S4 specifically includes:

[0128] S41. Decode the optimized charging and discharging strategy into an executable instruction set, and extract the on / off timing of the power supply port, the expansion trigger threshold, and the load monitoring cycle.

[0129] The optimized charging and discharging strategy is decoded into an executable instruction set, extracting key parameters such as the on / off timing of the power supply port, the capacity expansion trigger threshold (e.g., SOC < 30%), and the load monitoring cycle (e.g., 10 seconds / time). The executable instruction set adopts a hierarchical encoding structure, including opcodes (e.g., relay switches), parameter fields (e.g., port numbers), and timestamps, ensuring accurate parsing by the execution module.

[0130] S42. According to the on / off timing of the power supply port, control the intelligent relay drive circuit to switch the on / off state of the power supply port in priority order.

[0131] Based on the on / off timing of the power supply ports, the intelligent relay drive circuit switches the port status according to the command priority (critical load > non-critical load), and adopts a first-off-then-on timing sequence to prevent short circuits. The closed-loop verification unit collects the port current / voltage at a frequency of 1kHz, calculates the actual power in real time and compares it with the expected value of the command. If the deviation is >15%, an abnormal interrupt is triggered and reported.

[0132] S43. Monitor whether the remaining battery power is lower than the capacity expansion trigger threshold. If so, send an encrypted handshake signal to the pre-connected capacity expansion battery pack, verify the identity, activate the battery pack parallel circuit, and dynamically connect the capacity expansion battery pack to increase the total energy storage capacity.

[0133] When the remaining battery power is lower than the capacity expansion trigger threshold (configurable, default 30%), a handshake signal containing the device ID and a random number challenge is sent to the expansion battery pack. The expansion pack returns an encrypted response code (containing the device certificate and dynamic key). After successful verification, the parallel contactor is closed, and the current sharing control circuit is activated to achieve seamless grid connection.

[0134] S44. Collect power data of each port load based on the load monitoring cycle, and calculate the power offset based on the power data of the port load and the expected power in the power distribution strategy.

[0135] The load power is collected according to the load monitoring cycle, and the power offset ΔP is calculated as: actual power - expected power.

[0136] S45. When the absolute value of the power offset exceeds the preset offset threshold multiple times consecutively, a strategy update signal is generated, and the power distribution strategy is updated according to the power offset and the access status of the expanded battery pack.

[0137] A policy update signal is generated when the absolute value of ΔP exceeds a preset offset threshold (e.g., nominal 20%) three consecutive times. Offset polarity. Decide on the direction of adjustment (increase / decrease non-critical load power).

[0138] It should be noted that the offset threshold can be dynamically adjusted based on the remaining battery power (the lower the battery level, the stricter the threshold). Anti-accidental touch mechanism: Offset triggering requires a power change rate > 5% / second to avoid interference from gradual load changes.

[0139] The power offset and the access status of the expanded battery pack (such as the added capacity value) are pushed to the data processing module via a message queue. This triggers the dynamic allocation algorithm to regenerate the updated power allocation strategy with the latest parameters, overwriting the original version. The updated power allocation strategy is identified by its version number (e.g., V2.1.5) to ensure atomic switching at the execution layer.

[0140] In this embodiment of the application, step S5 specifically includes the following steps:

[0141] S51: Real-time monitoring of battery temperature data and power supply port status. When the battery temperature exceeds the safety threshold or the main power input is interrupted, a three-level fault classification mechanism is triggered, and an emergency response instruction set is generated according to the fault type and severity.

[0142] Construct a three-level fault classification mechanism:

[0143] Level 1 fault: Battery temperature exceeds limit (e.g., >55℃) or single-port short circuit triggers partial load disconnection.

[0144] Level 2 fault: Main power input interruption and energy storage capacity <20%, initiate backup power pre-switching.

[0145] Level 3 fault: Temperature over-limit combined with input interruption activates the full system protection mode.

[0146] Differentiated emergency instruction sets are generated by encoding fault types and assigning severity weights (levels 1-3).

[0147] S52. Based on the emergency response instruction set, prioritize cutting off the intelligent relays of non-critical load power supply ports and start the backup power switching module to dynamically select a diesel generator or energy storage battery pack as a temporary power supply according to the availability weight of the backup power.

[0148] The availability weight of the backup power supply is obtained by weighting and summing the remaining capacity and response speed of the backup power supply using preset weighting coefficients; the backup power supply is then selected based on its availability weight. The availability weight of the backup power supply is W = 0.6 × remaining battery capacity of the backup power supply + 0.4 × response speed of the backup power supply (diesel engine > battery pack).

[0149] Dynamic switching logic: If the availability weight W of the diesel engine is greater than 0.8 and the start-up time is less than 30 seconds, the diesel engine will be used first; otherwise, the energy storage battery pack will be switched to the diesel engine.

[0150] Non-critical load disconnection adopts a cascading release strategy, disconnecting in batches from low to high priority.

[0151] S53. Push fault type, handling measures and current load power supply status in real time through interactive interface, generate encrypted processing log and synchronize to cloud server, and receive policy correction instructions or priority adjustment requests input by user.

[0152] The content pushed through the interactive interface includes:

[0153] Fault visualization: temperature heat map, load power supply status matrix (red / yellow / green three-color label).

[0154] Encrypted Logs: The log stream is encrypted using the TLS 1.3 protocol, and the fields include timestamp and event code.

[0155] User commands are verified through a whitelist mechanism, allowing modification of non-critical load priorities (±1 levels) and preset battery life thresholds.

[0156] S54. Inject user correction instructions and processing logs into the dynamic allocation algorithm, recalculate the load priority and backup power switching rules in the power allocation strategy, update the power allocation strategy version, and trigger the execution module to take effect in real time.

[0157] User-mandated correction commands update algorithm parameters via the policy injection interface: priority remapping, with modified load priorities updated in real-time to the S13 configuration file. Backup rules are optimized by training a decision tree model based on historical fault data to improve the accuracy of backup power selection. Policy version numbers use semantic encoding (major version.minor version.revision number), and hot updates are supported only when the major version is consistent.

[0158] Please refer to Figure 2 , Figure 3 and Figure 4 This application embodiment also provides an emergency power supply, which is controlled using the emergency power supply method described in the foregoing method embodiments. The emergency power supply includes:

[0159] The sensor module 210 includes a power monitoring sensor, an ambient temperature sensor, and a power sensor deployed on the battery pack and at the input / output ports.

[0160] Data processing module 220 integrates a multi-core processor and a dynamic allocation algorithm unit, used to generate and optimize power allocation strategies based on data collected by the sensor module and load priority list;

[0161] The execution module 230 includes a smart relay and a load monitoring circuit, which executes a charging and discharging strategy to switch the on / off state of the power supply port and monitors changes in port load demand.

[0162] The interactive module 240 is equipped with a high-definition touch screen 40 and a cloud communication interface.

[0163] In the embodiments of this application, combined with Figure 4 As shown, the emergency power supply also includes a housing assembly 10, and a battery module is disposed inside the housing assembly 10;

[0164] A power output module 21 and an expansion interface module 22 are provided on the outer side wall of the housing assembly 10, and the interaction module is integrated on one side of the expansion interface module 22.

[0165] The housing assembly 10 is equipped with a movable wheel 30 and a drive assembly for driving the movable wheel 30 to move.

[0166] This application also provides an electronic device, which includes a processor and a memory;

[0167] The memory is used to store program code and transfer the program code to the processor;

[0168] The processor is used to execute the emergency power supply method of the emergency power supply in the foregoing method embodiments according to the instructions in the program code.

[0169] This application also provides a computer-readable storage medium for storing program code, which, when executed by a processor, implements the emergency power supply method of the emergency power supply in the aforementioned method embodiments.

[0170] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0171] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0172] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0174] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0176] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0177] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An emergency power supply method for an emergency power source, characterized in that, include: Collect data on the remaining battery power, ambient temperature, input and output power, and solar power of the emergency power supply, and receive a preset load priority list to generate an initial power supply parameter dataset. Based on the initial power supply parameter dataset, a power allocation strategy is generated through a dynamic allocation algorithm, including: A multi-objective optimization model is constructed based on the load priority list, and the partition ratio of the critical load protection zone and the non-critical load floating zone is determined through the multi-objective optimization model. The available total energy pool is calculated based on the remaining battery power and total battery energy. Based on the partition ratio, the available total energy pool is divided into a critical load protection zone and a non-critical load floating zone according to the load priority level through a dynamic allocation algorithm. For the critical load protection zone, the goal is to minimize the sum of the differences between the allocated power and the minimum power requirement of all devices, and to ensure that the minimum power requirement of the devices corresponding to the critical load protection zone is fully met, and to reserve emergency redundancy power, thereby generating the critical load allocation result. For the non-critical load floating zone, the power adjustment range of the non-critical load floating zone is dynamically calculated by a fuzzy logic controller, taking into account the battery remaining power decay rate and the ambient temperature change rate. This generates the upper and lower limits of the power adjustment of non-critical equipment and obtains the non-critical load floating rules. The critical load allocation results are fused with the non-critical load floating rules to generate an initial power allocation strategy; Based on the ambient temperature and the solar power data, the power distribution strategy is adaptively compensated, and the charging mode and output power distribution ratio are dynamically adjusted to generate an optimized charging and discharging strategy. The intelligent relay is controlled to switch the on / off state of the power supply port according to the optimized charging and discharging strategy, and the port load demand changes are monitored to update the power distribution strategy. When an abnormal battery temperature or power interruption is detected, the fault handling mechanism is activated, non-critical loads are disconnected and switched to backup power, real-time status alarms and processing logs are pushed, and user feedback is received to correct the power distribution strategy.

2. The emergency power supply method according to claim 1, characterized in that, The system collects data on the remaining battery power, ambient temperature, input / output power, and solar power of the emergency power supply, and receives a preset load priority list to generate an initial power supply parameter dataset, including: Collect emergency power supply battery remaining power, ambient temperature, input and output power data and solar power data at a preset frequency; The system receives a list of load priorities input by the user through an interactive interface, verifies the format and completeness of the list, and generates a standardized priority configuration file. The remaining battery power data, ambient temperature, input / output power data, and solar power data are time-aligned with the priority configuration file, and after removing outliers, they are integrated into a structured data package according to a preset data template to obtain the initial power supply parameter dataset.

3. The emergency power supply method according to claim 1, characterized in that, The process of fusing the critical load allocation results with the non-critical load floating rules to generate an initial power allocation strategy further includes: The feasibility of the initial power allocation strategy under the target scenario is verified through Monte Carlo simulation. If the simulation fails, the process is returned to redistribute the total available energy pool.

4. The emergency power supply method according to claim 1, characterized in that, The step of adaptively compensating the power distribution strategy based on the ambient temperature and the solar power data, dynamically adjusting the charging mode and output power distribution ratio, and generating an optimized charging and discharging strategy includes: By collecting the ambient temperature in real time and combining it with the relationship curve between historical ambient temperature and battery charging and discharging efficiency, the decay trend of battery charging and discharging efficiency within a future preset time period is predicted. The maximum allowable power threshold of the charging mode is corrected based on the predicted battery charging and discharging efficiency decay trend and the temperature compensation coefficient calculated based on the ambient temperature. Calculate the photovoltaic power fluctuation rate based on the solar power data, and dynamically adjust the charging mode and solar charging weight ratio based on the photovoltaic power fluctuation rate. A fuzzy controller is used to optimize the output power allocation ratio in the power allocation strategy based on the temperature compensation coefficient and the solar charging weight ratio, so as to obtain the power allocation correction parameters. The power allocation correction parameters are input into the target optimization model for solution, generating an optimized charging and discharging strategy.

5. The emergency power supply method according to claim 1, characterized in that, The control of the intelligent relay to switch the on / off state of the power supply port according to the optimized charging and discharging strategy, and to monitor changes in port load demand to update the power distribution strategy, includes: The optimized charging and discharging strategy is decoded into an executable instruction set, and the on / off timing, expansion trigger threshold, and load monitoring cycle of the power supply port are extracted. According to the on / off timing of the power supply port, the intelligent relay drive circuit is controlled to switch the on / off state of the power supply port in priority order. If the remaining battery power is lower than the capacity expansion trigger threshold, an encrypted handshake signal is sent to the pre-connected capacity expansion battery pack. After verifying the identity, the battery pack parallel circuit is activated, and the capacity expansion battery pack is dynamically connected to increase the total energy storage capacity. The power data of each port load is collected based on the load monitoring cycle, and the power offset is calculated based on the power data of the port load and the expected power in the power distribution strategy. If the absolute value of the power offset exceeds the preset offset threshold multiple times consecutively, a strategy update signal is generated, and the power distribution strategy is updated according to the power offset and the access status of the expanded battery pack.

6. The emergency power supply method according to claim 1, characterized in that, When an abnormal battery temperature or power interruption is detected, a fault handling mechanism is activated, cutting off non-critical loads and switching to backup power. Simultaneously, real-time status alarms and processing logs are pushed, and user feedback is received to correct the power distribution strategy, including: When an abnormal battery temperature or power interruption is detected, a three-level fault classification mechanism is triggered, generating an emergency response instruction set based on the fault type and severity. Based on the emergency response instruction set, the intelligent relays of non-critical load power supply ports are disconnected first, and diesel generators or energy storage battery packs are selected as backup power sources according to the availability weight of backup power. The system pushes fault types, handling measures, and current load power supply status in real time through an interactive interface, generates encrypted processing logs and synchronizes them to the cloud server, and simultaneously receives policy correction instructions or priority adjustment requests from users to update the power distribution policy.

7. The emergency power supply method according to claim 6, characterized in that, The method for calculating the availability weight of the backup power supply includes: The availability weight of the backup power supply is obtained by weighting and summing the remaining capacity and response speed of the backup power supply using preset weighting coefficients.

8. An emergency power supply, characterized in that, The emergency power supply method according to any one of claims 1 to 7 is used to achieve regulation, wherein the emergency power supply comprises: The sensor module includes a power monitoring sensor, an ambient temperature sensor, and a power sensor deployed in the battery pack's input / output ports; The data processing module integrates a multi-core processor and a dynamic allocation algorithm unit, and is used to generate and optimize power allocation strategies based on the data collected by the sensor module and the load priority list. The execution module, including intelligent relays and load monitoring circuits, is used to execute charging and discharging strategies to switch the on / off state of the power supply port and monitor changes in port load demand. The interactive module is equipped with a high-definition touch screen and a cloud communication interface.

9. The emergency power supply according to claim 8, characterized in that, The emergency power supply also includes a housing assembly, and a battery module is disposed inside the housing assembly; The outer wall of the housing assembly is provided with a power output module and an expansion interface module, and the interaction module is integrated on one side of the expansion interface module. The housing assembly is equipped with casters and a drive assembly for moving the casters.

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