Emergency power supply and emergency power supply method thereof

Abstract

The invention belongs to the technical field of power supply, and discloses an emergency power supply and an emergency power supply method thereof, and the method comprises the steps: collecting the battery remaining capacity, environment temperature, input and output power data and solar power data of the emergency power supply, receiving a load priority list, generating an initial power supply parameter data set, and storing the initial power supply parameter data set; generating a power distribution strategy through a dynamic distribution algorithm; self-adaptive compensation is carried out on the distribution strategy according to the environment temperature and the solar power data, and an optimized charging and discharging strategy is generated; switching the on-off state of the port according to the optimized strategy, and monitoring the load demand change of the port to update the strategy; and when it is detected that the battery temperature is abnormal or power supply is interrupted, starting a fault processing mechanism, cutting off a non-critical load, switching to a standby power supply, pushing a real-time state alarm and a processing log, and receiving user feedback to correct a strategy. According to the invention, through dynamic power distribution, environment compensation and closed-loop fault processing, efficient power supply and stable operation of the emergency power supply in a complex scene are realized.

Description

Technical Field

[0001] The present application relates to the field of power supply technology, and in particular to an emergency power supply and an emergency power supply method thereof. Background Art

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

[0003] Existing emergency power supply technologies often use fixed priority allocation or static charging and discharging strategies, such as allocating power based on preset load levels or relying on a single charging mode (such as mains / solar power) to replenish power. 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 distribution and charging and discharging optimization mechanism prevents the system from dynamically adjusting its strategy based on battery status, changes in load demand, and fluctuations in external energy input. This can easily lead to power outages for critical equipment, battery overloads, or low energy utilization. For example, when load demand suddenly increases or solar input plummets, traditional methods struggle to reallocate power or switch charging modes in a timely manner, resulting in reduced power supply stability and difficulty meeting the stringent requirements of complex emergency scenarios.

[0004] In view of this, there is a need to address the technical problem in the existing technology of unstable power resource allocation in emergency scenarios such as dynamic load fluctuations, changing environmental conditions and sudden failures. Summary of the Invention

[0005] In order to solve the above problems, the present application provides an emergency power supply and an emergency power supply method thereof.

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

[0007] Collect the remaining battery power, ambient temperature, input and output power data, and solar power data of the emergency power supply, receive the preset load priority list, and generate the initial power supply parameter data set;

[0008] Based on the initial power supply parameter data set, generating a power allocation strategy through a dynamic allocation algorithm;

[0009] Adaptively compensate the power distribution strategy according to the ambient temperature and the solar power data, dynamically adjust the charging mode and the output power distribution ratio, and generate an optimized charging and discharging strategy;

[0010] Controlling the intelligent relay to switch the on / off state of the power supply port according to the optimized charge and discharge strategy, and monitoring the changes in the port load demand to update the power distribution strategy;

[0011] When abnormal battery temperature or power interruption is detected, the fault handling mechanism is activated to cut off non-critical loads and switch to backup power supply. At the same time, real-time status alarms and processing logs are pushed, and user feedback is received to modify the power distribution strategy.

[0012] Optionally, the method collects the remaining battery power, ambient temperature, input and output power data, and solar power data of the emergency power supply, receives a preset load priority list, and generates an initial power supply parameter data set, including:

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

[0014] Receive a load priority list input by a user through an interactive interface, verify the format and integrity of the load priority list, and generate a standardized priority configuration file;

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

[0016] Optionally, generating a power allocation strategy by a dynamic allocation algorithm based on the initial power supply parameter data set includes:

[0017] Building a multi-objective optimization model based on the load priority list, and determining the partition ratio of the critical load guarantee area and the non-critical load floating area through the multi-objective optimization model;

[0018] Calculating an available total energy pool according to the remaining battery power and the total battery energy, and dividing the available total energy pool into a critical load guarantee zone and a non-critical load floating zone according to load priority levels through a dynamic allocation algorithm based on the partition ratio;

[0019] For the critical load protection area, the goal is to minimize the sum of the differences between the allocated power and the minimum power requirements of all devices, ensure that the minimum power requirements of the devices corresponding to the critical load protection area are fully met, and reserve emergency redundant power, and generate a critical load allocation result;

[0020] For the non-critical load floating area, the power adjustment range of the non-critical load floating area is dynamically calculated by a fuzzy logic controller in combination with the battery remaining power attenuation rate and the ambient temperature change rate, and the upper and lower limits of the power adjustment of the non-critical equipment are generated to obtain the non-critical load floating rule;

[0021] The critical load distribution result is integrated with the non-critical load floating rule to generate an initial power distribution strategy.

[0022] Optionally, the critical load allocation result is integrated with the non-critical load floating rule to generate an initial power allocation strategy, and then further includes:

[0023] The feasibility of the initial power allocation strategy in the target scenario is verified through Monte Carlo simulation. If the simulation fails, the available total energy pool is redivided.

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

[0025] The real-time collected ambient temperature is combined with a relationship curve between historical ambient temperature and battery charge and discharge efficiency to predict the attenuation trend of battery charge and discharge efficiency within a preset time period in the future;

[0026] Correcting a maximum allowable power threshold of a charging mode based on the predicted attenuation trend of the battery charge and discharge efficiency and a temperature compensation coefficient calculated based on the ambient temperature;

[0027] Calculating the photovoltaic power fluctuation rate according to the solar power data, and dynamically adjusting the charging mode and the solar charging weight ratio according to the photovoltaic power fluctuation rate;

[0028] A fuzzy controller is used to optimize the output power distribution ratio in the power distribution strategy according to the temperature compensation coefficient and the solar charging weight ratio to obtain a power distribution correction parameter;

[0029] The power distribution correction parameters are input into the target optimization model for solution to generate an optimized charge and discharge strategy.

[0030] Optionally, controlling the intelligent relay to switch the on / off state of the power supply port according to the optimized charge and discharge strategy, and monitoring changes in port load demand to update the power distribution strategy, includes:

[0031] Decoding the optimized charge and discharge strategy into an executable instruction set, and extracting the on / off timing, expansion trigger threshold, and load monitoring period of the power supply port;

[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 order of priority;

[0033] Monitor whether the remaining battery power is lower than the expansion trigger threshold. If so, send an encrypted handshake signal to the pre-connected expansion battery pack, activate the battery pack parallel circuit after identity verification, and dynamically connect the expansion battery pack to increase the total energy storage capacity;

[0034] collecting power data of each port load based on the load monitoring period, and calculating a power offset according to the power data of the port load and the expected power in the power distribution strategy;

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

[0036] Optionally, when abnormal battery temperature or power interruption is detected, a fault handling mechanism is initiated to disconnect non-critical loads and switch to a backup power source, while simultaneously sending real-time status alarms and processing logs, and receiving user feedback to modify the power allocation strategy, including:

[0037] When 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 the power supply ports of non-critical loads are preferentially cut off, and a diesel generator or an energy storage battery pack is selected as a backup power source according to the availability weight of the backup power source;

[0039] The fault type, treatment measures and current load power supply status are pushed in real time through the interactive interface, an encrypted processing log is generated and synchronized to the cloud server, and at the same time, the policy modification instructions or priority adjustment requests input by the user are received to update the power distribution strategy.

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

[0041] The remaining capacity and response speed of the backup power supply are weighted and summed by a preset weight coefficient to obtain the availability weight of the backup power supply.

[0042] A second aspect of the present application provides an emergency power supply, which is controlled by using the emergency power supply method of any one of the first aspects. The emergency power supply includes:

[0043] Sensor modules, including power monitoring sensors deployed in the battery pack, ambient temperature sensors, and power sensors at input and output ports;

[0044] a data processing module integrating a multi-core processor and a dynamic allocation algorithm unit, for generating and optimizing a power allocation strategy based on the data collected by the sensor module and a load priority list;

[0045] The execution module includes an intelligent relay and a load monitoring circuit, which is used to execute the charge and discharge strategy to switch the on and off status of the power supply port and monitor the changes in the 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 further includes a housing assembly, wherein a battery module is disposed inside the housing assembly;

[0048] A power output module and a capacity expansion interface module are provided on the outer side wall of the housing assembly, and the interaction module is integrated on one side of the capacity expansion interface module;

[0049] The housing assembly is provided with a moving wheel and a driving assembly for driving the moving wheel to move.

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

[0051] The present application provides an emergency power supply method for an emergency power supply, which generates an initial power supply parameter data set by collecting battery remaining power, ambient temperature, input and output power data, and solar power data, and receiving a preset load priority list; runs a dynamic allocation algorithm based on the data set, and generates a power distribution strategy in combination with real-time power and load priority to ensure continuous power supply to key equipment and avoid system overload; then, according to changes in ambient temperature and fluctuations in solar input power, adaptively compensates the power distribution strategy, and dynamically adjusts the charging and discharging mode and power distribution ratio to adapt to the environment, extend battery life, and improve energy utilization; controls the intelligent relay to switch the on and off status of the power supply port according to the optimized charging and discharging strategy, and continuously monitors load changes to update the strategy; when a temperature abnormality or power interruption is detected, immediately cuts off non-critical loads and switches to the backup power supply, pushes alarm logs through the interactive interface, and receives user feedback to ensure rapid response in emergency scenarios and achieve closed-loop optimization; this method achieves efficient power supply and stable operation of emergency power supplies in complex scenarios through intelligent dynamic power distribution, adaptive environmental compensation, and closed-loop fault processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A schematic flow chart of an emergency power supply method for an emergency power supply provided in an embodiment of the present application;

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

[0055] Figure 3 Another structural schematic diagram of an emergency power supply provided in an embodiment of the present application;

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

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

[0058] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0059] For easier understanding, please refer to Figure 1 , an embodiment of the present application provides an emergency power supply method of an emergency power supply, comprising:

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

[0061] Sensor modules installed in the emergency power supply collect real-time data on remaining battery charge, ambient temperature, input and output power, and solar power. These data, combined with a preset load priority list, generate an initial power supply parameter dataset. This step provides the data foundation for subsequent strategies: the battery's remaining charge (SOC) reflects the current energy storage state, ambient temperature is used to assess the risk of battery charge and discharge efficiency degradation, and input and output power data quantifies energy input and load demand. Combined with the emergency power supply's high-precision sensors (such as power monitoring sensors and industrial waterproof sockets), this ensures real-time and reliable data collection, laying the core input conditions for dynamic power distribution.

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

[0063] Based on the initial data set, a dynamic allocation algorithm is run through the data processing module, combining real-time battery power and load priority to generate a power allocation strategy. This step uses a multi-objective optimization model (such as power supply stability and battery life) to divide 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 enable the algorithm to ensure full power operation of critical equipment (such as communication base stations) while flexibly adjusting the power limit of non-critical loads (such as lighting equipment), achieving dual management of efficient resource utilization and system overload risks.

[0064] S3. Adaptively compensate the power distribution strategy based on ambient temperature and solar power data, dynamically adjust the charging mode and output power distribution ratio, and generate an optimized charging and discharging strategy;

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

[0066] S4, controlling the intelligent relay to switch the on / off state of the power supply port according to the optimized charge and discharge strategy, and monitoring the changes in the 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 charge and discharge strategy. When a sudden increase in load demand causes the battery level to fall below the expansion threshold, an encrypted verification process is automatically activated to connect an external battery pack. The emergency power supply is equipped with an intelligent battery expansion port (up to 10) and an IPX4 protective housing. A closed-loop verification unit ensures accurate port switching. Combined with real-time load monitoring (such as Type-C / USB output power fluctuations), it enables dynamic policy updates and seamless battery life expansion.

[0068] S5. When abnormal battery temperature or power outage is detected, the fault handling mechanism is activated to disconnect non-critical loads and switch to the backup power source. At the same time, real-time status alarms and processing logs are pushed, and user feedback is received to modify the power distribution strategy.

[0069] When a temperature anomaly or power outage is detected, a three-level fault classification mechanism is activated: non-critical loads are disconnected, backup power sources (such as diesel generators or energy storage batteries) are switched, 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. The high-definition display and cloud interface allow users to modify policies in real time (such as adjusting priorities), forming a closed-loop link between fault response and policy optimization, significantly improving the system's fault tolerance in emergency scenarios.

[0070] The present application provides an emergency power supply method for an emergency power supply, which generates an initial power supply parameter data set by collecting battery remaining power, ambient temperature, input and output power data, and solar power data, and receiving a preset load priority list; runs a dynamic allocation algorithm based on the data set, and generates a power distribution strategy in combination with real-time power and load priority to ensure continuous power supply to key equipment and avoid system overload; then, according to changes in ambient temperature and fluctuations in solar input power, adaptively compensates the power distribution strategy, and dynamically adjusts the charging and discharging mode and power distribution ratio to adapt to the environment, extend battery life, and improve energy utilization; controls the intelligent relay to switch the on and off status of the power supply port according to the optimized charging and discharging strategy, and continuously monitors load changes to update the strategy; when a temperature abnormality or power interruption is detected, immediately cuts off non-critical loads and switches to the backup power supply, pushes alarm logs through the interactive interface, and receives user feedback to ensure rapid response in emergency scenarios and achieve closed-loop optimization; this method achieves efficient power supply and stable operation of emergency power supplies in complex scenarios through intelligent dynamic power distribution, adaptive environmental compensation, and closed-loop fault processing.

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

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

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

[0074] Initialize the sensor module, setting the battery 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 and output power data and solar power data for each load at the input port. Differentiate the sensor sampling frequencies to balance data accuracy with system resource consumption. Battery monitoring uses high-frequency sampling (1Hz) to capture rapid charging and discharging fluctuations, while temperature monitoring uses low-frequency sampling (0.033Hz) to match thermal inertia characteristics. The power sensor tracks loads in real time to ensure dynamic response.

[0075] It's important to note that frequency optimization logic sets sampling frequency thresholds based on battery chemistry (such as the slope of the voltage-capacity curve for 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 stable temperature data.

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

[0077] The system receives a user-defined load priority list through an interactive interface (such as a touch screen or host software). The load priority list includes device type, priority level, and minimum power requirement. A validation module verifies the data format (such as JSON / XML) and logical integrity of the load priority list (e.g., no conflicting priorities, and power requirements within system limits). The system then generates a standardized configuration file for subsequent algorithm calls.

[0078] S13, aligning the battery remaining power data, ambient temperature, input and output power data, and solar power data with the priority configuration file in time series, and after removing outliers, integrating them into a structured data packet according to a preset data template to obtain an initial power supply parameter data set.

[0079] Multi-source heterogeneous data (such as battery charge and temperature data) is aligned using timestamps, and a sliding window algorithm is used to remove outliers (such as transient power spikes). Structured data packets are packaged according to preset templates (such as key-value pairs or matrices) to facilitate rapid parsing by the central processor.

[0080] It should be noted that the timing alignment method uses hardware clock synchronization signals to ensure that the time base error of sensor data is less than 10ms. Dynamic thresholds are set for outlier determination (for example, power data exceeding 120% of the rated value for three seconds is considered an anomaly). Historical data trend analysis (such as exponential smoothing) is combined with historical data trend analysis 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 cooling strategies, while the power sampling frequency can be increased to compensate for capacity estimation errors as the battery ages.

[0082] It should be noted that the weight calculation model uses fuzzy logic or linear regression model 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 regularly update battery health parameters.

[0084] Adaptive trigger conditions: When the ambient temperature exceeds 35°C, the temperature data weight is automatically increased to 0.7 (default 0.5), strengthening the influencing factor of temperature control decision-making.

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

[0086] S21. Construct a multi-objective optimization model based on the load priority list and use it to determine the partition ratios between the critical load guarantee area and the non-critical load floating area. The multi-objective optimization model provides a quantitative decision-making basis for the division between critical and non-critical areas. When dividing the critical load guarantee area and the non-critical load floating area, the partition ratios must be dynamically adjusted based on the objectives optimized by the multi-objective optimization model (power supply stability, battery life, and priority satisfaction rate). Specifically, the multi-objective optimization model outputs a weight coefficient α1, with the partition ratio of the critical load guarantee area equal to the weight coefficient α1 output by the multi-objective optimization model, and the partition ratio of the non-critical load floating area equal to 1-α1.

[0087] The initial power supply parameter dataset is preprocessed and converted into a computable format through data cleaning (e.g., normalization and denoising). A multi-objective optimization model is then constructed based on the load priority levels. Specifically, the multi-objective optimization model is mathematically represented as: Min{1 / Stab, -Duration, 1 / Pr}. The ε-constraint method is used to transform multiple objectives into a single objective. The solution is divided into two stages: (1) a fixed priority satisfaction rate of ≥95% is used as a hard constraint, and (2) a linear weighted optimization is used within the feasible domain to find the optimal solution for power supply stability and battery life.

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

[0089] S22. Calculate the available total energy pool based on the remaining battery power and the total battery energy, and divide the available total energy pool into a critical load guarantee zone and a non-critical load floating zone according to load priority levels using a dynamic allocation algorithm based on the partition ratio;

[0090] The available total energy pool E_available is calculated based on the remaining battery charge (SOC) and the total battery energy. E_available = SOC × total battery capacity - reserved emergency energy (usually 10% of the total battery capacity). The available total energy pool E_available is divided into a critical load guarantee area and a non-critical load floating area based on load priority through a dynamic allocation algorithm. The critical load guarantee area accounts for α1, and the non-critical load floating area accounts for 1-α1:

[0091] Critical load protection zone: Priority 1 devices (i.e., the highest priority devices) consume energy E_critical = Σ(priority 1 device power × expected run time). The expected run time is the user-defined duration for the load to require continuous power supply. The power of the priority 1 device can be adjusted based on the critical load protection zone percentage α1.

[0092] Non-critical load floating area: The remaining energy E_floating = E_available - E_critical, which is allocated to non-critical equipment on demand.

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

[0094] Failure fallback mechanism: Automatically switches to full-criticality mode (protecting only priority 1 devices) when partitioning fails.

[0095] S23. For the critical load protection area, aim to minimize the difference between the allocated power of all devices and the minimum power requirement, ensure that the minimum power requirements of the devices corresponding to the critical load protection area are fully met, and reserve emergency redundant power, and generate a critical load allocation result;

[0096] A linear programming model is used to determine the optimal power allocation for devices in the critical load support zone, using the objective function Min(Σ|P_allocated - P_demand|), where P_allocated represents the allocated power and P_demand represents the device's minimum power requirement. The objective function aims to minimize the sum of the differences between the allocated power and the minimum power demand for all devices in the critical zone. Constraints include: each device's allocated power ≥ its minimum power demand, the total allocated power ≤ E_critical / preset endurance threshold (where the preset endurance threshold is the minimum guaranteed runtime requirement for the system and is a safety threshold set internally, distinct from the expected runtime), and reserved redundant power ≥ 15% of the total critical load power. The resulting critical load allocation solution includes the power allocation plan {P_allocated, i} for each device, where i represents the i-th device in the critical load support zone.

[0097] S24. For the non-critical load floating area, dynamically calculate the power adjustment range of the non-critical load floating area by using a fuzzy logic controller in combination with the battery remaining power attenuation rate and the ambient temperature change rate, generate the upper and lower limits of the power adjustment of the non-critical equipment, and obtain the non-critical load floating rule;

[0098] A fuzzy logic controller is designed. Its input variables include the battery remaining charge decay rate (ΔSOC / Δt), the ambient temperature change rate (ΔT / Δt), and the remaining energy ratio in the non-critical load floating area (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 floating zone of non-critical loads. The power limit (including upper and lower limits) of non-critical equipment is dynamically adjusted through the fuzzy rule base (for example, "If ΔSOC / Δt is high and the E_floating ratio is low, then P_max is reduced by 20%").

[0100] S25. Fusion the critical load allocation result with the non-critical load floating rule to generate an initial power distribution strategy.

[0101] The critical load allocation results and non-critical load floating rules are integrated into a complete strategy to obtain the initialized power distribution strategy.

[0102] Monte Carlo simulations can be used to randomly generate parameter combinations such as load fluctuations and temperature changes (for at least 1000 iterations) to verify the feasibility of the initialized power allocation strategy under a preset scenario (defined as power outages ≤ 3 times / 24 hours). If the simulation failure rate is greater than 5%, the process returns to S22 to repartition the available total energy pool.

[0103] If verification passes, the verified power distribution strategy is encoded as an executable instruction set. This includes the power port on / off timing (e.g., relay actuation timing), power limit values ​​(P_min / P_max), and expansion trigger thresholds. This executable instruction set is then output to the execution module's instruction queue, and the power distribution strategy's effective timestamp and flag are simultaneously updated. Executable instructions are sorted by execution priority and written to the queue. Timestamps ensure timing consistency for multi-device collaborative operations.

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

[0105] S31. Predicting the attenuation trend of the battery charge and discharge efficiency within a preset time period in the future by using the real-time collected ambient temperature and a relationship curve between the historical ambient temperature and the battery charge and discharge efficiency;

[0106] By combining real-time ambient temperature data with historical ambient temperature-battery charge-discharge efficiency curves (such as the Arrhenius model), the system predicts changes in battery charge-discharge efficiency η over a preset time period (e.g., the next 30 minutes). For example, when the ambient temperature rises to 45°C, the quadratic function model fitted to historical data can predict the efficiency degradation caused by increased battery internal resistance (e.g., an 8% drop in efficiency).

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

[0108] S32, correcting the maximum allowable power threshold of the charging mode based on the predicted attenuation trend of the battery charge and discharge efficiency and the temperature compensation coefficient calculated based on the ambient temperature;

[0109] The dynamic deviation ΔE between the remaining battery capacity and the load demand can be calculated based on the current remaining battery capacity and the current power distribution strategy. ΔE = current remaining capacity - (total load power demand × expected run time). This dynamic deviation ΔE quantifies the gap between the current energy reserve and the expected total load demand. It is an indicator of whether the current power reserve can meet the total demand of all loads (total load power demand) over a period of time (expected run time). A negative dynamic deviation ΔE indicates a shortage, while a positive dynamic deviation ΔE indicates a surplus.

[0110] The temperature compensation coefficient α2 is calculated based on the real-time collected ambient temperature T, where α2 = 1-|T-25°C| × 0.005. The predicted battery charge and discharge efficiency η is calculated based on the predicted attenuation trend of the battery charge and discharge efficiency. 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 at high / low temperatures. 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] PV power fluctuation rate β = standard deviation of solar power (power sampling value in the last 5 minutes) / average solar power; construct a PV power fluctuation rate-charging priority mapping table. If the PV power fluctuation rate exceeds the preset threshold, the fast charging and slow charging modes are dynamically switched, and the solar charging weight is adjusted:

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

[0114] 10%≤β<30%: Fast charging + slow charging mixed mode, the solar charging weight accounts for 60%.

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

[0116] S34, using a fuzzy controller to optimize the output power distribution ratio in the power distribution strategy according to the temperature compensation coefficient and the solar charging weight ratio, and obtain a power distribution correction parameter;

[0117] Construct a dual-input single-output fuzzy controller:

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

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

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

[0121] Fuzzy rule example: IF α2 = HIGH AND γ = WEAK THEN δ = -15% (reduce power to non-critical loads to prioritize heat dissipation).

[0122] S35. Input the power allocation correction parameters into the target optimization model for solution to generate an optimized charge and discharge 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), where the original value is the allocated power P_allocated of the critical load generated in step S23.

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

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

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

[0128] S41, decoding the optimized charge and discharge strategy into an executable instruction set, and extracting the on-off timing, expansion trigger threshold, and load monitoring period of the power supply port;

[0129] The optimized charge and discharge strategy is decoded into an executable instruction set, extracting key parameters such as the power port's on / off timing, expansion trigger threshold (e.g., SOC < 30%), and load monitoring cycle (e.g., 10 seconds / time). This executable instruction set uses a hierarchical encoding structure, including operation codes (e.g., relay on / off), parameter fields (e.g., port number), and timestamps, ensuring accurate parsing of the execution module.

[0130] S42, according to the on-off timing of the power supply port, controlling the intelligent relay drive circuit to switch the on-off state of the power supply port in order of priority;

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

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

[0133] When the remaining battery charge falls below the 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 (including the device certificate and dynamic key). Upon successful verification, the parallel contactor is closed and the current sharing control circuit is activated for seamless grid connection.

[0134] S44. Collect power data of each port load based on the load monitoring period, and calculate the power offset according to 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 period, 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 value for multiple consecutive times, a strategy update signal is generated to update the power distribution strategy according to the power offset and the access status of the expansion battery pack.

[0137] When the absolute value of ΔP exceeds the preset offset threshold (such as 20% of the nominal value) for three consecutive times, a strategy update signal is generated. Determine the adjustment direction (increase / decrease the power of non-critical loads).

[0138] It should be noted that the offset threshold can be dynamically adjusted based on the remaining battery power (the lower the power, the stricter the threshold). Anti-false triggering mechanism: The offset trigger must also meet the power change rate > 5% / second to avoid interference from gradually changing loads.

[0139] The power offset and the status of the expansion battery pack (e.g., newly added capacity) are pushed to the data processing module via a message queue, triggering the dynamic allocation algorithm to regenerate an updated power allocation strategy with the latest parameters, overwriting the original power allocation strategy. The updated power allocation strategy is marked with a version number (e.g., V2.1.5) to indicate that it overwrites the old version, ensuring atomic switching at the execution layer.

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

[0141] S51. Real-time monitoring of battery temperature data and power port on / off status. When it is detected that the battery temperature exceeds a safety threshold or the main power input is interrupted, a three-level fault classification mechanism is triggered, generating an emergency response instruction set based on the fault type and severity.

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

[0143] Level 1 fault: The battery temperature exceeds the limit (e.g. > 55°C) or a single port is short-circuited, triggering local load disconnection.

[0144] Level 2 fault: If the main power input is interrupted and the energy storage capacity is less than 20%, the backup power pre-switching is started.

[0145] Level 3 fault: Temperature exceeds the limit and input interruption occurs, activating the full system protection mode.

[0146] Generate differentiated emergency instruction sets through fault type coding and severity weight (level 1-3).

[0147] S52: Based on the emergency response instruction set, the intelligent relays of the power supply ports for non-critical loads are preferentially disconnected, and the backup power supply switching module is activated to dynamically select a diesel generator or an energy storage battery pack as a temporary power supply based on the availability weight of the backup power supply;

[0148] The backup power source's availability weight is calculated by adding the remaining capacity and response speed of the backup power source using preset weight coefficients. The backup power source is selected based on this availability weight. The backup power source's availability weight W = 0.6 × the remaining battery capacity of the backup power source + 0.4 × the response speed of the backup power source (diesel engine > battery pack).

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

[0150] Non-critical loads are disconnected in batches according to their priority, using a cascade release strategy.

[0151] S53: Push the fault type, treatment measures, and current load power supply status in real time through the interactive interface, generate an encrypted processing log and synchronize it to the cloud server, and receive the policy modification instructions or priority adjustment requests input by the user;

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

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

[0154] Encrypted logs: Use the TLS 1.3 protocol to encrypt log streams, with fields including timestamps and event codes.

[0155] User commands are verified through a whitelist mechanism, which only allows modifications to non-critical load priorities (±1 level) and preset endurance thresholds.

[0156] S54: Inject the user correction instruction and processing log 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 correction instructions update algorithm parameters through the policy injection interface, including priority remapping. The modified load priority is updated in real time in the S13 configuration file. Backup rule optimization uses a decision tree model trained 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). Hot updates are supported only when the major version is consistent.

[0158] Please refer to Figure 2 、 Figure 3 and Figure 4 The embodiment of the present application further provides an emergency power supply, which is controlled by the emergency power supply method of the emergency power supply in the above method embodiment. The emergency power supply includes:

[0159] The sensor module 210 includes a power monitoring sensor, an ambient temperature sensor, and power sensors at the input and output ports of the battery pack;

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

[0161] The execution module 230 includes an intelligent relay and a load monitoring circuit, which is used to execute the charge and discharge strategy to switch the on and off state of the power supply port and monitor the changes in the 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 embodiment of this application, Figure 4 As shown, the emergency power supply further includes a housing assembly 10, and a battery module is disposed inside the housing assembly 10;

[0164] The outer wall of the housing assembly 10 is provided with a power output module 21 and a capacity expansion interface module 22 , and the interaction module is integrated into one side of the capacity expansion interface module 22 ;

[0165] The housing assembly 10 is provided with a moving wheel 30 and a driving assembly for driving the moving wheel 30 to move.

[0166] An embodiment of the present application further provides an electronic device, the device including a processor and a memory;

[0167] The memory is used to store program codes and transmit the program codes to the processor;

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

[0169] An embodiment of the present application also provides a computer-readable storage medium, which is used to store program code. When the program code is executed by a processor, it implements the emergency power supply method of the emergency power supply in the aforementioned method embodiment.

[0170] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0171] In the specification of this application and the above-mentioned drawings, the terms "first," "second," "third," "fourth," etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements 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 "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0174] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0175] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0176] If the integrated unit is implemented in the form of 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 the present application, 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. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name: Read-Only Memory, English abbreviation: ROM), random access memory (full name: Random Access Memory, English abbreviation: RAM), disk or optical disk, and other media that can store program code.

[0177] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An emergency power supply method for an emergency power supply, characterized in that: include: Collect the remaining battery power, ambient temperature, input and output power data, and solar power data of the emergency power supply, receive the preset load priority list, and generate the initial power supply parameter data set; Based on the initial power supply parameter data set, generating a power allocation strategy through a dynamic allocation algorithm; Adaptively compensate the power distribution strategy according to the ambient temperature and the solar power data, dynamically adjust the charging mode and the output power distribution ratio, and generate an optimized charging and discharging strategy; Controlling the intelligent relay to switch the on / off state of the power supply port according to the optimized charge and discharge strategy, and monitoring the changes in the port load demand to update the power distribution strategy; When abnormal battery temperature or power interruption is detected, the fault handling mechanism is activated to cut off non-critical loads and switch to backup power supply. At the same time, real-time status alarms and processing logs are pushed, and user feedback is received to modify the power distribution strategy.

2. The emergency power supply method of the emergency power supply according to claim 1, characterized in that: The method collects the remaining battery power, ambient temperature, input and output power data, and solar power data of the emergency power supply, receives a preset load priority list, and generates an initial power supply parameter data set, including: Collect the emergency power supply's battery remaining capacity, ambient temperature, input and output power data, and solar power data at a preset frequency; Receive a load priority list input by a user through an interactive interface, verify the format and integrity of the load priority list, and generate a standardized priority configuration file; The battery remaining power data, the ambient temperature, the input and output power data, and the solar power data are time-series aligned with the priority configuration file, and after removing abnormal values, they are integrated into a structured data packet according to a preset data template to obtain an initial power supply parameter data set.

3. The emergency power supply method of the emergency power supply according to claim 1, characterized in that: Generating a power distribution strategy by a dynamic distribution algorithm based on the initial power supply parameter data set includes: Building a multi-objective optimization model based on the load priority list, and determining the partition ratio of the critical load guarantee area and the non-critical load floating area through the multi-objective optimization model; Calculating an available total energy pool according to the remaining battery power and the total battery energy, and dividing the available total energy pool into a critical load guarantee zone and a non-critical load floating zone according to load priority levels through a dynamic allocation algorithm based on the partition ratio; For the critical load protection area, the goal is to minimize the sum of the differences between the allocated power and the minimum power requirements of all devices, ensure that the minimum power requirements of the devices corresponding to the critical load protection area are fully met, and reserve emergency redundant power, and generate a critical load allocation result; For the non-critical load floating area, the power adjustment range of the non-critical load floating area is dynamically calculated by a fuzzy logic controller in combination with the battery remaining power attenuation rate and the ambient temperature change rate, and the upper and lower limits of the power adjustment of the non-critical equipment are generated to obtain the non-critical load floating rule; The critical load distribution result is integrated with the non-critical load floating rule to generate an initial power distribution strategy.

4. The emergency power supply method of claim 3, wherein: The critical load allocation result is integrated with the non-critical load floating rule to generate an initial power allocation strategy, and then further includes: The feasibility of the initial power allocation strategy in the target scenario is verified through Monte Carlo simulation. If the simulation fails, the available total energy pool is redivided.

5. The emergency power supply method of claim 3, wherein: The method of adaptively compensating the power distribution strategy based on the ambient temperature and the solar power data, dynamically adjusting the charging mode and the output power distribution ratio, and generating an optimized charging and discharging strategy includes: The real-time collected ambient temperature is combined with a relationship curve between historical ambient temperature and battery charge and discharge efficiency to predict the attenuation trend of battery charge and discharge efficiency within a preset time period in the future; Correcting a maximum allowable power threshold of a charging mode based on the predicted attenuation trend of the battery charge and discharge efficiency and a temperature compensation coefficient calculated based on the ambient temperature; Calculating the photovoltaic power fluctuation rate according to the solar power data, and dynamically adjusting the charging mode and the solar charging weight ratio according to the photovoltaic power fluctuation rate; A fuzzy controller is used to optimize the output power distribution ratio in the power distribution strategy according to the temperature compensation coefficient and the solar charging weight ratio to obtain a power distribution correction parameter; The power distribution correction parameters are input into the target optimization model for solution to generate an optimized charge and discharge strategy.

6. The emergency power supply method of claim 1, wherein: The controlling intelligent relay switches the on / off state of the power supply port according to the optimized charge and discharge strategy, and monitors the change of the port load demand to update the power distribution strategy, including: Decoding the optimized charge and discharge strategy into an executable instruction set, and extracting the on / off timing, expansion trigger threshold, and load monitoring period of the power supply port; 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 order of priority; Monitor whether the remaining battery power is lower than the expansion trigger threshold. If so, send an encrypted handshake signal to the pre-connected expansion battery pack, activate the battery pack parallel circuit after identity verification, and dynamically connect the expansion battery pack to increase the total energy storage capacity; collecting power data of each port load based on the load monitoring period, and calculating a power offset according to the power data of the port load and the expected power in the power distribution strategy; When the absolute value of the power offset exceeds the preset offset threshold value for multiple consecutive times, a strategy update signal is generated to update the power distribution strategy according to the power offset and the access status of the expanded battery pack.

7. The emergency power supply method of claim 1, wherein: When abnormal battery temperature or power outage is detected, the fault handling mechanism is activated to disconnect non-critical loads and switch to the backup power source. At the same time, real-time status alarms and processing logs are pushed, and user feedback is received to modify the power allocation strategy, including: When 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 the power supply ports of non-critical loads are preferentially cut off, and a diesel generator or an energy storage battery pack is selected as a backup power source according to the availability weight of the backup power source; The fault type, treatment measures and current load power supply status are pushed in real time through the interactive interface, an encrypted processing log is generated and synchronized to the cloud server, and at the same time, the policy modification instructions or priority adjustment requests input by the user are received to update the power distribution strategy.

8. The emergency power supply method of claim 7, wherein: The method for calculating the availability weight of the backup power supply includes: The remaining capacity and response speed of the backup power supply are weighted and summed by a preset weight coefficient to obtain the availability weight of the backup power supply.

9. An emergency power supply, characterized in that: The emergency power supply method according to any one of claims 1 to 8 is used to realize regulation, wherein the emergency power supply comprises: Sensor modules, including power monitoring sensors deployed in the battery pack, ambient temperature sensors, and power sensors at input and output ports; a data processing module integrating a multi-core processor and a dynamic allocation algorithm unit, for generating and optimizing a power allocation strategy based on the data collected by the sensor module and a load priority list; The execution module includes an intelligent relay and a load monitoring circuit, which is used to execute the charge and discharge strategy to switch the on and off status of the power supply port and monitor the changes in the port load demand; The interactive module is equipped with a high-definition touch screen and a cloud communication interface.

10. The emergency power supply according to claim 9, characterized in that: The emergency power supply further comprises a housing assembly, wherein a battery module is disposed inside the housing assembly; A power output module and a capacity expansion interface module are provided on the outer side wall of the housing assembly, and the interaction module is integrated on one side of the capacity expansion interface module; The housing assembly is provided with a moving wheel and a driving assembly for driving the moving wheel to move.

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