Charging control method, system and equipment for mobile energy storage power supply and medium

Abstract

The invention provides a mobile energy storage power supply charging control method, system and device and a medium, and relates to the technical field of mobile energy storage, and the method comprises the steps: predicting the predicted electric quantity, needing to be consumed by a rescue task, of a mobile energy storage power supply in combination with the task type and position characteristics of the rescue task; obtaining the residual electric quantity of the mobile energy storage power supply, and calculating the electric quantity difference between the predicted electric quantity and the residual electric quantity when the residual electric quantity is smaller than the predicted electric quantity; generating a first power utilization strategy of the necessary power utilization equipment and a second power utilization strategy of the unnecessary power utilization equipment according to the electric quantity difference value; predicting a task change trend of the rescue task according to the position features, and generating a multi-stage electric quantity demand prediction curve according to the task change trend; and according to the multi-stage electric quantity demand prediction curve, adjusting the first power utilization strategy and the second power utilization strategy, and correspondingly generating a first target power utilization strategy and a second target power utilization strategy. The system has the technical effects that insufficient power supply or electric quantity waste of key electric equipment is avoided, and the rescue efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of mobile energy storage technology, specifically to a mobile energy storage power supply charging control method, system, device, and medium. Background Technology

[0002] With the increasing complexity and diversification of emergency rescue operations, mobile energy storage power supplies have become indispensable key equipment at rescue sites. However, in actual rescue missions, due to the varied mission types, complex rescue environments, and diverse types of electrical equipment, how to rationally allocate and control the power usage of mobile energy storage power supplies to ensure the successful completion of rescue missions has become an urgent technical problem to be solved. In particular, when the remaining power of the mobile energy storage power supply is insufficient to support the entire rescue mission, how to achieve optimized power allocation and dynamic adjustment while ensuring power supply to critical equipment is a significant challenge currently facing the field of emergency rescue.

[0003] In existing technologies, a static power management method based on historical data is typically used. This involves pre-setting fixed power consumption strategies and allocation schemes based on the power consumption patterns of similar rescue missions in the past. While this method can achieve a certain degree of rational power allocation, the dynamic nature of rescue missions means that pre-set fixed power consumption strategies are difficult to adapt to new demands. This can lead to insufficient power supply to critical equipment or wasted power, affecting rescue efficiency. Summary of the Invention

[0004] This application provides a mobile energy storage power supply charging control method, system, and device to avoid insufficient power supply or wasted power for critical electrical equipment and improve rescue efficiency.

[0005] In a first aspect, this application provides a mobile energy storage power supply charging control method, the method comprising: acquiring the task type and rescue location of a rescue mission, and the location characteristics of the rescue location, and combining the task type and the location characteristics to predict the predicted power consumption of the mobile energy storage power supply required by the rescue mission; The remaining power of the mobile energy storage power source is obtained. When the remaining power is less than the predicted power, the power difference between the predicted power and the remaining power is calculated. When the power difference is greater than a preset difference, multiple electrical devices are divided into necessary electrical devices and unnecessary electrical devices according to the task type. Based on the power difference, a first power consumption strategy for the necessary electrical devices and a second power consumption strategy for the unnecessary electrical devices are generated. The multiple electrical devices are electrical devices corresponding to the rescue task. Based on the location characteristics, the task change trend of the rescue task is predicted. Based on the task change trend, a multi-stage power demand prediction curve is generated. Based on the multi-stage power demand prediction curve, the first power consumption strategy and the second power consumption strategy are adjusted to generate a first target power consumption strategy and a second target power consumption strategy.

[0006] By adopting the above technical solution, the predicted power consumption of a rescue mission can be predicted by acquiring the mission type and location characteristics of the rescue location, enabling accurate assessment of power demand before the mission begins. When the remaining power of the mobile energy storage power source is detected to be less than the predicted power, the power difference can be calculated and compared with a preset difference to determine whether power optimization control measures need to be activated in a timely manner. When the power difference is large, the equipment is divided into necessary and unnecessary equipment according to the mission type, and a first power consumption strategy and a second power consumption strategy are generated accordingly. This ensures reliable power supply to critical equipment and optimizes the allocation of limited power. In particular, this application further predicts the mission change trend based on location characteristics and generates a multi-stage power demand prediction curve. Based on this, the first and second power consumption strategies are dynamically adjusted to generate a first target power consumption strategy and a second target power consumption strategy. This allows the power consumption strategy to adapt to the dynamic changes of the rescue mission, avoids insufficient power supply to critical equipment or power waste, and improves rescue efficiency.

[0007] Secondly, this application provides a mobile energy storage power supply charging control system, the system comprising: a first acquisition module, a second acquisition module, a first generation module, a second generation module, and an adjustment module; wherein, The first acquisition module is used to acquire the task type and rescue location of the rescue mission, as well as the location characteristics of the rescue location, and predict the predicted power consumption of the mobile energy storage power source required by the rescue mission based on the task type and the location characteristics. The second acquisition module is used to acquire the remaining power of the mobile energy storage power source, and calculate the power difference between the predicted power and the remaining power when the remaining power is less than the predicted power. The first generation module is used to divide multiple power-consuming devices into necessary power-consuming devices and unnecessary power-consuming devices according to the task type when the power difference is greater than a preset difference, and generate a first power consumption strategy for the necessary power-consuming devices and a second power consumption strategy for the unnecessary power-consuming devices according to the power difference, wherein the multiple power-consuming devices are power-consuming devices corresponding to the rescue mission. The second generation module is used to predict the task change trend of the rescue mission based on the location characteristics, and generate a multi-stage power demand prediction curve based on the task change trend. The adjustment module is used to adjust the first power consumption strategy and the second power consumption strategy according to the multi-stage power demand prediction curve, and generate a first target power consumption strategy and a second target power consumption strategy accordingly.

[0008] Thirdly, this application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to make the electronic device execute a computer program such as any of the mobile energy storage power supply charging control methods described above.

[0009] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and executed any of the above-mentioned mobile energy storage power supply charging control methods.

[0010] In summary, this application includes at least one of the following beneficial technical effects: By acquiring the task type and location characteristics of the rescue site, the predicted power consumption of the rescue mission can be calculated, enabling accurate assessment of power demand before the mission begins. When the remaining power of the mobile energy storage power source is detected to be less than the predicted power, the power difference can be calculated and compared with a preset difference to determine whether power optimization control measures need to be activated in a timely manner. In cases of large power differences, the equipment is categorized into necessary and unnecessary equipment based on the task type, and a first and second power consumption strategy are generated accordingly. This ensures reliable power supply to critical equipment while optimizing the allocation of limited power. Specifically, this application further predicts the task change trend based on location characteristics and generates a multi-stage power demand prediction curve. Based on this, the first and second power consumption strategies are dynamically adjusted to generate a first target power consumption strategy and a second target power consumption strategy. This allows the power consumption strategy to adapt to the dynamic changes of the rescue mission, avoiding insufficient power supply to critical equipment or power waste, and improving rescue efficiency. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of a mobile energy storage power supply charging control method provided in an embodiment of this application; Figure 2 This is a flowchart of a mobile energy storage power intelligent dispatch and control system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a mobile energy storage power supply charging control system provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0012] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0014] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0015] Figure 1 This is a schematic flowchart of a mobile energy storage power supply charging control method provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S106: S101: Obtain the mission type and location of the rescue mission, as well as the location characteristics of the rescue location. Based on the mission type and location characteristics, predict the predicted power consumption of the mobile energy storage power source required for the rescue mission.

[0016] When the rescue command center receives a rescue mission instruction, the system first needs to accurately assess the power demand of the mission. This is because different types of rescue missions require significantly different electrical equipment, work intensity, and duration, and the environmental characteristics of the rescue location will directly affect the actual power consumption and working efficiency of the equipment. Therefore, comprehensive prediction must be made to ensure that the mobile energy storage power supply can meet the power demand of the entire rescue process.

[0017] In practice, the system obtains task type information through the rescue dispatch platform. Task types include earthquake rescue, flood rescue, fire rescue, and medical emergency rescue, among others. Each task type corresponds to different standard equipment configurations and expected operating modes. Simultaneously, the system obtains the geographic coordinates of the rescue location and queries detailed location characteristics through a geographic information system and meteorological database. Location characteristics include parameters in three main dimensions: the first, the geographic environment parameter, encompasses elements such as topography, altitude, and geological conditions; the first, the climate condition parameter, includes meteorological data such as temperature, humidity, wind speed, and precipitation; and the first, the infrastructure completeness parameter, reflects the local power grid coverage, transportation convenience, and communication network conditions.

[0018] After obtaining the mission type and location characteristics, the system retrieves related missions of the same type from the historical rescue mission database. These related missions refer to historical cases with the same mission type in past rescue operations. The system extracts historical power consumption data for these related missions, including the actual power consumption, working time, and total power consumption of various electrical equipment. The statistical average of these historical data is used as the initial power consumption benchmark value for the current mission type.

[0019] To improve prediction accuracy, the system further calculates the correlation index between the target location features of the associated task and the location features of the current rescue location. The calculation process involves comparing and analyzing the second geographic environment parameter, second climate condition parameter, and second infrastructure completeness parameter of the historical location of the associated task with the corresponding parameters of the current location. The system calculates the first similarity between the first and second geographic environment parameters through a comprehensive evaluation of indicators such as terrain matching degree, altitude difference, and geological condition similarity; it calculates the second similarity between the first and second climate condition parameters based on a comparative analysis of meteorological elements such as temperature difference range, humidity difference, and wind force difference; and it calculates the third similarity between the first and second infrastructure completeness parameters, determined by the matching degree of infrastructure indicators such as power grid density, transportation accessibility, and communication coverage.

[0020] The system calculates a comprehensive correlation index by weighting the first, second, and third similarities according to preset weights. A higher correlation index indicates a more similar environmental condition between the historical rescue location and the current location, thus increasing the reference value of historical power consumption data. When the correlation index exceeds a preset correlation threshold, it indicates that the historical data has strong reference value. The system calculates the correlation ratio between the correlation index and a preset benchmark correlation index, and adjusts the initial power consumption proportionally based on this ratio. For example, a correlation ratio of 1.2 indicates that the current location conditions are more severe than historical locations, requiring a 20% increase in the initial power consumption as the adjusted power consumption. When the correlation index is not greater than the preset correlation threshold, it indicates that the historical data has limited reference value. The system then queries a preset standard power consumption table based on the task type. This table is an industry standard developed based on extensive rescue experience and equipment technical parameters, obtaining the standard power consumption value for the corresponding task type as the predicted power consumption.

[0021] Based on the above embodiments, as an optional implementation, in S101, the predicted power consumption of the mobile energy storage power supply required for the rescue mission, combined with the mission type and location characteristics, specifically includes S11-S12: S11: Obtain the historical power consumption of related tasks with the same task type from multiple historical rescue missions, and use the historical power consumption as the initial power consumption of the task type.

[0022] The system retrieves records of related tasks of the same type as the current task from the historical database of rescue missions. Related tasks refer to cases in historical rescue activities that belong to the same type as the current task. For example, if the current task is earthquake rescue, the system will extract relevant data from all historical earthquake rescue missions. The system obtains historical power consumption information for these related tasks, including key parameters such as the actual power consumption of various rescue equipment, total power usage, and mission duration. Through statistical analysis of data from multiple related tasks, the system calculates the average power consumption level for this task type. This statistical result serves as the initial power consumption benchmark for the task type, providing a data starting point for subsequent refined adjustments.

[0023] S12, calculate the target location features and the correlation index of the location features of the associated task, adjust the initial power consumption according to the correlation index, and generate the predicted power consumption of the mobile energy storage power source required for the rescue task.

[0024] The system calculates a correlation index between the target location features of the associated task and the location features of the current rescue location. This correlation index reflects the similarity between historical rescue locations and the current location in terms of environmental conditions. The calculation of the correlation index involves comparing parameters across multiple dimensions, including geographical environment, climate conditions, and infrastructure completeness. The system analyzes the matching degree of each dimension's parameters using a similarity algorithm and performs a comprehensive evaluation according to preset weights. A high correlation index indicates that historical data has strong reference value. Based on the comparison between the correlation index and the benchmark value, the system proportionally adjusts the initial power consumption to generate a more realistic predicted power consumption.

[0025] Based on the above embodiments, as an optional implementation, in S12, calculating the target location features and the correlation index of the location features of the associated task, and adjusting the initial power consumption according to the correlation index to generate the predicted power consumption of the mobile energy storage power supply required for the rescue task specifically includes S21-S25: S21, obtain the first geographic environment parameter, the first climate condition parameter, and the first infrastructure completeness parameter from the location features; obtain the second geographic environment parameter, the second climate condition parameter, and the second infrastructure completeness parameter from the target location features.

[0026] The system extracts detailed feature parameters from both the current rescue location and historically related mission locations. For the current rescue location, the system obtains first geographic environment parameters, including topographic relief, altitude, and geological type; first climate condition parameters, covering meteorological indicators such as temperature range, humidity level, wind speed, and precipitation; and first infrastructure completeness parameters, reflecting basic support conditions such as power grid density, transportation convenience, and communication coverage. Simultaneously, the system extracts corresponding parameters for the target location features from historically related mission data, namely second geographic environment parameters, second climate condition parameters, and second infrastructure completeness parameters, providing a complete data foundation for subsequent similarity calculations.

[0027] S22, calculate the first similarity between the first geographic environment parameter and the second geographic environment parameter, the second similarity between the first climate condition parameter and the second climate condition parameter, and the third similarity between the first infrastructure completeness parameter and the second infrastructure completeness parameter.

[0028] The system calculates the first similarity between the first and second geographical environment parameters by comparing the numerical differences and distribution characteristics of elements such as terrain features, altitude differences, and geological conditions to assess the degree of similarity. When calculating the second similarity between the first and second climate condition parameters, the system analyzes the matching degree of meteorological elements such as temperature differences, humidity variations, and wind force comparisons to assess the similarity of the impact of climate conditions on equipment operation. When calculating the third similarity between the first and second infrastructure completeness parameters, the system compares the completeness and availability of the infrastructure to assess the similarity level of external support conditions.

[0029] S23, the first similarity, second similarity and third similarity are weighted and summed to generate the association index.

[0030] The system performs a weighted summation of the first, second, and third similarity scores to generate a comprehensive correlation index. The weights for the summation are determined based on the importance of each dimension in different rescue scenarios. For example, climate conditions have a higher weight in polar rescues, while infrastructure has a higher weight in urban rescues. The correlation index typically ranges from 0 to 1; a higher value indicates more similar environmental conditions between the two locations, and greater reference value for historical electricity data.

[0031] S24. When the correlation index is greater than the preset correlation threshold, calculate the correlation ratio between the correlation index and the preset benchmark correlation index; adjust the initial power consumption proportionally according to the correlation ratio to obtain the adjusted power consumption, and use the adjusted power consumption as the predicted power consumption.

[0032] When the correlation index is greater than the preset correlation threshold, it indicates that the historical data has strong reference value. The system calculates the correlation ratio between the correlation index and the preset benchmark correlation index, which is the ideal matching degree benchmark value set by the system. The initial power consumption is adjusted proportionally according to the correlation ratio. For example, when the correlation ratio is 1.15, it means that the current location conditions are more complex than the historical locations, and the initial power consumption needs to be increased by 15%. The adjusted power consumption is then used as the final predicted power consumption.

[0033] S25, when the correlation index is not greater than the preset correlation threshold, query the preset standard power consumption table based on the task type, obtain the corresponding standard power consumption value, and use the standard power consumption value as the predicted power consumption.

[0034] When the correlation index is not greater than the preset correlation threshold, it indicates that the historical data has limited reference value, and continued use may lead to significant prediction errors. At this point, the system queries a preset standard power consumption table based on the task type. This table is an industry standard database developed based on extensive rescue practices and equipment specifications, containing benchmark power consumption values ​​for various rescue tasks under standard conditions. The system obtains the standard power consumption value corresponding to the current task type and uses it as the predicted power consumption value to ensure the basic reliability of the prediction results.

[0035] S102, obtain the remaining power of the mobile energy storage power source, and when the remaining power is less than the predicted power, calculate the power difference between the predicted power and the remaining power.

[0036] In practice, the system obtains the current remaining power information through the battery management system built into the mobile energy storage power source. Mobile energy storage power sources are typically equipped with an intelligent battery management unit (BMU), which monitors parameters such as battery pack voltage, current, and temperature in real time, and accurately calculates the remaining power using algorithms such as coulometrics and voltage integration. The system reads the remaining power value from the BMU's data interface; this value, expressed in kilowatt-hours (kWh), represents the total electrical energy the battery pack can still provide under standard discharge conditions. To ensure data accuracy, the system also obtains battery health status information, including parameters such as battery capacity degradation, internal resistance changes, and temperature compensation coefficient, and corrects the remaining power value to obtain the actual usable power under the current environmental conditions.

[0037] After obtaining the accurate remaining power, the system compares and analyzes it with the predicted power calculated in step S101. When the remaining power is greater than or equal to the predicted power, it indicates that the current power reserve is sufficient to meet the power needs of the entire rescue mission. The system records this status and continues to monitor power changes. However, when the remaining power is less than the predicted power, it indicates a risk of insufficient power supply, and the rescue mission may be unable to be completed due to power depletion. At this time, the system immediately enters the power difference calculation and risk assessment mode.

[0038] The power difference is calculated by subtracting the remaining power from the predicted power consumption. This difference represents the electrical energy needed to complete the rescue mission. The magnitude of this difference directly reflects the severity of the power supply shortage and serves as a crucial basis for developing energy-saving strategies and equipment scheduling plans. For example, in a mountain earthquake rescue mission, if the system predicts that the rescue mission will require 120 kWh of electricity, but the mobile energy storage power source currently has only 85 kWh remaining, the power difference is 35 kWh. This means that at least 35 kWh of power consumption needs to be reduced through energy-saving measures or equipment adjustments.

[0039] While calculating the power shortage difference, the system also analyzes the proportion of this difference to the predicted power demand. This proportion provides a more intuitive reflection of the relative severity of the power shortage. When the difference is small, minor power adjustments may suffice; however, when the difference is large, more stringent energy-saving measures or even the shutdown of some non-critical equipment are required. Furthermore, the system categorizes the power shortage difference into risk levels based on the urgency and importance of the rescue mission, providing decision support for subsequent emergency response.

[0040] S103, when the power difference is greater than the preset difference, according to the task type, multiple electrical devices are divided into necessary electrical devices and unnecessary electrical devices, and according to the power difference, a first power consumption strategy for necessary electrical devices and a second power consumption strategy for unnecessary electrical devices are generated. The multiple electrical devices are the electrical devices corresponding to the rescue task.

[0041] The preset difference is a critical value set by the system based on rescue experience and safety margins, typically 10-15% of the predicted power consumption. When the actual power shortage exceeds this threshold, it indicates that the power supply shortage has reached a point where emergency energy-saving measures must be taken. At this time, the system must immediately activate its intelligent equipment management mechanism, using scientific equipment classification and differentiated power consumption strategies to minimize power consumption while ensuring the core needs of the rescue mission, thus avoiding the impact on the normal operation of critical rescue functions due to blindly saving power.

[0042] In practice, the system first queries a pre-defined equipment priority table to determine the corresponding equipment classification criteria based on the type of the current rescue mission. This equipment priority table is a database built upon the actual needs and importance of equipment in different rescue scenarios, detailing the priority ranking and classification rules for different types of electrical equipment in various rescue missions. For example, in earthquake rescue missions, life detectors, medical emergency equipment, and emergency lighting systems are classified as essential electrical equipment because these devices are directly related to life safety and basic rescue functions; while audio broadcasting equipment, non-core area lighting, and some auxiliary communication equipment are classified as non-essential electrical equipment, and the temporary discontinuation of these devices will not affect the core rescue process.

[0043] Essential electrical equipment refers to critical equipment that performs core functions in rescue missions and directly affects the rescue outcome and personnel safety. The normal operation of this type of equipment is crucial to the success of the rescue mission, and its power supply must be prioritized even in situations of power shortage. Non-essential electrical equipment refers to equipment that plays an auxiliary or enhancing role in the rescue process, but whose temporary shutdown will not seriously affect the achievement of the core rescue objectives. This type of equipment can save energy by shutting down or operating at reduced power when there is insufficient power.

[0044] After classifying the equipment, the system calculates the first total power demand for essential equipment and the second total power demand for non-essential equipment. The first total power demand is calculated by statistically analyzing the rated power and expected operating time of all essential equipment, while the second total power demand is the total power consumption of all non-essential equipment under normal operating conditions. These two calculations provide a quantitative basis for subsequent power consumption strategy development.

[0045] When the power difference is no greater than the second total power demand, it indicates that the power shortage can be resolved by adjusting non-essential electrical equipment. The system selects some non-essential equipment to shut down or reduce power based on the power difference and a preset shutdown priority. The shutdown priority is determined by a ranking rule based on factors such as equipment importance, power consumption, and the feasibility of alternative solutions. The system gradually shuts down or adjusts equipment power according to this priority order until the saved power reaches the required power difference. Simultaneously, the system generates a second power consumption strategy for non-essential equipment, which details which equipment needs to be shut down, which needs to operate at reduced power, and the specific power adjustment parameters. For essential electrical equipment, the system maintains its normal operation and generates a corresponding first power consumption strategy to ensure that the power supply to these critical devices is not affected.

[0046] When the power difference exceeds the second total power demand, it indicates that even shutting down all non-essential electrical equipment cannot completely resolve the power shortage. The system needs to adjust the power output of essential electrical equipment as well. In this case, the system first shuts down all non-essential equipment, generates a corresponding second power strategy, and then calculates the remaining difference between the power difference and the second total power demand. This remaining difference represents the power shortage that still exists after shutting down all non-essential equipment, which needs to be compensated for by adjusting the operating parameters of essential electrical equipment. Based on the remaining difference and a preset power reduction priority, the system performs fine-grained power adjustment on essential electrical equipment. The power reduction priority is a sequence of adjustments designed for essential equipment, prioritizing those whose power adjustments have a smaller impact on the rescue effect, ensuring that core functional equipment can maintain basic operational status.

[0047] Based on the above embodiments, as an optional implementation, in S103, according to the task type, multiple electrical devices are divided into necessary electrical devices and unnecessary electrical devices, and according to the power difference, a first power consumption strategy for necessary electrical devices and a second power consumption strategy for unnecessary electrical devices are generated, specifically including S31-S34: S31, based on the task type and the preset equipment priority table, divide multiple electrical devices into necessary electrical devices and unnecessary electrical devices.

[0048] The system categorizes various electrical devices at the rescue site based on task type and a pre-defined equipment priority table. The equipment priority table is a classification standard established based on the characteristics of different rescue tasks and the importance of equipment functions, clearly defining the priority level of each type of equipment in different rescue scenarios. For earthquake rescue missions, equipment directly related to personnel safety, such as life detectors, medical emergency equipment, and lighting systems, is classified as essential electrical equipment, while equipment that can be temporarily deactivated without affecting core rescue functions, such as entertainment equipment, non-critical monitoring equipment, and auxiliary ventilation equipment, is classified as non-essential electrical equipment. This classification provides the basic framework for subsequent differentiated power management.

[0049] S32, calculate the first total power demand of necessary electrical equipment and the second total power demand of non-necessary electrical equipment.

[0050] When calculating the initial total power demand for essential equipment, the system comprehensively considers factors such as the rated power, estimated operating time, and startup power consumption of each essential device to obtain the minimum power guarantee required to maintain core emergency functions. Simultaneously, it calculates the second total power demand for non-essential equipment, assessing the total power consumption of all non-essential equipment under normal operating conditions. These two total power demand figures provide a quantitative reference for subsequent power allocation decisions.

[0051] S33, when the power difference is not greater than the second total power demand, select some devices from the non-essential power devices to shut down or reduce power operation according to the power difference and the preset shutdown priority, generate the second power consumption strategy for non-essential power devices, and maintain the normal operation of essential power devices to generate the first power consumption strategy for essential power devices.

[0052] When the power difference is no greater than the second total power demand, it indicates that the power shortage problem can be solved by adjusting non-essential electrical equipment without affecting the normal operation of essential equipment. The system selects appropriate equipment from the non-essential electrical equipment for shutdown or power reduction based on the magnitude of the power difference and a preset shutdown priority. The shutdown priority is determined by factors such as the equipment's impact on rescue efficiency, its substitutability, and the operational complexity of shutdown, prioritizing the shutdown of equipment with the least impact. Through this selective adjustment, the system generates a second power consumption strategy for non-essential electrical equipment while maintaining the normal operation of essential electrical equipment, generating a corresponding first power consumption strategy.

[0053] S34, when the power difference is greater than the second total power demand, shut down all non-essential electrical equipment, generate a second power consumption strategy for non-essential electrical equipment, calculate the remaining difference between the power difference and the second total power demand, and adjust the power of essential electrical equipment according to the remaining difference and the preset power reduction priority, and generate a first power consumption strategy for essential electrical equipment.

[0054] When the power consumption difference exceeds the second total power demand, even shutting down all non-essential electrical equipment cannot completely resolve the power shortage problem. In this case, the system adopts stricter power-saving measures. First, it shuts down all non-essential electrical equipment and generates a corresponding second power consumption strategy. Then, it calculates the remaining difference between the power consumption difference and the second total power demand. This remaining difference represents the power shortfall that needs to be compensated by adjusting essential equipment. Based on the magnitude of the remaining difference and a preset power reduction priority, the system adjusts the power of essential electrical equipment. The power reduction priority considers factors such as the criticality of the equipment's function, the feasibility of power adjustment, and the impact on rescue effectiveness, ensuring that while maintaining power balance, the effectiveness of critical functions is preserved as much as possible.

[0055] S104, based on location characteristics, predicts the mission change trend of the rescue mission, and generates a multi-stage power demand prediction curve based on the mission change trend.

[0056] Rescue missions are characterized by distinct phases, with significant differences in equipment configuration, workload, and power demand across different phases. Traditional static power management methods often fail to adapt to these dynamic changes, easily leading to power surpluses in some phases and power shortages in others. Therefore, the system needs to predict the development pattern of the rescue mission based on location characteristics, construct dynamic power demand change curves, and formulate targeted power allocation strategies for different phases to ensure optimal matching between power supply and actual demand throughout the entire rescue process.

[0057] In practice, the system first determines the complexity level of the rescue mission based on three dimensions of location characteristics: geographical environment, climate conditions, and infrastructure completeness. Geographical environment parameters include factors such as terrain relief, accessibility, and geological stability, which directly affect the deployment difficulty and efficiency of rescue equipment. Climate condition parameters encompass meteorological elements such as extreme temperatures, precipitation intensity, wind speed, and visibility; severe weather conditions increase equipment protection requirements and energy consumption. Infrastructure completeness parameters reflect the basic support capabilities of the rescue location, including power grid coverage, communication networks, road conditions, and medical resources; lower completeness means a higher self-sufficiency requirement for the rescue mission.

[0058] The system acquires indicators for topographic complexity, climatic severity, and infrastructure completeness, and then compares and analyzes these indicators against preset evaluation standards. The topographic complexity indicator is calculated using a digital elevation model and topographic relief algorithm. The system compares this indicator with preset geographical environment evaluation standards to generate a first-level geographical environment complexity score. The severity indicator is based on a comprehensive analysis of meteorological data, including factors such as temperature range, extreme weather probability, and environmental comfort. The system compares this indicator with preset climatic condition evaluation standards to generate a second-level climatic condition complexity score. The completeness indicator is quantitatively assessed using parameters such as infrastructure density, service coverage, and resource accessibility. The system compares this indicator with preset infrastructure evaluation standards to generate a third-level infrastructure completeness complexity score.

[0059] The system calculates a weighted average of the first, second, and third complexity scores according to preset weights to obtain a comprehensive complexity score. The weighting is typically determined based on empirical data from different rescue scenarios; for example, geographical environment has a higher weight in mountain rescues, while climate conditions have a greater weight in coastal rescues. The system maps the comprehensive complexity score to a corresponding complexity level—low, medium, and high—based on preset complexity level thresholds. This level directly influences the selection of subsequent task change trends.

[0060] After determining the complexity level, the system matches the corresponding task change trend from a pre-set task change trend database based on the task type and complexity level. This database is a knowledge base built upon a large number of historical rescue cases and expert experience, storing typical development patterns and stage characteristics of different task types at different complexity levels. For example, earthquake rescue in high-complexity environments typically requires longer equipment deployment time and more preliminary exploration work, while in low-complexity environments, it can quickly enter the main rescue phase.

[0061] Based on the matched task trends, the system divides the rescue mission into three main phases: the initial phase, the intermediate phase, and the final phase. The initial phase primarily includes site reconnaissance, equipment deployment, and safety assessment. This phase typically requires a large amount of exploration, lighting, and communication equipment, leading to a rapid increase in power demand. The intermediate phase is the main execution period of the rescue work, including core tasks such as personnel search and rescue, medical treatment, and material transportation. During this phase, equipment operation is at its peak, and power demand reaches its highest level and remains high. The final phase mainly involves site cleanup, post-disaster management, and equipment evacuation. As the rescue mission is gradually completed, power demand shows a decreasing trend.

[0062] The system calculates the power demand for each of the initial, intermediate, and final stages. The calculation is based on the configuration of rescue equipment and the expected operating time for each stage. The system calculates the total power demand for each stage based on the equipment list and work plan defined in the task change trend, combined with equipment power parameters and estimated operating time. The calculation also considers the impact of equipment startup power consumption, environmental adaptability adjustments, and redundancy backups on power demand.

[0063] Based on the time nodes and corresponding electricity demands of the initial, intermediate, and final stages, the system constructs a multi-stage electricity demand prediction curve. This curve, with time on the horizontal axis and electricity demand on the vertical axis, clearly represents the changing trend of electricity demand from the start to the end of the rescue mission. The curve typically exhibits a rapid rise in the initial stage, a plateau and fluctuation in the intermediate stage, and a gradual decline in the final stage, but the specific shape will vary depending on the type and complexity of the mission.

[0064] Based on the above embodiments, as an optional implementation, in S104, predicting the task change trend of the rescue mission according to location characteristics, and generating a multi-stage power demand prediction curve according to the task change trend, specifically includes S41-S44: S41. The complexity level of the rescue mission is determined based on the geographical environment, climate conditions, and infrastructure completeness in the location characteristics.

[0065] The system determines the complexity level of a rescue mission by comprehensively evaluating multiple dimensions of location characteristics. It analyzes geographical environmental parameters, including terrain complexity, accessibility, and geological stability, to assess the impact of environmental conditions on rescue operations. When analyzing climate parameters, the system considers the constraints of extreme temperatures, frequency of severe weather, and visibility conditions on equipment operation and personnel work. When analyzing infrastructure availability parameters, the system assesses the completeness of supporting conditions such as power supply stability, communication network coverage, and accessibility of medical resources. Based on the comprehensive score of these parameters, the system classifies the complexity level of rescue missions into different levels: simple, moderate, complex, and extreme, providing a classification basis for matching subsequent mission change trends.

[0066] Based on the above embodiments, as an optional implementation, in S41, determining the complexity level of the rescue mission according to the geographical environment, climate conditions, and infrastructure completeness in the location features specifically includes S411-S414: S411, respectively obtain the topographic complexity index of the geographical environment, the severity index of climatic conditions, and the completeness index of infrastructure.

[0067] The system extracts quantitative indicator data from three core dimensions. For the geographical environment dimension, the system obtains a terrain complexity index, which comprehensively reflects the complexity of geographical elements such as terrain relief, slope change rate, geological stability, and accessibility difficulty; a higher value indicates more complex terrain conditions. For the climate conditions dimension, the system obtains a severity index, calculated based on meteorological parameters such as extreme temperatures, wind speed, precipitation intensity, visibility level, and frequency of extreme weather, reflecting the degree of adverse impact of climate conditions on rescue operations. For the infrastructure completeness dimension, the system obtains a completeness index, which assesses the completeness of basic support conditions such as power supply stability, transportation network density, communication coverage, medical resource allocation, and emergency response capabilities; a lower value indicates less complete infrastructure and greater difficulty in rescue operations.

[0068] S412 compares the terrain complexity index with the preset geographical environment evaluation standard to generate the first complexity score of the geographical environment; compares the severity index with the preset climate condition evaluation standard to generate the second complexity score of the climate condition; and compares the completeness index with the preset infrastructure evaluation standard to generate the third complexity score of the infrastructure completeness.

[0069] The system compares the terrain complexity index with a preset geographical environment evaluation standard. This standard, established based on numerous rescue cases, divides the complexity index under different terrain conditions into several level intervals, each interval corresponding to a specific complexity score. The system generates a first geographical environment complexity score based on the interval to which the current index value belongs. Similarly, the system compares the severity index with a preset climate condition evaluation standard, generating a second complexity score based on the severity of the climate conditions. It also compares the completeness index with a preset infrastructure evaluation standard, generating a third complexity score based on the completeness of the infrastructure. This standardized scoring mechanism ensures the comparability and consistency of scores across different dimensions.

[0070] S413 calculates the weighted average of the first complexity score, the second complexity score, and the third complexity score to obtain the comprehensive complexity score.

[0071] The system calculates a weighted average of the complexity scores across three dimensions to generate a comprehensive complexity score. The weighting of each dimension is determined based on its importance in different rescue scenarios; for example, geographical environment has a higher weight in mountain rescues, climate conditions have a greater weight in polar rescues, and infrastructure is relatively important in urban rescues. The system dynamically adjusts the weighting ratios according to the characteristics of the current rescue mission, and the weighted average calculation yields a comprehensive score that fully reflects the complexity of the rescue environment.

[0072] S414: Based on a preset complexity level classification threshold, the overall complexity score is mapped to the corresponding complexity level, which includes low complexity, medium complexity, and high complexity.

[0073] The system maps the overall complexity score to a corresponding complexity level based on preset complexity level thresholds. These thresholds are determined through statistical analysis of historical rescue data, categorizing complexity into three main levels: low, medium, and high. When the overall complexity score is below the first threshold, the rescue mission is rated as low complexity, indicating a relatively simple rescue environment with fewer difficulties in equipment deployment and personnel operations. When the score is between the first and second thresholds, it is rated as medium complexity. When the score exceeds the second threshold, it is rated as high complexity, indicating an extremely complex rescue environment requiring a more cautious power management strategy.

[0074] S42, based on the task type and complexity level, matches the corresponding task change trend from the preset task change trend library, and divides the rescue task into the initial stage, the middle stage and the later stage according to the task change trend.

[0075] The mission change trend database is a knowledge base built upon a large number of historical rescue cases, containing typical development patterns and stage characteristics of different mission types at varying levels of complexity. The system retrieves the most matching mission change trend from the pattern database based on the current mission type and complexity level. This pattern details the complete timeline of the rescue mission from initiation to completion, along with the characteristic elements of each stage. Based on the matching mission change trend, the system scientifically divides the entire rescue mission into three stages: the initial stage, the intermediate stage, and the final stage. The initial stage mainly involves on-site assessment, equipment deployment, and emergency rescue activities; the intermediate stage focuses on continuous search and rescue, medical treatment, and safety assurance; and the final stage includes post-disaster recovery, equipment retrieval, and on-site restoration.

[0076] S43 calculates the stage power demand for the initial, intermediate, and late stages respectively. The stage power demand is determined based on the configuration of rescue equipment and the expected operating time for each stage.

[0077] For the initial stage of power demand calculation, the system calculates based on the rescue equipment configuration list and the expected operating time of each device. Typically, the initial stage requires the simultaneous operation of a large number of detection, communication, and lighting devices, resulting in relatively high power demand. The calculation for the mid-stage focuses on assessing the power consumption of continuously operating rescue equipment, such as life support equipment, monitoring systems, and construction machinery. The power demand calculation for the later stage considers factors such as gradual equipment shutdown, partial function transfer, and cleanup operations, typically showing a decreasing trend in power demand. The power demand calculation for each stage fully considers factors such as equipment startup power consumption, stable operation power consumption, and environmental correction factors to ensure the accuracy of the prediction results.

[0078] S44. Based on the time nodes of the initial, middle and late stages and the corresponding stage electricity demand, a multi-stage electricity demand forecast curve is constructed. The multi-stage electricity demand forecast curve is used to represent the changing trend of electricity demand from the start to the end of the rescue mission.

[0079] The system constructs a multi-stage electricity demand forecast curve based on the time node information of each stage, including the start time, duration, and end time of each stage, combined with the corresponding electricity demand data for each stage. This forecast curve is a continuous time function that can intuitively represent the dynamic change trend of electricity demand throughout the entire process of the rescue mission, from start to finish, including key information such as the time of peak demand, the distribution range of trough demand, and the slope characteristics of demand changes.

[0080] S105, based on the multi-stage electricity demand prediction curve, adjust the first electricity consumption strategy and the second electricity consumption strategy, and generate the first target electricity consumption strategy and the second target electricity consumption strategy accordingly.

[0081] In practice, the system first acquires the expected electricity demand and corresponding time node information for each stage of the multi-stage electricity demand forecast curve. The expected electricity demand reflects the power consumption required in the initial, middle, and later stages, while the time nodes identify the start time and duration of each stage. Based on this information, the system can construct a complete time-electricity demand mapping relationship, providing a time benchmark for subsequent dynamic power allocation.

[0082] After obtaining demand information at each stage, the system calculates the estimated remaining power of the mobile energy storage power source at the corresponding time points of each stage. This calculation process needs to consider the battery's natural discharge characteristics, the impact of ambient temperature on battery performance, and the cumulative effect of actual power consumption in previous stages. The system simulates the battery's power trajectory throughout the entire rescue process using a battery discharge model and environmental correction algorithms to obtain the estimated remaining power value at each key time point. This proactive power prediction helps the system identify potential power supply risks in advance.

[0083] The system assesses the match between expected electricity demand and estimated remaining electricity at each stage, identifying periods of power supply-demand imbalance through comparative analysis. When the expected electricity demand at a certain stage exceeds the estimated remaining electricity at that time point, it indicates a risk of insufficient power supply in that stage, and the system identifies this stage as a power adjustment stage. Identifying power adjustment stages is a prerequisite for dynamic optimization; only by accurately locating the problematic stage can targeted strategy adjustments be made.

[0084] For each identified power adjustment phase, the system formulates a corresponding adjustment plan based on the size of the power deficit in that phase. The power deficit is the difference between the expected power demand and the estimated remaining power in that phase, representing the amount of power shortage that needs to be compensated through strategy adjustments. The system adjusts the power configuration of non-essential equipment in the second power consumption strategy and essential equipment in the first power consumption strategy according to a preset adjustment priority sequence. The adjustment priority sequence is a dynamic adjustment order established based on factors such as equipment importance, adjustment flexibility, and the degree of impact on rescue effectiveness, ensuring that power balance is maintained while minimizing the impact on rescue functions.

[0085] During the adjustment process, the system first attempts to compensate for the power shortage by adjusting non-essential electrical equipment. During the power adjustment phase, the system may choose to shut down some non-essential equipment that was originally scheduled to run earlier, or adjust the operating time of these devices to other phases where power is relatively abundant. If adjusting non-essential equipment still cannot completely solve the power shortage problem, the system will then make appropriate power adjustments to essential electrical equipment, such as appropriately reducing the operating power of some equipment or shortening the operating time during non-critical periods, while ensuring basic functions are maintained.

[0086] Based on the adjusted power consumption configuration for each stage, the system recalculates the power demand for each stage and verifies the match between the adjusted power demand and the expected remaining power. This verification process is achieved by rebuilding the power balance model. The system simulates the execution effect of the adjusted power consumption strategy throughout the entire rescue process, ensuring that power supply and demand are balanced in all stages. If any imbalances are found during the verification process, the system further optimizes the adjustment scheme and finds the optimal power configuration scheme through iterative calculations.

[0087] When verification results show a good match between the adjusted power demand and the expected remaining power, the system will use the verified adjusted power configuration as the first target power consumption strategy and the second target power consumption strategy, respectively. The first target power consumption strategy is the final operating plan for necessary electrical equipment, which details the power settings, operating times, and priority arrangements for each key device at different stages. The second target power consumption strategy is the final management plan for non-essential electrical equipment, which clarifies the specific equipment that needs to be shut down, have its power reduced, or have its operating time adjusted at each stage, along with its adjustment parameters.

[0088] Based on the above embodiments, as an optional implementation, in S105, adjusting the first power consumption strategy and the second power consumption strategy according to the multi-stage power demand prediction curve, and generating the corresponding first target power consumption strategy and second target power consumption strategy specifically includes S51-S54: S51, obtain the expected electricity demand and corresponding time nodes for each stage in the multi-stage electricity demand forecast curve.

[0089] The system acquires expected electricity demand data for each stage, including specific electricity consumption at different points in time during the initial, middle, and later stages, as well as key time nodes such as the start time, peak demand time, and end time of each stage. This data provides a precise time-dimensional reference framework for subsequent electricity balance analysis and strategy adjustments, enabling the system to predictively identify the timing and severity of electricity supply-demand imbalances.

[0090] S52, calculate the expected remaining power of the mobile energy storage power source at each time point in each stage; determine the matching between the expected power demand and the expected remaining power in each stage; when there is a stage with insufficient power, identify that stage as the power adjustment stage.

[0091] The system calculates the expected remaining power at each stage based on the initial power of the mobile energy storage power source and the cumulative consumption in the previous stages. This calculation process considers practical factors such as the discharge characteristics of the energy storage power source, the impact of environmental factors on battery performance, and equipment start-up and shutdown losses. The system compares the expected power demand for each stage with the expected remaining power at the corresponding time point to determine the matching of power supply and demand. When the expected power demand for a certain stage exceeds the expected remaining power, it indicates that there is a risk of insufficient power supply in that stage. The system identifies this stage as a power adjustment stage, requiring strategy adjustments to resolve the power supply and demand imbalance.

[0092] S53, for the power adjustment phase, according to the size of the power shortage in this phase, and according to the preset adjustment priority sequence, the power configuration of the non-essential electrical equipment in the second power consumption strategy and the essential electrical equipment in the first power consumption strategy are adjusted in sequence.

[0093] The system first calculates the power shortage for this stage, i.e., the difference between the expected power demand and the estimated remaining power, as the target value for strategy adjustment. Based on a preset adjustment priority sequence, the system adopts a progressive adjustment method, prioritizing adjustments to equipment configurations with minimal impact on rescue effectiveness. The system first adjusts the configuration of non-essential equipment in the second power consumption strategy, reducing power consumption by shutting down some non-essential equipment, reducing equipment power, and delaying equipment startup time. When adjustments to non-essential equipment still cannot fully compensate for the power shortage, the system further adjusts the power configuration of essential equipment in the first power consumption strategy, performing appropriate power optimization for essential equipment while ensuring that core functions are not severely affected.

[0094] S54, based on the adjusted power consumption configuration for each stage, recalculate the power demand for each stage, verify the matching between the adjusted power demand and the expected remaining power, and use the verified adjusted power consumption configuration as the first target power consumption strategy and the second target power consumption strategy, respectively.

[0095] Based on the adjusted power consumption configuration for each stage, the system recalculates the power demand distribution throughout the entire rescue mission, generating a new power consumption time curve. The system verifies the match between the adjusted power demand and the projected remaining power at each time point, ensuring that power supply and demand are balanced in all stages without new power shortages. Simultaneously, the system assesses the impact of the strategy adjustment on rescue capabilities, ensuring that critical rescue capabilities are effectively guaranteed. Through multiple rounds of verification and optimization, the system ultimately determines the final verified adjusted power consumption configuration as the first and second target power consumption strategies. These two target strategies consider both hierarchical management of equipment importance and the dynamic balancing requirements over time.

[0096] For example, taking a rescue scenario following a 7.2 magnitude earthquake in a mountainous area as an example, when the rescue team arrives in the disaster area, the system first retrieves a landslide rescue mission conducted two months prior under similar geological conditions from the rescue database as a relevant reference. This historical mission used the same type of life detection equipment and medical rescue equipment in a similar mountainous environment, consuming a total of 650 kilowatt-hours of electricity, and lasting 68 hours. The system uses this as the basis for predicting electricity consumption.

[0097] The current earthquake-stricken area is located in a mountainous region at an altitude of 1350 meters, with drastically varied terrain and slopes generally exceeding 30 degrees. Multiple landslides have occurred since the earthquake, and roads have been severely damaged. Regarding climate, temperatures in the disaster area fluctuate between -5 and -8 degrees Celsius, accompanied by intermittent snowfall and winds reaching force 5-6. In terms of infrastructure, the power system is completely paralyzed, communication networks are largely disrupted, and the nearest hospital is more than 80 kilometers away. System comparative analysis reveals that the current rescue environment is more severe than in historically related missions, with a geographical similarity of 0.78, a climate similarity of 0.65, and an infrastructure completeness similarity of only 0.35.

[0098] Based on comprehensive similarity calculations, the system determined the correlation index to be 0.621. Due to the more severe current environment, the system revised the historical mission's power consumption from 650 kWh to 780 kWh as the predicted power requirement for the current rescue mission. The rescue team carried a mobile energy storage power supply with a capacity of 1200 kWh, with a reserve of 420 kWh.

[0099] In response to the urgent needs of earthquake relief, the system categorizes the rescue equipment it carries according to their importance. Life detectors, CPR equipment, defibrillators, oxygen concentrators, emergency lighting systems, and satellite communication equipment are classified as essential electrical equipment directly related to life safety. These devices have a total power of 1100 watts and require 79.2 kWh of electricity based on 72 hours of continuous operation. Surveillance cameras, portable computers, chargers, auxiliary lighting, and temporary heating equipment are classified as non-essential electrical equipment, with a total power of 800 watts and a corresponding electricity requirement of 57.6 kWh.

[0100] Since the available power of the mobile energy storage power supply far exceeds the predicted demand, the system has formulated a first power consumption strategy to ensure that all necessary equipment operates at full load, while a second power consumption strategy allows most non-essential equipment to operate normally, and only shuts down the charging function of a few portable devices to reserve a safety margin.

[0101] In terms of mission complexity assessment, the system analyzed the geographical environment, scoring 8.5 points for terrain complexity, 7.2 points for severity, and only 1.8 points for infrastructure completeness. Through weighted average calculation, the overall complexity score was 7.9 points, exceeding the high complexity threshold of 7.0 points. Therefore, the rescue mission was rated as high complexity.

[0102] Based on the characteristics and high complexity of earthquake rescue missions, the system matches corresponding development patterns from the mission change trend database, dividing the entire rescue process into three phases. The initial phase covers the first 24 hours of emergency search and rescue, requiring the simultaneous activation of all life detection equipment, medical equipment, high-power lighting systems, and communication equipment. This phase has the highest power demand, with an estimated power consumption of 185 kWh. The intermediate phase is the 24-56 hour continuous rescue period. As trapped personnel are gradually rescued, some high-power equipment can be used intermittently, with an estimated power consumption of 220 kWh. The later phase is the 56-72 hour site cleanup period, where the main equipment demand shifts to lighting, communication, and medical support, with an estimated power consumption of 120 kWh.

[0103] The multi-stage power demand prediction curve constructed by the system shows that the rescue mission reaches its peak power consumption in the initial stage, maintains a stable high consumption state in the middle stage, and the demand drops significantly in the later stage. Through power balance verification on the time axis, the system confirms that the mobile energy storage power supply has 1015 kWh remaining at the 24-hour node, 795 kWh remaining at the 48-hour node, and 675 kWh remaining at the 72-hour node. The remaining power at each time point can fully meet the needs of the subsequent stages, and there is no risk of insufficient power in any stage.

[0104] After dynamic balancing verification, the system confirmed that the current power consumption strategy configuration can fully meet the power needs of the entire rescue mission. The finalized target power consumption strategy ensures that the life detector can operate continuously for 72 hours without interruption, medical emergency equipment is on standby at all times, the emergency lighting system provides sufficient light source for nighttime rescue, and satellite communication equipment maintains real-time contact with the outside world. At the same time, monitoring equipment is reasonably configured for on-site safety monitoring, and computer equipment is used for data recording and coordination command, providing the rescue team with comprehensive and reliable power support.

[0105] During the actual rescue operation, this intelligent management solution successfully supported the rescue team's continuous 68-hour operation, rescuing 17 trapped individuals. The mobile energy storage power supply ultimately had over 600 kWh of remaining power. No equipment downtime due to insufficient power occurred throughout the entire rescue period, and critical life support equipment maintained stable operation, fully validating the effectiveness and reliability of this power management solution in complex rescue environments.

[0106] like Figure 2 As shown, Figure 2This is a flowchart illustrating the workflow of a mobile energy storage power intelligent dispatch and control system provided in this application embodiment. The system receives user identity / location information from a terminal device, and the mobile energy storage power supply provides status information such as remaining power. The control logic processing module receives these inputs and executes a series of processes, including predicting remaining power, system power, calculating power demand, formulating optimized power consumption strategies, predicting task completion, ensuring safety, and generating target power consumption strategies. Based on the processing results, it outputs two control paths: one is a priority power supply path, which prioritizes essential power-consuming equipment (such as data mining machines) according to the first target strategy; the other is a restriction / suspension path, which restricts or suspends power supply to non-essential power-consuming equipment (such as auxiliary lighting) according to the second target strategy. The entire system achieves intelligent power resource allocation and optimized management based on power status and equipment importance.

[0107] Based on the above method, this application also discloses a mobile energy storage power supply charging control system, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of a mobile energy storage power supply charging control system provided in an embodiment of this application. The system includes: a first acquisition module, a second acquisition module, a first generation module, a second generation module, and an adjustment module; wherein, The first acquisition module is used to acquire the task type and location of the rescue mission, as well as the location characteristics of the rescue location. Combining the task type and location characteristics, it predicts the power consumption of the mobile energy storage power source required for the rescue mission. The second acquisition module is used to acquire the remaining power of the mobile energy storage power source. When the remaining power is less than the predicted power, it calculates the power difference between the predicted power and the remaining power. The first generation module is used to, when the power difference is greater than a preset difference, classify multiple power-consuming devices into necessary power-consuming devices and unnecessary power-consuming devices according to the task type, and generate a first power consumption strategy for necessary power-consuming devices and a second power consumption strategy for unnecessary power-consuming devices based on the power difference. The multiple power-consuming devices are the power-consuming devices corresponding to the rescue mission. The second generation module is used to predict the task change trend of the rescue mission based on location characteristics, and generate a multi-stage power demand prediction curve based on the task change trend. The adjustment module is used to adjust the first power consumption strategy and the second power consumption strategy according to the multi-stage power demand prediction curve, and generate a first target power consumption strategy and a second target power consumption strategy accordingly.

[0108] Please see Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0109] The communication bus 1002 is used to realize the connection and communication between these components.

[0110] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0111] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0112] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0113] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 4 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a mobile energy storage power supply charging control method.

[0114] exist Figure 4 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call an application program stored in the memory 1005 for a mobile energy storage power supply charging control method. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0115] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0116] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0117] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed apparatus 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 displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0119] 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.

[0120] 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.

[0121] 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 device (CMD). 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 memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0122] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A charging control method for a mobile energy storage power supply, characterized in that, The method includes: The task type and location of the rescue mission, as well as the location characteristics of the rescue location, are obtained. Based on the task type and location characteristics, the predicted power consumption of the mobile energy storage power source for the rescue mission is predicted. Obtain the remaining power of the mobile energy storage power source; when the remaining power is less than the predicted power, calculate the power difference between the predicted power and the remaining power. When the power difference is greater than a preset difference, multiple electrical devices are divided into necessary electrical devices and unnecessary electrical devices according to the task type. Based on the power difference, a first power consumption strategy for the necessary electrical devices and a second power consumption strategy for the unnecessary electrical devices are generated. The multiple electrical devices are electrical devices corresponding to the rescue task. Based on the location features, predict the task change trend of the rescue mission, and generate a multi-stage power demand prediction curve based on the task change trend. Based on the multi-stage electricity demand prediction curve, the first electricity consumption strategy and the second electricity consumption strategy are adjusted to generate a first target electricity consumption strategy and a second target electricity consumption strategy.

2. The mobile energy storage power supply charging control method according to claim 1, characterized in that, The step of combining the task type and the location characteristics to predict the predicted power consumption of the mobile energy storage power source for the rescue mission includes: Obtain the historical power consumption of related tasks of the same type as the stated task from multiple historical rescue missions, and use the historical power consumption as the initial power consumption of the stated task type; Calculate the target location features of the associated task and the correlation index of the location features. Based on the correlation index, adjust the initial power consumption to generate the predicted power consumption of the mobile energy storage power source required for the rescue task.

3. The mobile energy storage power supply charging control method according to claim 2, characterized in that, The step of calculating the target location features of the associated task and the correlation index of the location features, adjusting the initial power consumption based on the correlation index, and generating a predicted power consumption of the mobile energy storage power source required for the rescue task includes: Obtain the first geographic environment parameter, the first climate condition parameter, and the first infrastructure completeness parameter from the location features; obtain the second geographic environment parameter, the second climate condition parameter, and the second infrastructure completeness parameter from the target location features; Calculate the first similarity between the first geographic environment parameter and the second geographic environment parameter, the second similarity between the first climate condition parameter and the second climate condition parameter, and the third similarity between the first infrastructure completeness parameter and the second infrastructure completeness parameter; The first similarity, the second similarity, and the third similarity are weighted and summed to generate a correlation index; When the correlation index is greater than the preset correlation threshold, the correlation ratio between the correlation index and the preset benchmark correlation index is calculated; based on the correlation ratio, the initial power consumption is proportionally adjusted to obtain the adjusted power consumption, and the adjusted power consumption is used as the predicted power consumption. When the correlation index is not greater than the preset correlation threshold, the preset standard power consumption table is queried based on the task type to obtain the corresponding standard power consumption value, and the standard power consumption value is used as the predicted power consumption.

4. The mobile energy storage power supply charging control method according to claim 1, characterized in that, The step of classifying multiple electrical devices into necessary and unnecessary electrical devices according to the task type, and generating a first power consumption strategy for the necessary electrical devices and a second power consumption strategy for the unnecessary electrical devices based on the power consumption difference, includes: Based on the task type and the preset equipment priority table, multiple electrical devices are divided into necessary electrical devices and unnecessary electrical devices. Calculate the first total power demand of the necessary electrical equipment and the second total power demand of the non-essential electrical equipment; When the power difference is not greater than the second total power demand, based on the power difference and the preset shutdown priority, some devices are selected from the non-essential power devices to be shut down or operate at reduced power, generating a second power consumption strategy for the non-essential power devices, while keeping the essential power devices running normally, generating a first power consumption strategy for the essential power devices. When the power difference is greater than the second total power demand, all non-essential electrical devices are shut down, a second power consumption strategy for the non-essential electrical devices is generated, the remaining difference between the power difference and the second total power demand is calculated, and the power of the essential electrical devices is adjusted according to the remaining difference and the preset power reduction priority, thus generating a first power consumption strategy for the essential electrical devices.

5. The mobile energy storage power supply charging control method according to claim 1, characterized in that, The step of predicting the mission change trend of the rescue mission based on the location features, and generating a multi-stage power demand prediction curve based on the mission change trend, includes: The complexity level of the rescue mission is determined based on the geographical environment, climate conditions, and infrastructure completeness in the location features. Based on the task type and the complexity level, the task change trend corresponding to the task type is matched from the preset task change trend library, and the rescue task is divided into the initial stage, the middle stage and the later stage according to the task change trend. The power demand for each of the initial, intermediate, and late stages is calculated, and the power demand for each stage is determined based on the configuration of rescue equipment and the expected operating time for each stage. Based on the time nodes of the initial stage, the middle stage, and the later stage and the corresponding stage electricity demand, a multi-stage electricity demand prediction curve is constructed. The multi-stage electricity demand prediction curve is used to characterize the changing trend of electricity demand from the start to the end of the rescue mission.

6. The mobile energy storage power supply charging control method according to claim 5, characterized in that, The determination of the complexity level of the rescue mission based on the geographical environment, climate conditions, and infrastructure completeness in the location features includes: The terrain complexity index of the geographical environment, the severity index of the climate conditions, and the completeness index of the infrastructure are obtained respectively. The terrain complexity index is compared with the preset geographical environment evaluation standard to generate a first complexity score of the geographical environment; the severity index is compared with the preset climate condition evaluation standard to generate a second complexity score of the climate condition; and the completeness index is compared with the preset infrastructure evaluation standard to generate a third complexity score of the infrastructure completeness. The first complexity score, the second complexity score, and the third complexity score are weighted and averaged to obtain the comprehensive complexity score. Based on a preset threshold for classifying complexity levels, the overall complexity score is mapped to a corresponding complexity level, which includes low complexity, medium complexity, and high complexity.

7. The mobile energy storage power supply charging control method according to claim 1, characterized in that, The step of adjusting the first power consumption strategy and the second power consumption strategy according to the multi-stage power demand prediction curve, and generating a first target power consumption strategy and a second target power consumption strategy accordingly, includes: Obtain the expected electricity demand and corresponding time points for each stage in the multi-stage electricity demand forecast curve; Calculate the expected remaining power of the mobile energy storage power source at each time point in each stage; determine the matching between the expected power demand and the expected remaining power in each stage; when there is a stage with insufficient power, identify that stage as a power adjustment stage. For the power adjustment phase, based on the size of the power shortage in this phase, the power configuration of the non-essential electrical equipment in the second power consumption strategy and the essential electrical equipment in the first power consumption strategy are adjusted sequentially according to a preset adjustment priority sequence. Based on the adjusted power consumption configuration for each stage, the power demand for each stage is recalculated, and the matching between the adjusted power demand and the expected remaining power is verified. The verified adjusted power consumption configuration is used as the first target power consumption strategy and the second target power consumption strategy, respectively.

8. A mobile energy storage power supply charging control system, characterized in that, The system includes: a first acquisition module, a second acquisition module, a first generation module, a second generation module, and an adjustment module; wherein, The first acquisition module is used to acquire the task type and rescue location of the rescue mission, as well as the location characteristics of the rescue location, and, in combination with the task type and the location characteristics, predict the predicted power consumption of the mobile energy storage power source required by the rescue mission. The second acquisition module is used to acquire the remaining power of the mobile energy storage power source, and when the remaining power is less than the predicted power, to calculate the power difference between the predicted power and the remaining power. The first generation module is used to divide multiple electrical devices into necessary electrical devices and unnecessary electrical devices according to the task type when the power difference is greater than a preset difference, and generate a first power consumption strategy for the necessary electrical devices and a second power consumption strategy for the unnecessary electrical devices according to the power difference, wherein the multiple electrical devices are electrical devices corresponding to the rescue task; The second generation module is used to predict the task change trend of the rescue mission based on the location features, and generate a multi-stage power demand prediction curve based on the task change trend. The adjustment module is used to adjust the first power consumption strategy and the second power consumption strategy according to the multi-stage power demand prediction curve, and generate a first target power consumption strategy and a second target power consumption strategy accordingly.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7.

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