A method and system for dynamically supplying power from an emergency battery pack based on emergency response time

Abstract

The present invention relates to the field of energy and electricity, and discloses a method for dynamically supplying electric energy of an emergency battery pack based on emergency response time, including: constructing an electric energy analysis model of an emergency scene, analyzing the potential electric energy demand of the emergency scene using the electric energy analysis model; constructing an emergency response signal of the emergency scene, determining a pre-charged battery module of the emergency scene, connecting the pre-charged battery module to obtain an emergency battery pack, analyzing the electrical fault of the emergency battery pack, using the emergency battery pack as a target emergency battery pack of the emergency scene, activating the target emergency battery pack, and obtaining an activated emergency battery pack; constructing a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scene, analyzing the dynamic output electric energy of the activated emergency battery pack using the dynamic load balancing algorithm, and executing the dynamic supply of electric energy of the emergency scene based on the dynamic output electric energy. The present invention can improve the emergency efficiency of emergency scenes.

Description

Technical Field

[0001] The invention relates to a method and system for dynamically supplying electric energy of an emergency battery pack based on emergency response time, and belongs to the field of energy and electricity. Background Art

[0002] Emergency battery pack power dynamic supply refers to a system and process that can quickly adjust and distribute power according to real-time needs when an emergency or disaster occurs. The emergency battery pack power dynamic supply system can play an important role in emergency scenarios such as natural disasters, accident rescue, and medical emergencies, providing necessary power support for rescue work.

[0003] Traditional emergency battery power supply methods usually rely on pre-configured fixed battery packs and manual operation processes. This method lacks real-time data collection and intelligent analysis capabilities, resulting in long emergency response times, inflexible power distribution, and difficulty in adapting to rapidly changing emergency needs, which may affect rescue efficiency and safety. Summary of the invention

[0004] The present invention provides an emergency battery pack power dynamic supply method and system based on emergency response time, the main purpose of which is to improve the emergency efficiency of emergency scenes.

[0005] To achieve the above object, the present invention provides a method for dynamically supplying power of an emergency battery pack based on emergency response time, comprising:

[0006] Collect emergency scenario data of the emergency scenario using the integrated communication system, construct an electric energy analysis model of the emergency scenario based on the emergency scenario data, and analyze the potential electric energy demand of the emergency scenario using the electric energy analysis model;

[0007] Analyzing the emergency coefficient of the potential power demand, and when the emergency coefficient is greater than the preset emergency threshold, constructing an emergency response signal of the emergency scenario according to the potential power demand;

[0008] Based on the emergency response signal, determine the pre-charged battery module of the emergency scenario, construct an emergency battery assembly model of the pre-charged battery module, and connect the pre-charged battery module according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery module includes physical connection and electrical connection;

[0009] Analyze the electrical fault of the emergency battery pack, and when the electrical fault meets a preset electrical fault threshold, use the emergency battery pack as the target emergency battery pack of the emergency scenario, and activate the target emergency battery pack using a preset fast activation function according to the emergency response time requirement corresponding to the potential electric energy demand, to obtain an activated emergency battery pack;

[0010] Construct a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scenario, and based on the potential power demand, use the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack, and execute dynamic power supply for the emergency scenario based on the dynamic output power.

[0011] Optionally, constructing the electric energy analysis model of the emergency scenario according to the emergency scenario data includes:

[0012] Preprocessing the emergency scene data to obtain preprocessed scene data;

[0013] Constructing an automated feature extraction algorithm for the preprocessed scene data;

[0014] Extracting a feature subset of the preprocessed scene data based on the automated feature extraction algorithm;

[0015] Integrate the feature subsets using a preset feature integration algorithm to obtain target features;

[0016] Determining an adaptive model for power analysis of the emergency scenario;

[0017] Using the target features to train the electric energy analysis adaptive model to obtain a trained electric energy analysis adaptive model;

[0018] Analyzing the power demand analysis performance of the trained power analysis adaptive model;

[0019] When the power demand analysis performance meets a preset analysis performance threshold, the trained power analysis adaptive model is used as the power analysis model for the emergency scenario.

[0020] Optionally, integrating the feature subsets using a preset feature integration algorithm to obtain target features includes:

[0021] Calculating the feature F1 score of the feature subset using the F1 score function corresponding to the feature integration algorithm;

[0022] Calculate the feature Gini coefficient of the feature subset according to the Gini coefficient function corresponding to the feature integration algorithm;

[0023] Calculating the feature information gain of the feature subset by using the information gain function corresponding to the feature integration algorithm;

[0024] According to the feature F1 score, the feature Gini coefficient and the feature information gain, the feature score of the feature subset is calculated using the following formula:

[0025] Score(fi)=F_score(fi)+Gini(fi)+Information Gain(fi)

[0026] Among them, Score(fi) represents the F1 score of the i-th feature in the feature subset, fi represents the i-th feature in the feature subset, F_score(fi) represents the feature Gini coefficient of the i-th feature in the feature subset, and Information Gain(fi) represents the feature information gain of the i-th feature in the feature subset;

[0027] Based on the feature scores, target features in the feature subset are extracted.

[0028] Optionally, analyzing the emergency coefficient of the potential electric energy demand includes:

[0029] Analyzing emergency scenario characteristics of the emergency scenario corresponding to the potential electric energy demand;

[0030] Analyzing the dynamic emergency factors of the potential electric energy demand according to the characteristics of the emergency scenario;

[0031] Based on the dynamic emergency factors, constructing a dynamic analysis model of the emergency of the potential electric energy demand;

[0032] Analyzing the model environment adaptability coefficient of the dynamic analysis model;

[0033] When the model environmental adaptability coefficient meets a preset environmental adaptability threshold, the dynamic analysis model is used to analyze the emergency coefficient of the potential electric energy demand.

[0034] Optionally, determining the pre-charged battery module for the emergency scenario based on the emergency response signal includes:

[0035] Analyzing the battery performance requirements of the emergency scenario based on the emergency response signal;

[0036] Analyze available battery modules for the emergency scenario;

[0037] Obtaining battery parameters of the available battery module;

[0038] Analyzing the battery performance of the available battery module based on the battery parameters;

[0039] Determining a target battery module for the emergency scenario according to the battery performance and the battery performance requirement;

[0040] A pre-charge state of the target battery module is identified, and a pre-charge battery module among the target battery modules is determined based on the pre-charge state.

[0041] Optionally, analyzing the battery performance of the available battery module based on the battery parameter includes:

[0042] According to the battery parameters, determine the battery capacity and output power of the available battery module under ideal conditions, that is, when the temperature coefficient, the charge and discharge rate coefficient and the aging coefficient are 1;

[0043] Based on the battery parameters, analyzing the temperature coefficient, charge and discharge rate coefficient, and aging coefficient of the available battery module;

[0044] The battery charge and discharge efficiency of the available battery module is calculated using the following formula using the temperature coefficient, charge and discharge rate coefficient, and aging coefficient:

[0045] D=((S a / r) / (S c / r))*(1-ω)*(1-e)*(1-μ)×100%

[0046] Where D represents the battery charge and discharge efficiency of the available battery module, S a represents the actual charging energy of the available battery module, r represents the discharged energy of the available battery module, S c It represents the theoretical chargeable energy of the available battery module, ω represents the temperature coefficient, e represents the charge and discharge rate coefficient, and μ represents the aging coefficient;

[0047] The battery performance of the available battery modules is analyzed according to the battery capacity, the output power, and the battery charge and discharge efficiency.

[0048] Optionally, the constructing of the emergency battery assembly model of the pre-charged battery module includes:

[0049] Determining an assembly method of the pre-charged battery module, wherein the assembly method includes series connection and parallel connection;

[0050] Analyzing the assembly power index of the assembly method;

[0051] Constructing an assembly objective function and battery constraints of the pre-charged battery module;

[0052] An emergency battery assembly model of the pre-charged battery module is constructed according to the assembly objective function, the battery constraint conditions, the assembly power index and the assembly method.

[0053] Optionally, according to the emergency response time requirement corresponding to the potential electric energy demand, activating the target emergency battery pack using a preset fast activation function to obtain an activated emergency battery pack includes:

[0054] Analyzing the activation stage of the target emergency battery pack according to the emergency response time requirement corresponding to the potential power demand;

[0055] marking an activation time threshold of the activation phase;

[0056] According to the activation stage and the activation time threshold, the activation time of the target emergency battery pack is calculated using the rapid activation function; wherein the rapid activation function:

[0057]

[0058] Among them, T ac represents the activation time of the target emergency battery pack, Q represents the rapid activation stage in the activation stage, A represents the stage from the rapid activation stage to the stable activation stage in the activation stage, P represents the stable activation stage in the activation stage, G represents the stage from the stable activation stage to the stop activation stage in the activation stage, t1 represents the initial time of the rapid activation stage, t2 represents the initial time of the stable activation stage, t3 represents the end time of the stable activation stage, t4 represents the time when the target emergency battery pack stops activating, and t represents the duration of the activation of the target emergency battery pack;

[0059] According to the activation time, the target emergency battery pack is activated using a preset fast activation function to obtain an activated emergency battery pack.

[0060] Optionally, the analyzing the dynamic output power of the activated emergency battery group by using the dynamic load balancing algorithm based on the potential power demand includes:

[0061] Analyzing the status parameters of the activated emergency battery pack;

[0062] Analyzing a set of power output parameters of the activated emergency battery pack based on the state parameters and the potential power demand;

[0063] Calculating the balancing response coefficient of the electric energy output parameter group corresponding to the electric energy output parameter group set by using the dynamic load balancing algorithm;

[0064] Determining a target electric energy output parameter group of the electric energy output parameter group according to the balanced response coefficient;

[0065] Based on the target power output parameter group, the dynamic output power of the activated emergency battery group is analyzed.

[0066] In order to solve the above problems, the present invention also provides an emergency battery pack power dynamic supply system based on emergency response time, the system comprising:

[0067] A potential power demand analysis module, used to collect emergency scenario data of an emergency scenario using an integrated communication system, construct an power analysis model of the emergency scenario based on the emergency scenario data, and analyze the potential power demand of the emergency scenario using the power analysis model;

[0068] An emergency response signal generating module, used for analyzing the emergency coefficient of the potential power demand, and constructing an emergency response signal of the emergency scenario according to the potential power demand when the emergency coefficient is greater than the preset emergency threshold;

[0069] An emergency battery pack construction module is used to determine the pre-charged battery module of the emergency scenario based on the emergency response signal, construct an emergency battery assembly model of the pre-charged battery module, and connect the pre-charged battery module according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery module includes physical connection and electrical connection;

[0070] An emergency battery pack activation module is used to analyze the electrical fault of the emergency battery pack. When the electrical fault meets a preset electrical fault threshold, the emergency battery pack is used as the target emergency battery pack of the emergency scenario, and according to the emergency response time requirement corresponding to the potential power demand, the target emergency battery pack is activated using a preset fast activation function to obtain an activated emergency battery pack;

[0071] The dynamic power supply module is used to construct a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scene, and based on the potential power demand, use the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack, and perform dynamic power supply for the emergency scene based on the dynamic output power.

[0072] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0073] at least one processor; and,

[0074] a memory communicatively connected to the at least one processor; wherein,

[0075] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned emergency battery pack dynamic energy supply method based on emergency response time.

[0076] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned emergency battery pack dynamic energy supply method based on emergency response time.

[0077] Compared with the problems described in the background technology, the present invention can construct an automatic and adaptive learning electric energy analysis model for emergency scenarios by constructing an electric energy analysis model for the emergency scenarios. The model can automatically adapt to the ever-changing environment and data, and provide strong support for emergency response. Furthermore, the present invention uses the electric energy analysis model to analyze the potential electric energy demand of the emergency scenario, which can provide a basis for optimizing resource allocation in the later stage and improve rescue efficiency. Furthermore, the present invention can timely determine whether an emergency is needed for the emergency scenario by analyzing the emergency coefficient of the potential electric energy demand, thereby improving the emergency efficiency. Furthermore, the present invention determines that the pre-charged battery module of the emergency scenario based on the emergency response signal can provide hardware guarantee for the later electric energy emergency. Furthermore, the present invention constructs the The emergency battery assembly model of the pre-charged battery module can construct an emergency battery assembly model of the pre-charged battery module that meets the needs of the emergency scene, ensuring that the power demand can be responded to quickly and effectively in an emergency. Next, the present invention can comprehensively analyze the electrical faults of the emergency battery pack by analyzing the electrical faults of the emergency battery pack, ensuring that the battery pack can work stably and reliably in an emergency. Finally, the present invention constructs the dynamic load balancing algorithm of the activated emergency battery pack and the emergency equipment corresponding to the emergency scene, which can automatically adjust the output of the battery pack according to the changes in the needs of the emergency equipment, ensuring the reasonable distribution and utilization of electric energy, and based on the potential power demand, the dynamic load balancing algorithm is used to analyze the dynamic output power of the activated emergency battery pack to ensure that the power demand is met in the emergency scene. Therefore, the present invention can improve the emergency efficiency of emergency scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A schematic flow chart of a method for dynamically supplying power from an emergency battery pack based on emergency response time provided by an embodiment of the present invention;

[0079] Figure 2 A functional module diagram of an emergency battery pack power dynamic supply system based on emergency response time provided by an embodiment of the present invention;

[0080] Figure 3 A schematic diagram of the structure of an electronic device of an emergency battery pack power dynamic supply system based on emergency response time provided by an embodiment of the present invention;

[0081] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0082] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0083] The embodiment of the present application provides a method for dynamically supplying power to an emergency battery pack based on emergency response time. The execution subject of the method for dynamically supplying power to an emergency battery pack based on emergency response time includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for dynamically supplying power to an emergency battery pack based on emergency response time can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0084] Embodiment 1:

[0085] Reference Figure 1 FIG. 1 is a flow chart of a method for dynamically supplying power of an emergency battery pack based on emergency response time according to an embodiment of the present invention. In this embodiment, the method for dynamically supplying power of an emergency battery pack based on emergency response time includes:

[0086] S1. Collect emergency scenario data of emergency scenarios using an integrated communication system, construct an electric energy analysis model of the emergency scenarios based on the emergency scenario data, and analyze potential electric energy demand of the emergency scenarios using the electric energy analysis model.

[0087] It should be explained that the integrated communication system refers to a comprehensive system that integrates multiple communication technologies, equipment and networks to achieve efficient and reliable information transmission and exchange, and the emergency scenario data refers to various information and parameters related to specific emergency situations, including environmental data, equipment data, infrastructure data, historical emergency data, etc. The power analysis model refers to a calculation model that can predict and evaluate the power demand in specific emergency situations based on the input emergency scenario data.

[0088] The present invention can construct an automatic and adaptive learning emergency scenario power analysis model by constructing the power analysis model of the emergency scenario. The model can automatically adapt to the changing environment and data and provide strong support for emergency response.

[0089] In detail, constructing the power analysis model of the emergency scenario according to the emergency scenario data includes:

[0090] Preprocessing the emergency scene data to obtain preprocessed scene data;

[0091] Constructing an automated feature extraction algorithm for the preprocessed scene data;

[0092] Extracting a feature subset of the preprocessed scene data based on the automated feature extraction algorithm;

[0093] Integrate the feature subsets using a preset feature integration algorithm to obtain target features;

[0094] Determining an adaptive model for power analysis of the emergency scenario;

[0095] Using the target features to train the electric energy analysis adaptive model to obtain a trained electric energy analysis adaptive model;

[0096] Analyzing the power demand analysis performance of the trained power analysis adaptive model;

[0097] When the power demand analysis performance meets a preset analysis performance threshold, the trained power analysis adaptive model is used as the power analysis model for the emergency scenario.

[0098] Among them, the pre-processed scene data refers to the original emergency scene data that has been pre-processed (such as cleaning, normalization, standardization, etc.), the automatic feature extraction algorithm refers to an automatic encoder algorithm that can automatically identify and extract features useful for power demand prediction, the feature subset refers to a set of features extracted from the pre-processed scene data, and these features are considered to be the most important for the prediction target (power demand), the feature integration algorithm refers to an algorithm that can combine the results of multiple feature extraction algorithms to generate new features, the target feature refers to the final feature set generated by the feature integration algorithm, and these features will be used for training and prediction of the power analysis model, the power analysis adaptive model refers to an adaptive neural network model that can automatically adapt to changes in different emergency scenarios, the trained power analysis adaptive model refers to a model obtained by training the power analysis adaptive model using the target feature, the power demand analysis performance refers to the accuracy and efficiency of the power analysis model in predicting power demand, and the analysis performance threshold refers to a preset power demand analysis performance standard. Only when the analysis performance of the model reaches or exceeds this threshold can the model be considered valid.

[0099] Furthermore, the step of integrating the feature subsets using a preset feature integration algorithm to obtain target features includes:

[0100] Calculating the feature F1 score of the feature subset using the F1 score function corresponding to the feature integration algorithm;

[0101] Calculate the feature Gini coefficient of the feature subset according to the Gini coefficient function corresponding to the feature integration algorithm;

[0102] Calculating the feature information gain of the feature subset by using the information gain function corresponding to the feature integration algorithm;

[0103] According to the feature F1 score, the feature Gini coefficient and the feature information gain, the feature score of the feature subset is calculated using the following formula:

[0104] Score(fi)=F_score(fi)+Gini(fi)+Information Gain(fi)

[0105] Among them, Score(fi) represents the F1 score of the i-th feature in the feature subset, fi represents the i-th feature in the feature subset, F_score(fi) represents the feature Gini coefficient of the i-th feature in the feature subset, and Information Gain(fi) represents the feature information gain of the i-th feature in the feature subset;

[0106] Based on the feature scores, target features in the feature subset are extracted.

[0107] Among them, the F1 score function refers to a function for calculating the feature F1 score, and the feature F1 score refers to a parameter reflecting the performance of the feature in the classification task. The Gini coefficient function refers to a function for calculating the feature Gini coefficient, and the feature Gini coefficient refers to a function reflecting the degree of inequality in the distribution of feature values. The information gain function refers to a function for calculating the feature information gain, and the feature information gain refers to a function reflecting the degree of information contribution of the feature to the target variable. The feature score refers to a comprehensive score of a feature subset calculated based on the feature F1 score, the feature Gini coefficient and the feature information gain.

[0108] The present invention uses the electric energy analysis model to analyze the potential electric energy demand of the emergency scene, which can provide a basis for optimizing resource allocation in the later stage and improve rescue efficiency. The potential electric energy demand refers to the electric energy demand for a period of time in the future predicted based on the collected emergency scene data and the electric energy analysis model in a specific emergency scene, including the predicted electric energy demand, demand time distribution, peak demand, etc.

[0109] S2. Analyze the emergency coefficient of the potential power demand, and when the emergency coefficient is greater than the preset emergency threshold, construct an emergency response signal for the emergency scenario according to the potential power demand.

[0110] The present invention can timely determine whether emergency response is needed for an emergency scenario by analyzing the emergency coefficient of the potential electric energy demand, thereby improving emergency response efficiency.

[0111] In detail, the analysis of the emergency coefficient of the potential electric energy demand includes:

[0112] Analyzing emergency scenario characteristics of the emergency scenario corresponding to the potential electric energy demand;

[0113] Analyzing the dynamic emergency factors of the potential electric energy demand according to the characteristics of the emergency scenario;

[0114] Based on the dynamic emergency factors, constructing a dynamic analysis model of the emergency of the potential electric energy demand;

[0115] Analyzing the model environment adaptability coefficient of the dynamic analysis model;

[0116] When the model environmental adaptability coefficient meets a preset environmental adaptability threshold, the dynamic analysis model is used to analyze the emergency coefficient of the potential electric energy demand.

[0117] Among them, the emergency scenario characteristics refer to various features and conditions related to specific emergency scenarios, such as the type of emergency and the size of the emergency area. The dynamic emergency factors refer to factors that change over time in emergency scenarios, which will affect changes in electricity demand. Dynamic emergency factors are key driving factors for changes in electricity demand, such as crowd evacuation, equipment damage, changes in environmental conditions and other factors. The dynamic analysis model refers to a mathematical model that can simulate and predict changes in electricity demand over time. The model environmental adaptability coefficient refers to the ability of the model to maintain prediction accuracy under different emergency scenarios and environmental conditions. The environmental adaptability threshold refers to a preset model environmental adaptability coefficient standard. Only when the model's environmental adaptability coefficient reaches or exceeds this threshold, the model is considered to be highly adaptable. The emergency coefficient refers to a parameter used to assess the urgency of electricity demand.

[0118] Optionally, the dynamic analysis model for emergency of potential electric energy demand based on the dynamic emergency factor can be trained by using historical factor data and real-time factor data of the dynamic emergency factor to predict and adjust the dynamic analysis model for electric energy demand according to changes in the dynamic emergency factor.

[0119] Optionally, the analyzing the model environment adaptability coefficient of the dynamic analysis model may be performed by calculating an average accuracy of model prediction of the dynamic analysis model under a variety of different environmental conditions.

[0120] The present invention constructs the emergency response signal of the emergency scenario according to the potential power demand, and can send emergency instructions in time, providing a basis for the subsequent emergency plan. Among them, the emergency response signal refers to a signal or instruction used to indicate or remind emergency response actions in an emergency situation.

[0121] S3. Based on the emergency response signal, determine the pre-charged battery module for the emergency scenario, construct an emergency battery assembly model of the pre-charged battery module, and connect the pre-charged battery module according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery module includes physical connection and electrical connection.

[0122] The present invention determines the pre-charged battery module of the emergency scene based on the emergency response signal to provide hardware guarantee for the later power emergency. The pre-charged battery module refers to a battery module that is fully charged in advance and can meet emergency needs.

[0123] In detail, the determining of the pre-charged battery module for the emergency scenario based on the emergency response signal includes:

[0124] Analyzing the battery performance requirements of the emergency scenario based on the emergency response signal;

[0125] Analyze available battery modules for the emergency scenario;

[0126] Obtaining battery parameters of the available battery module;

[0127] Analyzing the battery performance of the available battery module based on the battery parameters;

[0128] Determining a target battery module for the emergency scenario according to the battery performance and the battery performance requirement;

[0129] A pre-charge state of the target battery module is identified, and a pre-charge battery module among the target battery modules is determined based on the pre-charge state.

[0130] Among them, the battery performance requirement refers to the specific performance requirements of the battery module in the emergency scenario, such as battery capacity, output power, charging and discharging efficiency, etc. The available battery module refers to the set of battery modules available in the emergency scenario. The battery parameters refer to the specific technical specifications and performance indicators of the battery module. The battery performance refers to the performance of the battery module in actual use. The target battery module refers to the battery module finally determined for the emergency scenario among all available battery modules based on the battery performance requirements and battery performance analysis results. The pre-charge status refers to whether the battery module has been pre-charged before the emergency response begins.

[0131] Further, analyzing the battery performance of the available battery module based on the battery parameters includes:

[0132] According to the battery parameters, determine the battery capacity and output power of the available battery module under ideal conditions, that is, when the temperature coefficient, the charge and discharge rate coefficient and the aging coefficient are 1;

[0133] Based on the battery parameters, analyzing the temperature coefficient, charge and discharge rate coefficient, and aging coefficient of the available battery module;

[0134] The battery charge and discharge efficiency of the available battery module is calculated using the following formula using the temperature coefficient, charge and discharge rate coefficient, and aging coefficient:

[0135] D=((S a / r) / (S c / r))*(1-ω)*(1-e)*(1-μ)×100%

[0136] Where D represents the battery charge and discharge efficiency of the available battery module, S a represents the actual charging energy of the available battery module, r represents the discharged energy of the available battery module, S c It represents the theoretical chargeable energy of the available battery module, ω represents the temperature coefficient, e represents the charge and discharge rate coefficient, and μ represents the aging coefficient;

[0137] The battery performance of the available battery modules is analyzed according to the battery capacity, the output power, and the battery charge and discharge efficiency.

[0138] Among them, the battery capacity refers to the amount of electrical energy that the battery can store when fully charged, the output power refers to the maximum electrical power that the battery can provide within a certain period of time, the temperature coefficient refers to the ratio of battery performance to temperature change, the charge and discharge rate coefficient refers to the ratio of battery performance to charge and discharge rate change, the aging coefficient refers to the ratio of battery performance to usage time change, the actual charged energy refers to the energy actually obtained by the battery during the actual charging process, the released energy refers to the energy actually released by the battery during the actual discharging process, and the theoretically chargeable energy refers to the energy that can be charged under ideal conditions, that is, when the temperature coefficient, charge and discharge rate coefficient and aging coefficient are 1.

[0139] The present invention constructs an emergency battery assembly model of the pre-charged battery module to construct an emergency battery assembly model of the pre-charged battery module that meets the needs of emergency scenarios, ensuring that the power demand can be responded to quickly and effectively in emergency situations.

[0140] In detail, the construction of the emergency battery assembly model of the pre-charged battery module includes:

[0141] Determining an assembly method of the pre-charged battery module, wherein the assembly method includes series connection and parallel connection;

[0142] Analyzing the assembly power index of the assembly method;

[0143] Constructing an assembly objective function and battery constraints of the pre-charged battery module;

[0144] An emergency battery assembly model of the pre-charged battery module is constructed according to the assembly objective function, the battery constraint conditions, the assembly power index and the assembly method.

[0145] Among them, the series connection refers to the process of connecting the positive electrodes and negative electrodes of multiple battery modules together to increase the voltage of the battery pack, the parallel connection refers to the process of connecting the positive electrodes and negative electrodes of multiple battery modules together to increase the current of the battery pack, the assembly power index refers to the power performance index of the battery pack formed after the battery is assembled, the assembly objective function refers to the goal that needs to be optimized in the battery assembly process, including maximizing output power, minimizing cost, maximizing service life, etc., the battery constraint condition refers to the conditions that need to be met in the battery assembly process, such as battery capacity limit, battery voltage limit, battery current limit, etc., and the emergency battery assembly model refers to a mathematical model constructed according to the objective function, constraint conditions, power index and assembly method of battery assembly.

[0146] Optionally, the assembly objective function for constructing the pre-charged battery module can be constructed by integrating and comprehensively optimizing linear programming, integer programming, and nonlinear programming.

[0147] The present invention connects the pre-charged battery modules according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery modules includes physical connection and electrical connection to ensure that the power demand can be responded to quickly and effectively in an emergency. The emergency battery pack refers to a battery pack that constructs a pre-charged battery module that meets the needs of emergency scenarios. The physical connection refers to the use of physical connection elements such as connectors, terminals, bolts, etc. dedicated to the battery module to fix the battery modules together in a designed series and parallel manner, and the electrical connection refers to the use of electrical connection elements such as wires, cables, insulating materials, etc., according to the designed circuit diagram, The positive and negative poles of the battery module are electrically connected to the control circuit.

[0148] S4. Analyze the electrical fault of the emergency battery pack. When the electrical fault meets a preset electrical fault threshold, use the emergency battery pack as the target emergency battery pack for the emergency scenario, and activate the target emergency battery pack using a preset fast activation function according to the emergency response time requirement corresponding to the potential power demand to obtain an activated emergency battery pack.

[0149] The present invention can comprehensively analyze the electrical faults of the emergency battery pack by analyzing the electrical faults of the emergency battery pack, ensuring that the battery pack can work stably and reliably in an emergency. The electrical fault refers to the phenomenon that the circuit or equipment in the electrical system cannot work normally or works abnormally due to various reasons, such as short circuit, open circuit, poor contact and the like. In detail, the electrical fault can be analyzed by simulating the operation data collection of the emergency battery pack. The electrical fault threshold refers to the threshold for judging whether the electrical fault reaches the fault standard. The target emergency battery pack refers to a battery pack that meets the emergency work requirements and can work normally.

[0150] Optionally, according to the emergency response time requirement corresponding to the potential power demand, the target emergency battery pack is activated using a preset fast activation function, and the activated emergency battery pack can quickly activate the target emergency battery pack to ensure power support is provided within the emergency response time requirement.

[0151] In detail, according to the emergency response time requirement corresponding to the potential electric energy demand, the target emergency battery pack is activated using a preset fast activation function to obtain an activated emergency battery pack, including:

[0152] Analyzing the activation stage of the target emergency battery pack according to the emergency response time requirement corresponding to the potential power demand;

[0153] marking an activation time threshold of the activation phase;

[0154] According to the activation stage and the activation time threshold, the activation time of the target emergency battery pack is calculated using the rapid activation function; wherein the rapid activation function:

[0155]

[0156] Among them, T ac represents the activation time of the target emergency battery pack, Q represents the rapid activation stage in the activation stage, A represents the stage from the rapid activation stage to the stable activation stage in the activation stage, P represents the stable activation stage in the activation stage, G represents the stage from the stable activation stage to the stop activation stage in the activation stage, t1 represents the initial time of the rapid activation stage, t2 represents the initial time of the stable activation stage, t3 represents the end time of the stable activation stage, t4 represents the time when the target emergency battery pack stops activating, and t represents the duration of the activation of the target emergency battery pack;

[0157] According to the activation time, the target emergency battery pack is activated using a preset fast activation function to obtain an activated emergency battery pack.

[0158] Among them, the rapid activation function refers to a function used to describe the process of the battery pack quickly reaching a normal power supply state from a dormant state or an unactivated state, the rapid activation stage is the stage where the battery pack quickly reaches an initial activation state from a static state, the stable activation stage refers to the duration of stable activation, and the activation time refers to the time required for the battery pack to go from a static state to a fully activated state and stop activation.

[0159] S5. Construct a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scenario, and based on the potential power demand, use the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack, and perform dynamic power supply for the emergency scenario based on the dynamic output power.

[0160] The present invention constructs a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scene, which can automatically adjust the output of the battery pack according to the changes in the demand of the emergency equipment to ensure the reasonable distribution and utilization of electric energy. The dynamic load balancing algorithm refers to an algorithm that can dynamically adjust the output of the battery pack to meet the electric energy demand of the emergency equipment.

[0161] Optionally, the present invention uses the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack based on the potential power demand to ensure that the power demand is met in an emergency scenario.

[0162] In detail, the method of analyzing the dynamic output power of the activated emergency battery group based on the potential power demand by using the dynamic load balancing algorithm includes:

[0163] Analyzing the status parameters of the activated emergency battery pack;

[0164] Analyzing a set of power output parameters of the activated emergency battery pack based on the state parameters and the potential power demand;

[0165] Calculating the balancing response coefficient of the electric energy output parameter group corresponding to the electric energy output parameter group set by using the dynamic load balancing algorithm;

[0166] Determining a target electric energy output parameter group of the electric energy output parameter group according to the balanced response coefficient;

[0167] Based on the target power output parameter group, the dynamic output power of the activated emergency battery group is analyzed.

[0168] Among them, the state parameters refer to various parameters that describe the current state of the battery pack, such as voltage, current, temperature, remaining power, etc.; the power output parameter group set refers to a combination of power parameters that may be output by the battery pack at different time points, including voltage, current, power, etc.; the balancing response coefficient refers to a coefficient used to evaluate the degree of response of each power output parameter group to potential power demand in the dynamic load balancing algorithm; the dynamic load balancing algorithm refers to an algorithm that can dynamically adjust the output power parameters of the battery pack according to changes in power demand; the target power output parameter group refers to a combination of power output parameters calculated according to the dynamic load balancing algorithm that can most effectively meet the potential power demand; the dynamic output power refers to the power output of the battery pack at different time points in an emergency scenario according to the target power output parameter group.

[0169] Optionally, the use of the dynamic load balancing algorithm to calculate the balancing response coefficient of the electric energy output parameter group corresponding to the electric energy output parameter group set can encode the electric energy output parameter group as a chromosome of a genetic algorithm, and iteratively optimize the electric energy output parameter group through operations such as selection, crossover and mutation to obtain the parameter group with the highest balancing response coefficient.

[0170] Compared with the problems described in the background technology, the present invention can construct an automatic and adaptive learning electric energy analysis model for emergency scenarios by constructing an electric energy analysis model for the emergency scenarios. The model can automatically adapt to the ever-changing environment and data, and provide strong support for emergency response. Furthermore, the present invention uses the electric energy analysis model to analyze the potential electric energy demand of the emergency scenario, which can provide a basis for optimizing resource allocation in the later stage and improve rescue efficiency. Furthermore, the present invention can timely determine whether an emergency is needed for the emergency scenario by analyzing the emergency coefficient of the potential electric energy demand, thereby improving the emergency efficiency. Furthermore, the present invention determines that the pre-charged battery module of the emergency scenario based on the emergency response signal can provide hardware guarantee for the later electric energy emergency. Furthermore, the present invention constructs the The emergency battery assembly model of the pre-charged battery module can construct an emergency battery assembly model of the pre-charged battery module that meets the needs of the emergency scene, ensuring that the power demand can be responded to quickly and effectively in an emergency. Next, the present invention can comprehensively analyze the electrical faults of the emergency battery pack by analyzing the electrical faults of the emergency battery pack, ensuring that the battery pack can work stably and reliably in an emergency. Finally, the present invention constructs the dynamic load balancing algorithm of the activated emergency battery pack and the emergency equipment corresponding to the emergency scene, which can automatically adjust the output of the battery pack according to the changes in the needs of the emergency equipment, ensuring the reasonable distribution and utilization of electric energy, and based on the potential power demand, the dynamic load balancing algorithm is used to analyze the dynamic output power of the activated emergency battery pack to ensure that the power demand is met in the emergency scene. Therefore, the present invention can improve the emergency efficiency of emergency scenes.

[0171] Embodiment 2:

[0172] like Figure 2 , which is a functional module diagram of an emergency battery pack power dynamic supply system based on emergency response time provided by an embodiment of the present invention.

[0173] The emergency battery pack power dynamic supply system 200 based on emergency response time of the present invention can be installed in an electronic device. According to the functions to be implemented, the emergency battery pack power dynamic supply system 200 based on emergency response time can include a potential power demand analysis module 201, an emergency response signal generation module 202, an emergency battery pack construction module 203, an emergency battery pack activation module 204 and a power dynamic supply module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0174] In this embodiment, the functions of each module / unit are as follows:

[0175] The potential power demand analysis module 201 is used to collect emergency scenario data of the emergency scenario using the integrated communication system, build an electric energy analysis model of the emergency scenario according to the emergency scenario data, and analyze the potential power demand of the emergency scenario using the electric energy analysis model;

[0176] The emergency response signal generating module 202 is used to analyze the emergency coefficient of the potential power demand, and when the emergency coefficient is greater than the preset emergency threshold, construct an emergency response signal of the emergency scenario according to the potential power demand;

[0177] The emergency battery pack construction module 203 is used to determine the pre-charged battery module of the emergency scenario based on the emergency response signal, construct an emergency battery assembly model of the pre-charged battery module, and connect the pre-charged battery module according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery module includes physical connection and electrical connection;

[0178] The emergency battery pack activation module 204 is used to analyze the electrical fault of the emergency battery pack. When the electrical fault meets the preset electrical fault threshold, the emergency battery pack is used as the target emergency battery pack of the emergency scenario, and according to the emergency response time requirement corresponding to the potential power demand, the target emergency battery pack is activated by using a preset fast activation function to obtain an activated emergency battery pack;

[0179] The dynamic power supply module 205 is used to construct a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scenario, and based on the potential power demand, use the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack, and perform dynamic power supply for the emergency scenario based on the dynamic output power.

[0180] In detail, the modules described in the emergency battery pack power dynamic supply system 200 based on emergency response time described in the embodiment of the present invention adopt the same technical means as the emergency battery pack power dynamic supply method based on emergency response time described in the accompanying drawings when in use, and can produce the same technical effects, which will not be repeated here.

[0181] An embodiment of the present invention provides an electronic device for implementing a method for dynamically supplying electric energy of an emergency battery pack based on emergency response time.

[0182] See also Figure 3 As shown, the electronic device may include a processor 30, a memory 31, a communication bus 32 and a communication interface 33, and may also include a computer program stored in the memory 31 and executable on the processor 30, such as a method program for dynamically supplying power to an emergency battery pack based on emergency response time.

[0183] In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory (for example, executing a dynamic power supply program for emergency battery packs based on emergency response time, etc.), and calls data stored in the memory to execute various functions of the electronic device and process data.

[0184] The memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory can also include both an internal storage unit of the electronic device and an external storage device. The memory can not only be used to store application software and various types of data installed in the electronic device, such as the code of the emergency battery pack power dynamic supply program based on the emergency response time, but also can be used to temporarily store data that has been output or is to be output.

[0185] The communication bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory and at least one processor, etc.

[0186] The communication interface is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0187] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0188] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0189] The emergency battery pack power dynamic supply program based on the emergency response time stored in the memory of the electronic device is a combination of multiple instructions, and when executed in the processor, it can achieve:

[0190] Collect emergency scenario data of the emergency scenario using the integrated communication system, construct an electric energy analysis model of the emergency scenario based on the emergency scenario data, and analyze the potential electric energy demand of the emergency scenario using the electric energy analysis model;

[0191] Analyzing the emergency coefficient of the potential power demand, and when the emergency coefficient is greater than the preset emergency threshold, constructing an emergency response signal of the emergency scenario according to the potential power demand;

[0192] Based on the emergency response signal, determine the pre-charged battery module of the emergency scenario, construct an emergency battery assembly model of the pre-charged battery module, and connect the pre-charged battery module according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery module includes physical connection and electrical connection;

[0193] Analyze the electrical fault of the emergency battery pack, and when the electrical fault meets a preset electrical fault threshold, use the emergency battery pack as the target emergency battery pack of the emergency scenario, and activate the target emergency battery pack using a preset fast activation function according to the emergency response time requirement corresponding to the potential electric energy demand, to obtain an activated emergency battery pack;

[0194] Construct a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scenario, and based on the potential power demand, use the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack, and execute dynamic power supply for the emergency scenario based on the dynamic output power.

[0195] Specifically, the specific implementation method of the processor for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0196] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0197] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0198] Collect emergency scenario data of the emergency scenario using the integrated communication system, construct an electric energy analysis model of the emergency scenario based on the emergency scenario data, and analyze the potential electric energy demand of the emergency scenario using the electric energy analysis model;

[0199] Analyzing the emergency coefficient of the potential power demand, and when the emergency coefficient is greater than the preset emergency threshold, constructing an emergency response signal of the emergency scenario according to the potential power demand;

[0200] Based on the emergency response signal, determine the pre-charged battery module of the emergency scenario, construct an emergency battery assembly model of the pre-charged battery module, and connect the pre-charged battery module according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery module includes physical connection and electrical connection;

[0201] Analyze the electrical fault of the emergency battery pack, and when the electrical fault meets a preset electrical fault threshold, use the emergency battery pack as the target emergency battery pack of the emergency scenario, and activate the target emergency battery pack using a preset fast activation function according to the emergency response time requirement corresponding to the potential electric energy demand, to obtain an activated emergency battery pack;

[0202] Construct a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scenario, and based on the potential power demand, use the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack, and execute dynamic power supply for the emergency scenario based on the dynamic output power.

[0203] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0204] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0205] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0206] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0207] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0208] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0209] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for dynamically supplying power from an emergency battery pack based on emergency response time, characterized in that: The method comprises: Collect emergency scenario data of the emergency scenario using the integrated communication system, construct an electric energy analysis model of the emergency scenario based on the emergency scenario data, and analyze the potential electric energy demand of the emergency scenario using the electric energy analysis model; Analyzing the emergency coefficient of the potential power demand, and when the emergency coefficient is greater than a preset emergency threshold, constructing an emergency response signal of the emergency scenario according to the potential power demand; Based on the emergency response signal, determine the pre-charged battery module of the emergency scenario, construct an emergency battery assembly model of the pre-charged battery module, and connect the pre-charged battery module according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery module includes physical connection and electrical connection; Analyze the electrical fault of the emergency battery pack, and when the electrical fault meets the preset electrical fault threshold, use the emergency battery pack as the target emergency battery pack of the emergency scenario, and activate the target emergency battery pack using a preset fast activation function according to the emergency response time requirement corresponding to the potential power demand to obtain an activated emergency battery pack, wherein the activation of the target emergency battery pack using a preset fast activation function according to the emergency response time requirement corresponding to the potential power demand to obtain an activated emergency battery pack includes: analyzing the activation stage of the target emergency battery pack according to the emergency response time requirement corresponding to the potential power demand, marking the activation time threshold of the activation stage, and calculating the activation time of the target emergency battery pack using the fast activation function according to the activation stage and the activation time threshold; wherein the fast activation function: Among them, T ac represents the activation time of the target emergency battery pack, Q represents the rapid activation stage in the activation stage, A represents the stage from the rapid activation stage to the stable activation stage in the activation stage, P represents the stable activation stage in the activation stage, G represents the stage from the stable activation stage to the stop activation stage in the activation stage, t1 represents the initial time of the rapid activation stage, t2 represents the initial time of the stable activation stage, t3 represents the end time of the stable activation stage, t4 represents the time when the target emergency battery pack stops activating, t represents the duration of the activation of the target emergency battery pack, according to the activation time, the target emergency battery pack is activated by using a preset rapid activation function to obtain an activated emergency battery pack; Construct a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scenario, and based on the potential power demand, use the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack, and execute dynamic power supply for the emergency scenario based on the dynamic output power.

2. The method for dynamic power supply of emergency battery pack based on emergency response time as claimed in claim 1, characterized in that: The step of constructing an electric energy analysis model for the emergency scenario according to the emergency scenario data includes: Preprocessing the emergency scene data to obtain preprocessed scene data; Constructing an automated feature extraction algorithm for the preprocessed scene data; Extracting a feature subset of the preprocessed scene data based on the automated feature extraction algorithm; Integrate the feature subsets using a preset feature integration algorithm to obtain target features; Determining an adaptive model for power analysis of the emergency scenario; Using the target features to train the electric energy analysis adaptive model to obtain a trained electric energy analysis adaptive model; Analyzing the power demand analysis performance of the trained power analysis adaptive model; When the power demand analysis performance meets a preset analysis performance threshold, the trained power analysis adaptive model is used as the power analysis model for the emergency scenario.

3. The method for dynamically supplying power from an emergency battery pack based on emergency response time as claimed in claim 2, characterized in that: The step of integrating the feature subsets using a preset feature integration algorithm to obtain target features includes: Calculating the feature F1 score of the feature subset using the F1 score function corresponding to the feature integration algorithm; Calculate the feature Gini coefficient of the feature subset according to the Gini coefficient function corresponding to the feature integration algorithm; Calculating the feature information gain of the feature subset by using the information gain function corresponding to the feature integration algorithm; According to the feature F1 score, the feature Gini coefficient and the feature information gain, the feature score of the feature subset is calculated using the following formula: Score(fi)=F_score(fi)+Gini(fi)+Information Gain(fi) Among them, Score(fi) represents the feature score of the feature subset, F_score(fi) represents the F1 score of the i-th feature in the feature subset, fi represents the i-th feature in the feature subset, Gini(fi) represents the feature Gini coefficient of the i-th feature in the feature subset, and Information Gain(fi) represents the feature information gain of the i-th feature in the feature subset; Based on the feature scores, target features in the feature subset are extracted.

4. The method for dynamically supplying power from an emergency battery pack based on emergency response time as claimed in claim 3, characterized in that: The analysis of the emergency coefficient of the potential electric energy demand includes: Analyzing emergency scenario characteristics of the emergency scenario corresponding to the potential electric energy demand; Analyzing the dynamic emergency factors of the potential electric energy demand according to the characteristics of the emergency scenario; Based on the dynamic emergency factors, constructing a dynamic analysis model of the emergency of the potential electric energy demand; Analyzing the model environment adaptability coefficient of the dynamic analysis model; When the model environmental adaptability coefficient meets a preset environmental adaptability threshold, the dynamic analysis model is used to analyze the emergency coefficient of the potential electric energy demand.

5. The method for dynamic power supply of emergency battery pack based on emergency response time as claimed in claim 4, characterized in that: The method of determining the pre-charged battery module for the emergency scenario based on the emergency response signal includes: Analyzing the battery performance requirements of the emergency scenario based on the emergency response signal; Analyze available battery modules for the emergency scenario; Obtaining battery parameters of the available battery module; Analyzing the battery performance of the available battery module based on the battery parameters; Determining a target battery module for the emergency scenario according to the battery performance and the battery performance requirement; A pre-charge state of the target battery module is identified, and a pre-charge battery module among the target battery modules is determined based on the pre-charge state.

6. The method for dynamically supplying power from an emergency battery pack based on emergency response time as claimed in claim 5, characterized in that: The analyzing the battery performance of the available battery module based on the battery parameters includes: According to the battery parameters, determine the battery capacity and output power of the available battery module under ideal conditions, that is, when the temperature coefficient, the charge and discharge rate coefficient and the aging coefficient are 1; Based on the battery parameters, analyzing the temperature coefficient, charge and discharge rate coefficient, and aging coefficient of the available battery module; The battery charge and discharge efficiency of the available battery module is calculated using the following formula using the temperature coefficient, charge and discharge rate coefficient, and aging coefficient: D=((S a / r) / (S c / r))*(1-ω)*(1-e)*(1-μ)×100% Where D represents the battery charge and discharge efficiency of the available battery module, S a represents the actual charging energy of the available battery module, r represents the discharged energy of the available battery module, S c It represents the theoretical chargeable energy of the available battery module, ω represents the temperature coefficient, e represents the charge and discharge rate coefficient, and μ represents the aging coefficient; The battery performance of the available battery modules is analyzed according to the battery capacity, the output power, and the battery charge and discharge efficiency.

7. The method for dynamically supplying power from an emergency battery pack based on emergency response time as claimed in claim 6, characterized in that: The emergency battery assembly model of the pre-charged battery module is constructed, comprising: Determining an assembly method of the pre-charged battery module, wherein the assembly method includes series connection and parallel connection; Analyzing the assembly power index of the assembly method; Constructing an assembly objective function and battery constraints of the pre-charged battery module; An emergency battery assembly model of the pre-charged battery module is constructed according to the assembly objective function, the battery constraint conditions, the assembly power index and the assembly method.

8. The method for dynamically supplying power from an emergency battery pack based on emergency response time as claimed in claim 7, characterized in that: The step of analyzing the dynamic output power of the activated emergency battery group based on the potential power demand by using the dynamic load balancing algorithm includes: Analyzing the status parameters of the activated emergency battery pack; Analyzing a set of power output parameters of the activated emergency battery pack based on the state parameters and the potential power demand; Calculating the balancing response coefficient of the electric energy output parameter group corresponding to the electric energy output parameter group set by using the dynamic load balancing algorithm; Determining a target electric energy output parameter group of the electric energy output parameter group according to the balanced response coefficient; Based on the target power output parameter group, the dynamic output power of the activated emergency battery group is analyzed.

9. An emergency battery pack power dynamic supply system based on emergency response time, characterized in that: The system is used to execute the method for dynamically supplying power of an emergency battery pack based on emergency response time as described in any one of claims 1 to 8, the system comprising: A potential power demand analysis module, used to collect emergency scenario data of an emergency scenario using an integrated communication system, construct an power analysis model of the emergency scenario based on the emergency scenario data, and analyze the potential power demand of the emergency scenario using the power analysis model; An emergency response signal generating module, used for analyzing the emergency coefficient of the potential power demand, and constructing an emergency response signal of the emergency scenario according to the potential power demand when the emergency coefficient is greater than a preset emergency threshold; An emergency battery pack construction module is used to determine the pre-charged battery module of the emergency scenario based on the emergency response signal, construct an emergency battery assembly model of the pre-charged battery module, and connect the pre-charged battery module according to the emergency battery assembly model to obtain an emergency battery pack, wherein the connection of the pre-charged battery module includes physical connection and electrical connection; An emergency battery pack activation module is used to analyze the electrical fault of the emergency battery pack. When the electrical fault meets a preset electrical fault threshold, the emergency battery pack is used as the target emergency battery pack of the emergency scenario, and according to the emergency response time requirement corresponding to the potential power demand, the target emergency battery pack is activated using a preset fast activation function to obtain an activated emergency battery pack; The dynamic power supply module is used to construct a dynamic load balancing algorithm for the activated emergency battery pack and the emergency equipment corresponding to the emergency scene, and based on the potential power demand, use the dynamic load balancing algorithm to analyze the dynamic output power of the activated emergency battery pack, and perform dynamic power supply for the emergency scene based on the dynamic output power.

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