Intelligent battery management methods, devices and storage media for power distribution cabinets

Abstract

This invention relates to the field of power distribution cabinet management technology, and more particularly to a method, device, and storage medium for intelligent battery management in power distribution cabinets. The method includes: periodically collecting battery parameters, load parameters, and grid parameters; performing feature analysis on the battery and load parameters to obtain load characteristics and load behavior indices; formulating power supply rules based on the battery parameters, load characteristics, and grid parameters, and constructing a power allocation strategy model based on the power supply rules, battery parameters, and load parameters to generate a power allocation strategy; controlling the allocation of power according to the power allocation strategy; and constructing a cyclic monitoring model based on the battery parameters to update the power allocation strategy generation process. This invention achieves precise allocation and management of the output power of the power distribution cabinet battery to each load.

Description

Technical Field

[0001] This invention relates to the field of power distribution cabinet management technology, and in particular to a method, device and storage medium for intelligent battery management in power distribution cabinets. Background Technology

[0002] As a key device in power systems for power distribution and control, the management of the internal batteries in switchgear directly affects power supply quality and system reliability. With the increasing number of various load devices and the growing complexity of electricity consumption behaviors, traditional battery management methods based on fixed rules are no longer sufficient to meet the demands for efficient, reliable, and intelligent operation. Against this backdrop, developing an intelligent management method capable of real-time system status sensing, dynamic power distribution optimization, and self-learning capabilities has become an important direction for improving the performance of switchgear systems.

[0003] Existing battery management methods for distribution cabinets largely rely on fixed rules or simple threshold controls, lacking comprehensive analysis of load dynamics and battery status. This results in insufficient flexibility in power allocation strategies, making it difficult to adapt to complex and changing operating environments. Furthermore, traditional methods often neglect the coordination between battery health and load priorities, easily leading to power outages for critical loads or excessive battery wear. Under conditions of sudden load changes or grid fluctuations, existing technologies frequently exhibit response lag or strategy failures, impacting overall system energy efficiency and operational reliability. Summary of the Invention

[0004] The purpose of this invention is to provide a method, device and storage medium for intelligent battery management in power distribution cabinets, so as to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for intelligent battery management in a power distribution cabinet includes:

[0007] Periodically collect battery parameters, load parameters, and grid parameters;

[0008] Characteristic analysis is performed on battery parameters and load parameters to obtain load characteristics and load behavior indices;

[0009] Power supply rules are formulated based on battery parameters, load characteristics, and grid parameters. A power allocation strategy model is then built based on the power supply rules, battery parameters, and load parameters to generate a power allocation strategy.

[0010] Control the distribution of electrical energy according to the power distribution strategy;

[0011] A cycle monitoring model is built based on battery parameters to update the generation process of the power allocation strategy.

[0012] Furthermore, the periodic power change rate is determined based on the power, which is the ratio of the absolute value of the power difference between the current acquisition cycle and the previous acquisition cycle to the power of the previous acquisition cycle. The load characteristics are analyzed based on the periodic power change rate and load priority. The expression for the load characteristics is: Load characteristics = Load priority parameter × (1 + Periodic power change rate). When the load priority is a critical load, the load priority parameter is set to 3; when the load priority is a normal load, the load priority parameter is set to 2; and when the load priority is an interruptible load, the load priority parameter is set to 1.

[0013] Furthermore, the number of load starts and stops within 10 minutes is counted as the recent start and stop count. The ratio of the standard deviation to the average power within 10 minutes is used as the power volatility. Based on the recent start and stop count and the power volatility, the load behavior index is determined. The expression for the load behavior index is: Load behavior index = Recent start and stop count × Start and stop weight + Power volatility × Volatility weight, where Start and stop weight + Volatility weight = 1.

[0014] Furthermore, energy efficiency score, lifespan score, and reliability score are determined based on battery parameters and load parameters. The total load power is the sum of the power parameters in the load parameters. The ratio of the total load power to the battery output power is the energy efficiency score. The lifespan score is determined based on the health status and internal resistance. The expression for the lifespan score is: Lifespan score = Health status - 0.1 × Internal resistance. The ratio of the power supply time of the critical load with load priority within one hour to one hour is the reliability score.

[0015] Furthermore, a comprehensive objective function is constructed based on power supply rules, energy efficiency score, lifespan score, and reliability score. The expression of the comprehensive objective function is: Comprehensive score = Energy efficiency weight × Energy efficiency score + Lifespan weight × Lifespan score + Reliability weight × Reliability score, where Energy efficiency weight + Lifespan weight + Reliability weight = 1.

[0016] Furthermore, when the power supply rule is not triggered, an exhaustive search method is adopted, and a step size of 5% is set to construct the solution space of the load power allocation ratio. The comprehensive objective function is solved in the solution space of the load power allocation ratio to obtain the comprehensive score. The power allocation ratio of the load with the highest comprehensive score is taken as the power allocation strategy.

[0017] Furthermore, the battery output power to each load is controlled according to the power distribution strategy. If the load characteristic is greater than 1, a soft start mechanism is adopted when controlling the battery output power to the current load. The soft start time is set to the preset base time × (1 + load behavior index). During the soft start time, the battery output power to the current load gradually increases.

[0018] Furthermore, the rate of change of the comprehensive score in adjacent cyclic monitoring cycles is calculated. The rate of change of the comprehensive score is calculated as: comprehensive score of the current cyclic monitoring cycle - comprehensive score of the previous cyclic monitoring cycle / comprehensive score of the previous cyclic monitoring cycle. If the rate of change of the comprehensive score in three consecutive cyclic monitoring cycles is all less than -0.05, the feature threshold will be reduced and the reliability weight will be increased.

[0019] On the other hand, the present invention also provides a smart battery management device for a power distribution cabinet, comprising:

[0020] The periodic acquisition module is used to periodically acquire battery parameters, load parameters, and grid parameters;

[0021] The feature analysis module is used to perform feature analysis on battery parameters and load parameters to obtain load characteristics and load behavior index;

[0022] The strategy generation module is used to formulate power supply rules based on battery parameters, load characteristics and grid parameters, and to build an allocation strategy model based on the power supply rules, battery parameters and load parameters to generate power allocation strategies.

[0023] The strategy execution module is used to control the distribution of power according to the power distribution strategy;

[0024] The cycle monitoring module is used to build a cycle monitoring model based on battery parameters to update the generation process of the power distribution strategy.

[0025] On the other hand, the present invention also provides a storage medium characterized in that it stores instructions that, when run on a computer, cause the computer to execute the intelligent battery management method for power distribution cabinets as described in any of the preceding claims.

[0026] The beneficial effects of this invention are as follows: Through periodic acquisition of multiple parameters, analysis of load characteristics and behavior, generation of multi-objective strategies, and dynamic monitoring feedback, precise allocation and optimized management of the battery output power of the distribution cabinet are achieved. This method can effectively improve system energy efficiency, extend battery life, ensure the power supply reliability of critical loads, and enhance the system's adaptability to load fluctuations and grid changes. Through soft-start and cyclic monitoring mechanisms, the stability and safety of system operation are further improved. It is applicable to various distribution cabinet battery management scenarios and has high practical value and promotion prospects. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of the intelligent battery management method for the power distribution cabinet in this embodiment.

[0029] Figure 2 This is a flowchart of the feature analysis method for battery parameters and load parameters in this embodiment.

[0030] Figure 3 This is a flowchart of the method for generating the power distribution strategy in this embodiment.

[0031] Figure 4 This is a schematic diagram of the intelligent battery management device for the power distribution cabinet in this embodiment. Detailed Implementation

[0032] The present invention discloses a method, apparatus, and storage medium for intelligent battery management in power distribution cabinets, which will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated; they can be combined to achieve better technical effects. In the accompanying drawings of the following embodiments, the same reference numerals in each drawing represent the same features or components, which can be applied to different embodiments. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0033] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes and to aid those skilled in the art in understanding and reading the invention. They are not intended to limit the conditions under which the invention can be implemented. Any modifications to the structure, changes in proportions, or adjustments to size, provided they do not affect the effectiveness or purpose of the invention, should fall within the scope of the technical content disclosed in the invention. The scope of the preferred embodiments of the present invention includes other implementations, wherein functions may be performed not in the order stated or discussed, including substantially simultaneously or in reverse order, depending on the functions involved. This should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0034] Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0035] In the description of the embodiments of this application, " / " means "or", and "and / or" is used to describe the relationship between related objects, indicating that there can be three relationships. For example, "A and / or B" means: A and B exist alone, B exists alone, and A and B exist simultaneously. In the description of the embodiments of this application, "multiple" refers to two or more embodiments.

[0036] Please see Figure 1 As shown, this is the intelligent battery management method for the power distribution cabinet in this embodiment, including:

[0037] Step S1: Periodically collect battery parameters, load parameters, and grid parameters. The battery parameters include state of charge, health status, internal resistance, temperature, and battery output power. The load parameters include power and load priority. The load priority is a preset parameter, which includes critical loads, ordinary loads, and interruptible loads. The load priority can be dynamically adjusted based on the load's historical behavior and the current system state. For example, if a load starts and stops more than 5 times in one hour, it is determined to be frequently started and stopped, and it can be adjusted from an ordinary load to an interruptible load. When the current system state is a low load state, ordinary loads can be adjusted to critical loads, etc. The battery is an energy storage device used to supply power to multiple loads in the system. The grid parameters include grid time period and grid voltage. The grid time period includes peak time period and off-peak time period. The grid power is the voltage of the grid used for charging. The battery parameters and load parameters are collected once every preset collection period. In this embodiment, the preset collection period is set to 1 minute. This embodiment does not specifically limit the setting of the preset collection period, such as setting it to 3 minutes, 5 minutes, etc.

[0038] Specifically, in step S1 of this embodiment, by periodically collecting key parameters such as the battery's state of charge, health status, internal resistance, temperature, and output power, and combining them with the load's power and priority information, as well as external conditions such as grid time period and voltage, a comprehensive perception of the system's operating status is achieved. This real-time collection of multi-source data provides a reliable data foundation for subsequent intelligent analysis and strategy formulation, helps improve the system's adaptability to dynamic environments, avoids misjudgments or resource waste caused by incomplete information, and thus enhances the accuracy and real-time performance of management.

[0039] Please continue reading. Figure 1 As shown, the intelligent battery management method for the power distribution cabinet also includes:

[0040] Step S2 involves performing feature analysis on battery parameters and load parameters to obtain load characteristics and load behavior indices.

[0041] Please see Figure 2As shown, it is a feature analysis method for battery parameters and load parameters, including:

[0042] Step S21: Determine the periodic power change rate based on the power, and analyze the load characteristics based on the periodic power change rate and load priority.

[0043] Specifically, in step S21 of this embodiment, the periodic power change rate is determined based on the power. The power change rate is the ratio of the absolute value of the power difference between the current acquisition cycle and the previous acquisition cycle to the power of the previous acquisition cycle. The load characteristics are analyzed based on the periodic power change rate and load priority. The expression of the load characteristics is: load characteristics = load priority parameter × (1 + periodic power change rate). When the load priority is a critical load, the load priority parameter is set to 3. When the load priority is a normal load, the load priority parameter is set to 2. When the load priority is an interruptible load, the load priority parameter is set to 1.

[0044] Please continue reading. Figure 2 As shown, the feature analysis method for battery parameters and load parameters further includes:

[0045] Step S22: Determine the load behavior index based on battery parameters and load parameters.

[0046] Specifically, in step S22 of this embodiment, the number of load starts and stops within 10 minutes is counted as the recent start and stop count, the ratio of the standard deviation of power to the average value within 10 minutes is used as the power volatility, and the load behavior index is determined based on the recent start and stop count and the power volatility. The expression of the load behavior index is: Load behavior index = recent start and stop count × start and stop weight + power volatility × volatility weight, start and stop weight + volatility weight = 1.

[0047] Specifically, in this embodiment, the start / stop weight and fluctuation weight are set based on load priority. They are used to emphasize the impact of start / stop and power fluctuation. For example, when the load priority is critical load, the start / stop weight is set to 0.6 and the fluctuation weight is set to 0.4. When the load priority is ordinary load, the start / stop weight is set to 0.5 and the fluctuation weight is set to 0.5. When the load priority is interruptible load, the start / stop weight is set to 0.4 and the fluctuation weight is set to 0.6.

[0048] Specifically, in step S2 of this embodiment, by calculating the cycle power change rate, load priority parameters and load behavior index, the operating characteristics and behavior patterns of the load are deeply explored. This analysis can identify the load fluctuation and start-stop frequency, provide a quantitative basis for subsequent power allocation, help optimize the power allocation strategy, reduce the impact of load changes on the battery, and improve the stability of system operation and the service life of equipment.

[0049] Please continue reading. Figure 1 As shown, the intelligent battery management method for the power distribution cabinet also includes:

[0050] Step S3: Formulate power supply rules based on battery parameters, load characteristics and grid parameters, and build an allocation strategy model based on power supply rules, battery parameters and load parameters to generate power allocation strategy.

[0051] Please see Figure 3 As shown, this is a method for generating an energy distribution strategy, including:

[0052] Step S31: Formulate power supply rules based on battery parameters, load characteristics, and grid parameters.

[0053] Specifically, in step S31 of this embodiment, power supply rules are formulated based on state of charge, load characteristics, grid time period and grid voltage. When the state of charge is less than 20% and the grid voltage is less than 100V, the power allocation ratio of the battery with load priority as a normal load or interruptible load is set to 0. When the grid time period is a peak period, the power allocation ratio of the battery is set to 0.8 and the power allocation ratio of the grid is set to 0.2. When the load characteristics are greater than the characteristic threshold, the power allocation ratio of the current load is set to 1.

[0054] Specifically, in this embodiment, the feature threshold is set to 0.8. This embodiment does not impose specific limitations on the setting of the feature threshold, but the feature threshold should be set to satisfy the condition [0.7, 1).

[0055] Please continue reading. Figure 3 As shown, the method for generating the power allocation strategy further includes:

[0056] Step S32: Determine the energy efficiency score, lifespan score, and reliability score based on battery parameters and load parameters.

[0057] Specifically, in step S32 of this embodiment, energy efficiency score, lifespan score, and reliability score are determined based on battery parameters and load parameters. The total power in the load parameters is taken as the total load power, and the ratio of the total load power to the battery output power is taken as the energy efficiency score. The lifespan score is determined based on the health status and internal resistance. The expression for the lifespan score is: lifespan score = health status - 0.1 × internal resistance. The ratio of the power supply time of the load with critical load priority within one hour to one hour is taken as the reliability score.

[0058] Please continue reading. Figure 3 As shown, the method for generating the power allocation strategy further includes:

[0059] Step S33: Construct a comprehensive objective function based on power supply rules, energy efficiency score, lifespan score, and reliability score to obtain a comprehensive score.

[0060] Specifically, in step S33 of this embodiment, a comprehensive objective function is constructed based on power supply rules, energy efficiency score, lifespan score, and reliability score. The expression of the comprehensive objective function is: comprehensive score = energy efficiency weight × energy efficiency score + lifespan weight × lifespan score + reliability weight × reliability score, where energy efficiency weight + lifespan weight + reliability weight = 1.

[0061] Specifically, in this embodiment, the energy efficiency weight, lifespan weight, and reliability weight are dynamically set based on the power grid time period and health status. For example, if the power grid time period is a low-peak period, the energy efficiency weight is set to 0.6, the lifespan weight to 0.2, and the reliability weight to 0.2 to emphasize the impact of energy efficiency on the score. If the health status is less than 0.8, the energy efficiency weight is set to 0.2, the lifespan weight to 0.6, and the reliability weight to 0.2 to emphasize the impact of battery lifespan on the score. Otherwise, the energy efficiency weight is set to 0.4, the lifespan weight to 0.4, and the reliability weight to 0.2. When setting the energy efficiency weight, lifespan weight, and reliability weight, if the battery meets two conditions simultaneously, the average value of the corresponding weight values ​​can be used for analysis.

[0062] Please continue reading. Figure 3 As shown, the method for generating the power allocation strategy further includes:

[0063] Step S34: Solve the comprehensive objective function based on the battery parameters and load parameters to generate an energy distribution strategy.

[0064] Specifically, in step S34 of this embodiment, when the power supply rule is not triggered, an exhaustive search method is adopted, and a step size of 5% is set to construct the solution space of the load power allocation ratio. The comprehensive objective function is solved in the solution space of the load power allocation ratio to obtain the comprehensive score, and the power allocation ratio of the load with the highest comprehensive score is taken as the power allocation strategy.

[0065] Specifically, in step S3 of this embodiment, intelligent decision-making under complex operating conditions is achieved by formulating multi-dimensional power supply rules and constructing a comprehensive objective function. This model comprehensively considers energy efficiency, battery life and power supply reliability, and adapts to different scenario requirements through dynamic weight adjustment. In this way, while ensuring the power supply of critical loads, it optimizes overall energy efficiency and battery health, and improves the overall operating efficiency and economy of the system.

[0066] Please continue reading. Figure 1 As shown, the intelligent battery management method for the power distribution cabinet also includes:

[0067] Step S4: Control the distribution of power according to the power distribution strategy.

[0068] Specifically, in step S4 of this embodiment, the output power of the battery to each load is controlled according to the power distribution strategy. If the load characteristic is greater than 1, a soft start mechanism is adopted when controlling the output power of the battery to the current load. The soft start time is set to a preset base time × (1 + load behavior index). During the soft start time, the output power of the battery to the current load gradually increases.

[0069] Specifically, in this embodiment, the preset base time is set to 2 seconds. This embodiment does not impose specific limitations on the setting of the preset base time, such as it can also be set to 1 second, 3 seconds, etc.

[0070] Specifically, in step S4 of this embodiment, by executing the power distribution strategy and introducing a soft-start mechanism, precise control of the battery output power is achieved. This mechanism effectively mitigates the current surge during load startup, reduces the risk of equipment damage, and ensures a smooth transition in the power distribution process, thereby improving the system's dynamic response capability and operational safety. It is particularly suitable for application scenarios with high power supply quality requirements.

[0071] Please continue reading. Figure 1 As shown, the intelligent battery management method for the power distribution cabinet also includes:

[0072] Step S5: Construct a cycle monitoring model based on battery parameters to update the generation process of the power distribution strategy.

[0073] Specifically, in step S5 of this embodiment, the cyclic monitoring cycle is set to 10 seconds, and the energy efficiency weight, lifespan weight, reliability weight, and comprehensive score within 10 cyclic monitoring cycles are stored as cyclic monitoring data.

[0074] Specifically, this embodiment does not impose specific limitations on the setting of the cyclic monitoring period, but it can also be set to 15 seconds, 30 seconds, etc.

[0075] Specifically, in step S5 of this embodiment, the rate of change of the comprehensive score of adjacent cyclic monitoring cycles is calculated. The rate of change of the comprehensive score is the comprehensive score of the current cyclic monitoring cycle - the comprehensive score of the previous cyclic monitoring cycle / the comprehensive score of the previous cyclic monitoring cycle. If the rate of change of the comprehensive score of three consecutive cyclic monitoring cycles is all less than -0.05, the feature threshold is reduced by 10% and the reliability weight is increased by 10%.

[0076] Specifically, in step S5 of this embodiment, by setting a cyclic monitoring cycle and analyzing the trend of changes in the comprehensive score, continuous evaluation and feedback on the strategy execution effect can be achieved. This mechanism can promptly detect strategy deviations or system performance degradation, and dynamically adjust thresholds and weight parameters to ensure that the management strategy always matches the actual state of the system, enhance the system's self-learning and adaptive capabilities, and improve the stability and reliability of long-term operation.

[0077] Please see Figure 4 As shown, this is the intelligent battery management device for the power distribution cabinet in this embodiment, including:

[0078] The periodic acquisition module is used to periodically acquire battery parameters, load parameters, and grid parameters;

[0079] The feature analysis module is used to perform feature analysis on battery parameters and load parameters to obtain load characteristics and load behavior index;

[0080] The strategy generation module is used to formulate power supply rules based on battery parameters, load characteristics and grid parameters, and to build an allocation strategy model based on the power supply rules, battery parameters and load parameters to generate power allocation strategies.

[0081] The strategy execution module is used to control the distribution of power according to the power distribution strategy;

[0082] The cycle monitoring module is used to build a cycle monitoring model based on battery parameters to update the generation process of the power distribution strategy.

[0083] This application also provides a computer-readable storage medium storing instructions that, when run on a computer, cause the computer to execute the intelligent battery management method for power distribution cabinets as described in the above method embodiments.

[0084] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable programs, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable programs, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0085] In the above description, the disclosure of this invention is not intended to limit itself to these aspects. Rather, within the scope of the objectives of this disclosure, components can be selectively and operationally combined in any number. Furthermore, terms such as “comprising,” “encompassing,” and “having” should be interpreted by default as inclusive or open-ended, rather than exclusive or closed, unless explicitly defined as such. All technical, scientific, or other terms are to be understood by those skilled in the art, unless defined as such. Public terms found in dictionaries should not be interpreted too idealistically or impractically in the context of the relevant technical documents, unless explicitly defined as such in this disclosure. Any modifications or alterations made by those skilled in the art based on the foregoing disclosure are within the scope of the claims.

Claims

1. A method for intelligent management of a battery of a power distribution cabinet, characterized in that, The method comprises the following steps: periodically collecting battery parameters, load parameters and grid parameters; performing feature analysis on the battery parameters and the load parameters to obtain load features and a load behavior index; determining a periodic power change rate according to power, the power change rate being the absolute value of the difference between the power of the current collection period and the power of the previous collection period divided by the power of the previous collection period, and analyzing the load features based on the periodic power change rate and a load priority, the expression of the load features being: load features = load priority parameter × (1 + periodic power change rate), when the load priority is a critical load, setting the load priority parameter to 3, when the load priority is an ordinary load, setting the load priority parameter to 2, and when the load priority is an interruptible load, setting the load priority parameter to 1; counting the number of start-stop of the load within 10 minutes as a recent start-stop number, taking the ratio of the standard deviation to the average of the power within 10 minutes as a power fluctuation rate, and determining the load behavior index based on the recent start-stop number and the power fluctuation rate, the expression of the load behavior index being: load behavior index = recent start-stop number × start-stop weight + power fluctuation rate × fluctuation weight, start-stop weight + fluctuation weight = 1; formulating a power supply rule based on the battery parameters, the load features and the grid parameters, and constructing a distribution strategy model based on the power supply rule, the battery parameters and the load parameters to generate a power distribution strategy; determining an energy efficiency score, a life score and a reliability score based on the battery parameters and the load parameters, taking the total load power as the comprehensive power in the load parameters, taking the ratio of the total load power to the battery output power as the energy efficiency score, determining the life score according to the state of health and the internal resistance, the expression of the life score being: life score = state of health - 0.1 × internal resistance, and taking the ratio of the power supply time of the load with a critical load within one hour to one hour as the reliability score; constructing a comprehensive objective function according to the power supply rule, the energy efficiency score, the life score and the reliability score, the expression of the comprehensive objective function being: comprehensive score = energy efficiency weight × energy efficiency score + life weight × life score + reliability weight × reliability score, energy efficiency weight + life weight + reliability weight = 1; when the power supply rule is not triggered, using an exhaustive search method, setting a step length of 5% to construct a power distribution proportion solution space of the load, solving the comprehensive objective function in the power distribution proportion solution space of the load to obtain the comprehensive score, and taking the power distribution proportion of the load with the highest comprehensive score as the power distribution strategy; controlling the distribution of electric energy according to the power distribution strategy; constructing a cycle monitoring model based on the battery parameters to update the generation process of the power distribution strategy; controlling the output power of the battery to each load according to the power distribution strategy, if the load feature is greater than 1, using a soft start mechanism when controlling the output power of the battery to the current load, setting the soft start time = preset basic time × (1 + load behavior index), and gradually increasing the output power of the battery to the current load within the soft start time. A change rate of the comprehensive score of the adjacent cycle monitoring period is calculated, the change rate of the comprehensive score = the comprehensive score of the current cycle monitoring period - the comprehensive score of the last cycle monitoring period / the comprehensive score of the last cycle monitoring period, if the change rates of the comprehensive scores of three consecutive cycle monitoring periods are all less than -0.05, the feature threshold is reduced and the reliability weight is increased.

2. The power distribution cabinet battery intelligent management device applied to the power distribution cabinet battery intelligent management method of claim 1, characterized in that, The method comprises the steps of: periodically collecting battery parameters, load parameters and power grid parameters; performing feature analysis on the battery parameters and the load parameters to obtain load features and a load behavior index; formulating power supply rules based on the battery parameters, the load features and the power grid parameters, and constructing a distribution strategy model based on the power supply rules, the battery parameters and the load parameters to generate an electric energy distribution strategy; controlling the distribution of electric energy according to the electric energy distribution strategy; constructing a cycle monitoring model based on the battery parameters to update the generation process of the electric energy distribution strategy.

3. A storage medium, characterized by The computer stores instructions which, when executed on the computer, cause the computer to perform the intelligent management method of the switchboard battery according to claim 1.

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