UPS (Uninterrupted Power Supply) integration processing method for self-adaptive parameter adjustment of energy storage battery

Abstract

The invention relates to the technical field of battery parameter adjustment, and discloses an energy storage battery adaptive parameter adjustment UPS integration processing method, which comprises the following steps: respectively carrying out mode mining on battery state information and power grid operation state information to form operation modes of an energy storage battery and a power grid; a charging and discharging strategy objective function of the energy storage battery is constructed by using the operation mode, the charging and discharging strategy objective function is solved by using an improved heuristic algorithm to obtain charging and discharging strategy adjustment parameters of the energy storage battery, and the UPS receives the charging and discharging strategy adjustment parameters and performs adaptive parameter adjustment treatment on the energy storage battery. According to the method, an operation mode representing power grid operation fluctuation and the charging and discharging state and health degree of the energy storage battery is obtained through mode mining, a charging and discharging strategy objective function of the energy storage battery is constructed, charging and discharging strategy adjustment parameters maximizing the operation stability of the energy storage battery and the power grid are obtained through solving, and self-adaptive parameter adjustment is carried out on the energy storage battery.

Description

Technical Field

[0001] The present invention relates to the technical field of battery parameter regulation, and particularly to a UPS integrated disposal method for adaptive parameter regulation of energy storage batteries. Background Art

[0002] With the rapid development of information technology, industrial automation, and smart grids, the requirements for the reliability and quality of power supply are getting higher and higher. Especially for critical facilities such as data centers, hospitals, and communication networks, power supply interruptions may lead to serious economic losses and social impacts. Therefore, the uninterruptible power supply system (UPS) plays a crucial role in these fields. Traditional UPS systems mainly rely on lead-acid batteries or other conventional battery technologies. Although these batteries have been widely used in power backup, their performance has certain limitations. For example, lead-acid batteries have a short service life, poor temperature adaptability, and low energy density, and cannot meet the requirements of modern high-efficiency and long-life power protection. New energy storage batteries such as lithium-ion batteries, sodium-sulfur batteries, and solid-state batteries have gradually been applied to UPS systems. With their high energy density and long service life, they have become ideal choices to replace traditional batteries. However, in actual applications, especially during the integration process of UPS systems, the service life of these new batteries will be affected by parameters such as working modes and charge-discharge efficiency, thus affecting the reliability and performance of UPS systems. Summary of the Invention

[0003] In view of this, the present invention proposes a UPS integrated disposal method for adaptive parameter regulation of energy storage batteries. By analyzing the power supply mode of the working environment, the working parameters of the energy storage battery are dynamically regulated, enabling the energy storage battery to achieve optimal charge-discharge management under different power grid fluctuation states, extending the service life of the energy storage battery, and improving the stability of power protection.

[0004] To achieve the above object, a UPS integrated disposal method for adaptive parameter regulation of energy storage batteries provided by the present invention includes the following steps: S1: Obtain the battery state information of the energy storage battery under different battery state indicators, as well as the power grid operation state information; S2: Perform pattern mining on the battery state information and the power grid operation state information respectively to form the operation patterns of the energy storage battery and the power grid; S3: Combine the operation patterns of the energy storage battery and the power grid to construct an objective function for the charge-discharge strategy of the energy storage battery; S4: Use an improved heuristic algorithm to solve the objective function of the charge-discharge strategy to obtain the charge-discharge strategy adjustment parameters of the energy storage battery. The UPS receives the charge-discharge strategy adjustment parameters and performs adaptive parameter regulation on the energy storage battery.

[0005] As a further improvement method of the present invention: Optionally, the battery state information of the energy storage battery at the current operating moment is : ; Wherein: are the index data of the energy storage battery in 5 battery state indexes in sequence, and the 1st - 5th battery state indexes are voltage index, current index, temperature index, state of charge index and health state index in sequence.

[0006] Optionally, the power grid operation state information is the sequence data of the power grid at multiple operating moments, the sequence data includes current sequence data and voltage sequence data, and the representation form of the power grid operation state information is y: ; ; ; Wherein: T represents transpose, represents the current sequence data, represents the voltage sequence data; represents the current value of the power grid at the operating moment t, represents the current value of the power grid at the operating moment ; represents the preset time interval between adjacent operating moments; represents the voltage value of the power grid at the operating moment t, represents the voltage value of the power grid at the operating moment ; , M + 1 represents the length of the sequence data.

[0007] Optionally, perform pattern mining on the battery state information of the energy storage battery, and the pattern mining process is: Calculate the health degree of the energy storage battery at the current operating moment t: ; Wherein: represents the preset temperature threshold of the energy storage battery; Combine the health degree change rate of the energy storage battery to correct the health degree : ; wherein: represents the rate of change of the state of health of the energy storage battery at the current operating time t, represents the state of health of the energy storage battery at the operating time t - 1; the operating mode of the energy storage battery is constructed as: ; wherein: represents the operating mode of the energy storage battery, is the sign function.

[0008] Optionally, pattern mining is performed on the grid operating state information y, and the pattern mining result of the grid operating state information y is the grid operating fluctuation value and the current value and voltage value of the grid at the current operating time t. The grid operating fluctuation value includes the current fluctuation value and the voltage fluctuation value. The current fluctuation value is , where represents the current sequence data standard deviation of, represents the current sequence data mean value of, and the voltage fluctuation value is , where represents the voltage sequence data standard deviation of, represents the voltage sequence data mean value of, and the operating mode of the grid is .

[0009] Optionally, the expression of the charge and discharge strategy objective function is : ; ; ; ; ; ; ; wherein: represents the charge and discharge strategy adjustment parameter of the energy storage battery, represents the voltage adjustment parameter of the energy storage battery, represents the current adjustment parameter of the energy storage battery; represents the convolution operator; Represents the objective function for the stable operation of the energy storage battery, Represents the objective function for the stable operation of the power grid; Represents the adjustment parameter of the energy storage battery based on the charge-discharge strategy The stable value of the health degree of the energy storage battery, Represents the mapping matrix, Represents the adjustment parameter of the energy storage battery based on the charge-discharge strategy The stable value of the charging rate of the energy storage battery, Represents the preset state of charge threshold, Represents the adjustment parameter of the energy storage battery based on the charge-discharge strategy The stable value of the charge-discharge switching of the energy storage battery, In turn are The weight coefficients; Represents the preset grid standard current, Represents the preset grid standard voltage.

[0010] Optionally, solve the charge-discharge strategy objective function, and the solution process is as follows: Initialize and generate H groups of particles. Each group of particles is in the form of a two-dimensional vector, and each group of particles corresponds to a set of charge-discharge strategy adjustment parameters of the energy storage battery, and iterate on the H groups of particles; Take each group of particles obtained by iteration as the variable of the charge-discharge strategy objective function, take the charge-discharge strategy objective function value as the fitness function value of each group of particles, and select the particle with the highest fitness function value as the optimal particle for this iteration. The optimal particle is used to guide the iteration direction and iteration step size of all particles in the next iteration process; Retain H / 2 groups of particles with the largest fitness function value after iteration, and use the retained particles as elite particles to generate reverse particles to obtain H / 2 groups of reverse particles. The generation formula of the reverse particles is: ; Where: Represents the reverse particle generated by the elite particle Y, Represents a two-dimensional vector, and The vector value in is a random number between 0 and 1; Represents element-wise addition; Represents the preset upper boundary of the particle, Represents the preset lower boundary of the particle. The upper boundary of the particle and the lower boundary of the particle are both in the form of a two-dimensional vector; Take the retained H / 2 groups of particles and H / 2 groups of reverse particles as the H groups of particles obtained by iteration; Iterate the steps of the repeated particles and the reverse particle generation steps until the preset maximum particle iteration number is reached, and terminate the iteration; Take the H groups of particles generated after terminating the iteration as the variables of the objective function of the charge-discharge strategy, and select the particle with the highest fitness function value as the solution result of the charge-discharge strategy adjustment parameter.

[0011] Optionally, the UPS is composed of multiple modules, including an input module, a rectifier module, a battery module, an inverter module, and an output module; The process of the UPS receiving the charge-discharge strategy adjustment parameter and performing adaptive parameter adjustment on the energy storage battery is as follows: The UPS receives the charge-discharge strategy adjustment parameter and extracts the voltage adjustment parameter. The battery module adjusts the voltage of the energy storage battery to be consistent with the voltage adjustment parameter; Extract the current adjustment parameter. If the current adjustment parameter is positive, the input module receives alternating current with a current magnitude of the current adjustment parameter from the power grid, converts the alternating current into direct current using the rectifier module and provides it to the energy storage battery, and the energy storage battery stores electrical energy; if the current adjustment parameter is negative, the energy storage battery provides direct current with a current magnitude of the current adjustment parameter to the inverter module. The inverter module receives the direct current sent by the energy storage battery, converts the direct current into alternating current and provides it to the output module, and the input module sends the alternating current to the power grid, and the energy storage battery releases electrical energy.

[0012] To solve the above problems, the present invention provides an electronic device, which includes: A memory that stores at least one instruction; A communication interface that enables the communication of the electronic device; and A processor that executes the instructions stored in the memory to implement the above-mentioned UPS integrated disposal method for adaptive parameter adjustment of the energy storage battery.

[0013] To solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned UPS integrated disposal method for adaptive parameter adjustment of the energy storage battery.

[0014] Compared with the prior art, the present invention proposes a UPS integrated disposal method for adaptive parameter adjustment of an energy storage battery, and this technology has the following advantages: First of all, this solution proposes a mode mining method to perform mode mining on the battery state information and the power grid operation state information, and obtain the operation modes that characterize the power grid operation fluctuations and the charge-discharge state and health of the energy storage battery, so as to realize the operation mode mining of the power grid and the energy storage battery.

[0015] Meanwhile, this solution proposes an adaptive parameter adjustment method for energy storage batteries. By combining the operation modes of energy storage batteries and the power grid, an objective function for the charge and discharge strategy of energy storage batteries is constructed. The stable value of the charge and discharge switching of the energy storage battery takes into account that the charge and discharge switching of the energy storage battery will reduce its lifespan. The stable value of the health of the energy storage battery considers the impact on the health after adjusting the current and voltage of the energy storage battery. The stable value of the charging rate of the energy storage battery uses the state of charge and voltage to evaluate the change in the battery charging rate after adjusting the parameters of the energy storage battery. Then, the above stable values are used to quantify the operation stability of the energy storage battery after adjusting the parameters, and a multi-dimensional operation stability objective function of the energy storage battery is constructed. Combining with the power grid operation fluctuation value, a power grid operation stability objective function is constructed. When the power grid operation fluctuation value is large, it is necessary to use the energy storage battery to receive the surplus electric energy from the power grid or release electric energy to the power grid, reduce the power grid operation fluctuation, and improve the power grid operation stability. An improved heuristic algorithm is used for solving. During the solving process, reverse particles are introduced to generate new particles, so as to increase the globality of particle optimization and improve the optimization performance. The UPS integration method is adopted to receive the adjusted parameters of the charge and discharge strategy and perform adaptive parameter adjustment on the energy storage battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is a schematic flowchart of a UPS integration disposal method for adaptive parameter adjustment of an energy storage battery provided by an embodiment of the present invention; Figure 2 FIG. is a UPS integration disposal structure diagram provided by an embodiment of the present invention.

[0017] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] 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.

[0019] The embodiment of the present application provides a UPS integration disposal method for adaptive parameter adjustment of an energy storage battery. The execution subject of the UPS integration disposal method for adaptive parameter adjustment of the energy storage battery includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the UPS integration disposal method for adaptive parameter adjustment of the energy storage battery can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0020] Referring to Figure 1 , Embodiment 1 of the present invention is as follows: An UPS integrated disposal method for adaptive parameter adjustment of energy storage batteries, comprising the following steps: S1: Obtain the battery state information of the energy storage battery under different battery state indicators, as well as the power grid operation state information.

[0021] The battery state information of the energy storage battery at the current operating moment is : ; Wherein: Are the index data of the energy storage battery under 5 battery state indicators in sequence, where the 1st - 5th battery state indicators are voltage indicator, current indicator, temperature indicator, state of charge indicator, and health state indicator in sequence; The calculation method of the index data of the energy storage battery under the state of charge indicator is: ; Wherein: Represents the state of charge of the energy storage battery at the initial operating moment , t represents the current operating moment of the energy storage battery, Represents the current of the energy storage battery at the operating moment , where a positive current indicates charging and a negative current indicates discharging; Represents the battery capacity of the energy storage battery at the initial operating moment ; The calculation method of the index data of the energy storage battery under the health state indicator is: ; Wherein: Represents the battery capacity of the energy storage battery at the current operating moment t.

[0022] The power grid operation state information is the sequence data of the power grid at multiple operating moments, and the sequence data includes current sequence data and voltage sequence data. The representation form of the power grid operation state information is y: ; ; ; Wherein: T represents transpose, Represents the current sequence data, Represents the voltage sequence data; Represents the current value of the power grid at the operating moment t, Represents the power grid at the operating moment The current value, represents the time interval between preset adjacent operating times; represents the voltage value of the power grid at the operating time t, represents the power grid at the operating time voltage value; , M + 1 represents the length of the sequence data.

[0023] S2: Perform pattern mining on the battery state information and the power grid operating state information respectively to form the operating modes of the energy storage battery and the power grid.

[0024] Perform pattern mining on the battery state information of the energy storage battery The pattern mining process is as follows: Calculate the health degree of the energy storage battery at the current operating time t : ; Where: represents the preset temperature threshold of the energy storage battery; Combine the health degree change rate of the energy storage battery to correct the health degree : ; Where: represents the health degree change rate of the energy storage battery at the current operating time t, represents the health degree of the energy storage battery at the operating time t - 1; Construct the operating mode of the energy storage battery: ; Where: represents the operating mode of the energy storage battery, is the sign function. Specifically, represents the current charge and discharge state of the energy storage battery, greater than 0 indicates that the energy storage battery is in the charging state, less than 0 indicates that the energy storage battery is in the discharging state.

[0025] Perform pattern mining on the power grid operating state information y. The pattern mining result of the power grid operating state information y is the power grid operation fluctuation value and the current value and voltage value of the power grid at the current operating time t. The power grid operation fluctuation value includes the current fluctuation value and the voltage fluctuation value. The current fluctuation value is where Represents the current sequence data of the standard deviation, represents the current sequence data of the mean value, and the voltage fluctuation value is , where represents the voltage sequence data of the standard deviation, represents the voltage sequence data of the mean value, and the operating mode of the power grid is .

[0026] S3: Combine the energy storage battery and the operating mode of the power grid to construct the objective function of the charge and discharge strategy of the energy storage battery.

[0027] The expression of the objective function of the charge and discharge strategy is :[[]]END]] ; ; ; ; ; ; ; where:[[]]END]] represents the charge and discharge strategy adjustment parameter of the energy storage battery, represents the voltage adjustment parameter of the energy storage battery, represents the current adjustment parameter of the energy storage battery; represents the convolution operator; represents the objective function for the stable operation of the energy storage battery, represents the objective function for the stable operation of the power grid; represents the stable value of the state of health of the energy storage battery based on the charge and discharge strategy adjustment parameter , represents the mapping matrix for mapping the current and voltage of the energy storage battery into the influence ratio of the state of health, represents the stable value of the charging rate of the energy storage battery based on the charge and discharge strategy adjustment parameter , represents the preset state of charge threshold, represents the stable value of the charge and discharge switching of the energy storage battery based on the charge and discharge strategy adjustment parameter , are successively of the weight coefficients; represents a preset grid standard current, represents a preset grid standard voltage. As a preferred embodiment of the present invention, the stable value of the charge and discharge switching of the energy storage battery considers that the charge and discharge switching of the energy storage battery will reduce the life of the energy storage battery. The stable value of the health degree of the energy storage battery considers the impact on the health degree after the energy storage battery adjusts the current and voltage. The stable value of the charging rate of the energy storage battery evaluates the change of the battery charging rate after adjusting the energy storage battery parameters by using the state of charge and voltage. Furthermore, the stable values are used to quantify the operating stability of the energy storage battery after adjusting the parameters, and a multi-dimensional operating stability objective function of the energy storage battery is constructed. Combining with the grid operation fluctuation value, a grid operation stability objective function is constructed. When the grid operation fluctuation value is large, it is necessary to use the energy storage battery to receive the surplus electric energy from the grid or release electric energy to the grid, reduce the fluctuation of the grid operation, and improve the grid operation stability.

[0028] S4: Use an improved heuristic algorithm to solve the charge and discharge strategy objective function to obtain the charge and discharge strategy adjustment parameters of the energy storage battery. The UPS receives the charge and discharge strategy adjustment parameters and performs adaptive parameter adjustment on the energy storage battery.

[0029] Solve the charge and discharge strategy objective function, and the solution process is as follows: Initialize and generate H groups of particles. Each group of particles is in the form of a two-dimensional vector, and each group of particles corresponds to a set of charge and discharge strategy adjustment parameters of the energy storage battery, and iterate on the H groups of particles; Take each group of particles obtained by iteration as the variable of the charge and discharge strategy objective function, take the charge and discharge strategy objective function value as the fitness function value of each group of particles, and select the particle with the highest fitness function value as the optimal particle of this iteration. The optimal particle is used to guide the iteration direction and iteration step size of all particles in the next iteration process. The iteration formula of the particle is: ; ; ; Where: represents the iteration result of particle X, represents the optimal particle among the H groups of particles corresponding to the same iteration times as particle X; represents the iteration coefficient for particle X to perform iteration, represents the step size control coefficient, R represents the preset step size parameter, represents a random number between 0 and 1, represents the iteration control coefficient, represents the iteration times corresponding to particle X, represents the preset maximum number of particle iterations; Retain the H / 2 groups of particles with the largest fitness function values after iteration, and use the retained particles as elite particles to generate reverse particles, obtaining H / 2 groups of reverse particles. The generation formula for the reverse particles is as follows: ; Where: represents the reverse particle generated by the elite particle Y, represents a two-dimensional vector, and the vector values in are random numbers between 0 and 1; represents element-wise addition; represents the preset upper boundary of the particle, represents the preset lower boundary of the particle. Both the particle upper boundary and the particle lower boundary are in the form of two-dimensional vectors; Use the retained H / 2 groups of particles and H / 2 groups of reverse particles as the H groups of particles obtained by iteration; Repeat the particle iteration step and the reverse particle generation step until the preset maximum number of particle iterations is reached, and terminate the iteration; Use the H groups of particles generated after terminating the iteration as the variables of the charge and discharge strategy objective function, and select the particle with the highest fitness function value as the solution result of the charge and discharge strategy adjustment parameter.

[0030] The UPS is composed of multiple modules, including an input module, a rectifier module, a battery module, an inverter module, and an output module; The process by which the UPS receives the charge and discharge strategy adjustment parameter and performs adaptive parameter adjustment on the energy storage battery is as follows: The UPS receives the charge and discharge strategy adjustment parameter and extracts the voltage adjustment parameter. The battery module adjusts the voltage of the energy storage battery to be consistent with the voltage adjustment parameter; Extract the current adjustment parameter. If the current adjustment parameter is positive, the input module receives alternating current with a current magnitude of the current adjustment parameter from the power grid, uses the rectifier module to convert the alternating current into direct current and provides it to the energy storage battery, and the energy storage battery stores electrical energy; if the current adjustment parameter is negative, the energy storage battery provides direct current with a current magnitude of the current adjustment parameter to the inverter module. The inverter module receives the direct current sent by the energy storage battery, converts the direct current into alternating current and provides it to the output module, and the input module sends the alternating current to the power grid, and the energy storage battery releases electrical energy.

[0031] Embodiment 2: As Figure 2As shown in the figure, it is the structural diagram of UPS integrated disposal provided by an embodiment of the present invention. The input module is used to receive alternating current from the power grid and provide the alternating current to the rectifier module. The rectifier module is used to convert the alternating current into direct current and provide it to the energy storage battery. The battery module is used to regulate the voltage of the energy storage battery. The inverter module is used to receive the direct current sent by the energy storage battery, convert the direct current into alternating current, and provide the alternating current to the output module. The input module sends the alternating current to the power grid.

[0032] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure in the scope of the patent application.

[0033] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments. And the term "including", "comprising" or any other variant thereof in this article is intended to cover a non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, device, article or method including that element.

[0034] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0035] The above are only the preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An adaptive parameter adjustment method for an energy storage battery integrated with a UPS, characterized in that, The method includes: S1: Obtain the battery state information of the energy storage battery under different battery state indicators, as well as the grid operation state information. The battery state indicators include voltage indicators, current indicators, temperature indicators, state of charge indicators, and health state indicators; S2: Perform pattern mining on the battery state information and the grid operation state information respectively to form the operation modes of the energy storage battery and the grid; S3: Combine the operation modes of the energy storage battery and the grid to construct an objective function for the charge and discharge strategy of the energy storage battery. The objective function for the charge and discharge strategy takes the adjustment parameters of the charge and discharge strategy of the energy storage battery as variables and aims to maximize the operation stability of the energy storage battery and the grid; S4: Use an improved heuristic algorithm to solve the objective function for the charge and discharge strategy to obtain the adjustment parameters of the charge and discharge strategy of the energy storage battery. The UPS receives the adjustment parameters of the charge and discharge strategy and performs adaptive parameter adjustment on the energy storage battery.

2. The UPS integrated disposal method for adaptive parameter adjustment of an energy storage battery according to claim 1, wherein The battery state information of the energy storage battery at the current operating moment is :[[]]END]] ； Where: They are the index data of the energy storage battery for five battery state indicators. Among them, the first to fifth battery state indicators are voltage indicator, current indicator, temperature indicator, state of charge indicator, and health state indicator in sequence.

3. The UPS integrated disposal method for adaptive parameter adjustment of an energy storage battery according to claim 1, wherein, The grid operation state information is sequential data of the grid at multiple operation times. The sequential data includes current sequential data and voltage sequential data. The representation form of the grid operation state information is y: ； ； ； Where: T represents transpose, represents current sequence data, represents voltage sequence data; represents the current value of the power grid at the operating time t, represents the power grid at the operating time of the current value, represents the preset time interval between adjacent operating times; represents the voltage value of the power grid at the operating time t, represents the power grid at the operating time voltage value; , M + 1 represents the length of the sequence data.

4. The UPS integrated disposal method for adaptive parameter adjustment of an energy storage battery according to claim 2, characterized in that, For the battery state information of the energy storage battery perform pattern mining, where the pattern mining process is as follows: Calculate the health state of the energy storage battery at the current operating time t : ； Where: Indicates a preset energy storage battery temperature threshold; Based on the rate of change of the state of health of the energy storage battery, correct the state of health as follows: ； Where: represents the rate of change of the state of health of the energy storage battery at the current operating time t, represents the state of health of the energy storage battery at the operating time t-1; Construct the operation mode of the energy storage battery: ； Where: Indicates the operating mode of the energy storage battery, is the sign function.

5. The UPS integrated disposal method for adaptive parameter adjustment of an energy storage battery according to claim 3, wherein, Perform pattern mining on the power grid operation status information y. The pattern mining results of the power grid operation status information y are the power grid operation fluctuation value, as well as the current value and voltage value of the power grid at the current operation moment t. The power grid operation fluctuation value includes the current fluctuation value and the voltage fluctuation value. The current fluctuation value is , where represents the standard deviation of the current sequence data , represents the mean value of the current sequence data . The voltage fluctuation value is , where represents the standard deviation of the voltage sequence data , represents the mean value of the voltage sequence data . The operation mode of the power grid is .

6. The UPS integrated disposal method for adaptive parameter adjustment of an energy storage battery according to claim 1, wherein The expression of the objective function of the charge and discharge strategy is :[[]]END]] ； ； ； ； ； ； ； Where: Represents the charge and discharge strategy adjustment parameters of the energy storage battery, Represents the voltage adjustment parameters of the energy storage battery, Represents the current adjustment parameters of the energy storage battery; denotes the convolution operator; Represents the objective function for the stable operation of the energy storage battery, Represents the objective function for the stable operation of the power grid; Indicates the regulated parameter based on the charge-discharge strategy The stable value of the state of health of the energy storage battery, Indicates the mapping matrix, Indicates the regulated parameter based on the charge-discharge strategy The stable value of the charging rate of the energy storage battery, Indicates the preset state of charge threshold, Indicates the regulated parameter based on the charge-discharge strategy The stable value of the charge-discharge switching of the energy storage battery, In turn are The weight coefficients; represents a preset grid standard current, represents a preset grid standard voltage.

7. The UPS integrated disposal method for adaptive parameter adjustment of an energy storage battery according to claim 6, wherein Solve the objective function for the charge and discharge strategy. The solution process is as follows: Initialize and generate H groups of particles. Each group of particles is in the form of a two-dimensional vector, and each group of particles corresponds to a set of adjustment parameters for the charge and discharge strategy of the energy storage battery. Iterate the H groups of particles; Take each group of particles obtained by iteration as the variable of the objective function for the charge and discharge strategy, take the value of the objective function for the charge and discharge strategy as the fitness function value of each group of particles, and select the particle with the highest fitness function value as the optimal particle for this iteration. The optimal particle is used to guide the iteration direction and iteration step size of all particles in the next iteration process; Retain H / 2 groups of particles with the largest fitness function value after iteration and use the retained particles as elite particles to generate reverse particles to obtain H / 2 groups of reverse particles. The generation formula for the reverse particles is: ； Where: Denotes the reverse particle generated by the elite particle Y, Denotes a two-dimensional vector, and the vector value in it is a random number between 0 and 1; represents element-wise addition; Represents a preset upper boundary of the particle Represents a preset lower boundary of the particle, and both the upper boundary and the lower boundary of the particle are in the form of two-dimensional vectors; Take the retained H / 2 groups of particles and the H / 2 groups of reverse particles as the H groups of particles obtained by iteration; Repeat the particle iteration step and the reverse particle generation step until the preset maximum particle iteration times are reached, and terminate the iteration; Take the H groups of particles generated after terminating the iteration as the variables of the objective function for the charge and discharge strategy, and select the particle with the highest fitness function value as the solution result of the adjustment parameters for the charge and discharge strategy.

8. The UPS integrated disposal method for adaptive parameter adjustment of an energy storage battery according to claim 1, characterized in that, The UPS consists of multiple modules, including an input module, a rectifier module, a battery module, an inverter module, and an output module; The process of the UPS receiving the adjustment parameters of the charge and discharge strategy and performing adaptive parameter adjustment on the energy storage battery is as follows: The UPS receives the adjustment parameters of the charge and discharge strategy and extracts the voltage adjustment parameters. The battery module adjusts the voltage of the energy storage battery to be consistent with the voltage adjustment parameters; Extract the current regulation parameter. If the current regulation parameter is positive, the input module receives alternating current with a magnitude equal to the current regulation parameter from the power grid, converts the alternating current into direct current using the rectifier module and supplies it to the energy storage battery, and the energy storage battery stores electrical energy; if the current regulation parameter is negative, the energy storage battery supplies direct current with a magnitude equal to the current regulation parameter to the inverter module, the inverter module receives the direct current sent by the energy storage battery, converts the direct current into alternating current and supplies it to the output module, the input module sends the alternating current to the power grid, and the energy storage battery releases electrical energy.

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