Combined power supply control method and system for multiple emergency power generation equipment

Abstract

The present disclosure provides a method and system for joint power supply control of multiple emergency power generation equipment, the method comprising: receiving a total power demand in a current control cycle; predicting an estimated power generation and an estimated remaining life of each emergency power generation equipment in the current control cycle using a machine learning algorithm based on historically sensed environmental information and operating condition information of each emergency power generation equipment; determining at least two of the multiple emergency power generation equipment as joint power supply equipment in the current control cycle based on the estimated remaining life of the multiple emergency power generation equipment; and optimizing the power supply ratio of each joint power supply equipment using a preset optimization algorithm, wherein the optimization algorithm converges when the difference between the total power generation of each joint power supply equipment and the total power demand in the current control cycle is minimized, and the total power generation of each joint power supply equipment is equal to the sum of the products of the estimated power generation of each joint power supply equipment and the power supply ratio of the corresponding joint power supply equipment.

Description

Technical Field

[0001] The present disclosure relates to the field of emergency power supply, and more specifically, to a combined power supply control method and system for multiple emergency power generation equipment. Background Art

[0002] Emergency networking includes different types of distributed sources, such as the difference in output characteristics between emergency diesel generators and distributed inverter-type power supplies. The control methods of distributed inverter-type power supplies are also different, and can be divided into voltage source mode and current source mode distributed power supplies. Generally speaking, in order to achieve autonomous operation of emergency networking, control needs to achieve real-time balance between system active power and reactive power, and ensure multiple goals such as frequency requirements, voltage quality and system stability. Traditional hierarchical control has a clear control structure for load-bearing operation, but it is difficult to balance power regulation and system stability. For group control of multiple emergency power supplies, it is necessary to propose an improved optimization control strategy for the stable control of the system.

[0003] In addition to the above-mentioned conventional optimal performance control of each emergency power supply, how to achieve the optimal overall comprehensive performance of each emergency power supply has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the embodiments of the present disclosure is to provide a combined power supply control method and system for multiple emergency power generation equipment to achieve optimal overall comprehensive performance of each emergency power supply.

[0005] In a first aspect, the present invention provides a method for controlling a combined power supply of multiple emergency power generation equipment, comprising: receiving a total power demand in a current control cycle;

[0006] Based on the historically sensed environmental information and operating condition information of each emergency power generation device, the trained machine learning algorithm is used to predict the estimated power generation and the estimated remaining life of each emergency power generation device in the current control cycle;

[0007] Determining, based on the estimated remaining lifespans of the multiple emergency power generation devices, at least two of the multiple emergency power generation devices as joint power supply devices in a current control cycle;

[0008] A preset optimization algorithm is used to optimize the power supply ratio of each joint power supply device, wherein the optimization algorithm converges when the difference between the total power generation of each joint power supply device and the total power supply demand of the current control cycle is minimized, and the total power generation of each joint power supply device is equal to the sum of the products of the estimated power generation of each joint power supply device and the power supply ratio of the corresponding joint power supply device.

[0009] Furthermore, the step of determining at least two of the multiple emergency power generation devices as joint power supply devices in the current control cycle according to the average value of the remaining life of the multiple emergency power generation devices includes:

[0010] Calculating the average remaining life of the multiple emergency power generation equipment;

[0011] Determining whether the number of emergency power generation devices having estimated remaining life values ​​greater than the remaining life average value among the plurality of emergency power generation devices is greater than or equal to two;

[0012] When determining whether the number of emergency power generation devices among the multiple emergency power generation devices whose estimated remaining life is greater than the average remaining life is greater than or equal to two, the emergency power generation devices among the multiple emergency power generation devices whose estimated remaining life is greater than the average remaining life are used as joint power supply devices within the current control cycle.

[0013] Furthermore, the various emergency power generation equipment include wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generators, hydrogen energy equipment and fuel cell equipment.

[0014] Furthermore, the machine learning algorithm is a neural network algorithm, and the preset optimization algorithm is a genetic algorithm;

[0015] The step of using the emergency power generation equipment whose estimated remaining life value is greater than the remaining life average value among the multiple emergency power generation equipment as the joint power supply equipment in the current control cycle comprises:

[0016] The emergency power generation equipment whose estimated remaining lifespan is greater than the average remaining lifespan is sorted in descending order according to the estimated power generation amount of the current control cycle, and the top N emergency power generation equipment are selected as the first priority joint power supply equipment in the current control cycle; N is a preset value;

[0017] The step of optimizing the power supply ratio of each joint power supply device by using a preset optimization algorithm includes:

[0018] A preset optimization algorithm is used to optimize the power supply ratio of each first priority joint power supply device.

[0019] Furthermore, after the step of calculating the average remaining life of the multiple emergency power generation equipment, the step further includes:

[0020] When the number of emergency power generation devices having estimated remaining life values ​​greater than the average remaining life value among the multiple emergency power generation devices is less than two, the emergency power generation device having the largest estimated remaining life value is used as the joint power supply device in the current control cycle.

[0021] In a second aspect, the present invention provides a combined power supply control system for multiple emergency power generation equipment, including:

[0022] A power supply demand receiving module is used to receive the total power supply demand of the current control cycle;

[0023] The comprehensive performance prediction module is used to predict the estimated power generation and the estimated remaining life of each emergency power generation equipment in the current control cycle based on the historically sensed environmental information and operating condition information of each emergency power generation equipment using the trained machine learning algorithm;

[0024] A power supply equipment determination module, configured to determine at least two of the multiple emergency power generation equipment as joint power supply equipment in a current control cycle according to the estimated remaining lifespans of the multiple emergency power generation equipment;

[0025] The power supply ratio optimization module is used to optimize the power supply ratio of each joint power supply device by using a preset optimization algorithm, wherein the optimization algorithm converges when the difference between the total power generation of each joint power supply device and the total power supply demand of the current control cycle is minimized, and the total power generation of each joint power supply device is equal to the sum of the products of the estimated power generation of each joint power supply device and the power supply ratio of the corresponding joint power supply device.

[0026] Furthermore, the power supply equipment determination module includes:

[0027] An average life calculation unit, used to calculate the average remaining life of the multiple emergency power generation equipment;

[0028] an equipment quantity determination unit, configured to determine whether the number of emergency power generation equipment having an estimated remaining life value greater than the average remaining life value among the plurality of emergency power generation equipment is greater than or equal to two;

[0029] A power supply equipment determination unit is used to, when determining whether the number of emergency power generation equipment whose estimated remaining life is greater than the average remaining life among the multiple emergency power generation equipment is greater than or equal to two, take the emergency power generation equipment whose estimated remaining life is greater than the average remaining life among the multiple emergency power generation equipment as a combined power supply equipment in the current control cycle.

[0030] Furthermore, the multiple emergency power generation equipment includes wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generators, hydrogen energy equipment and fuel cell equipment; the machine learning algorithm is a neural network algorithm, and the preset optimization algorithm is a genetic algorithm;

[0031] The combined power supply control system further comprises:

[0032] The emergency power generation control module is used to use the emergency power generation device with the largest estimated remaining life as the joint power supply device in the current control cycle when the number of emergency power generation devices with the estimated remaining life greater than the average remaining life is less than two among the multiple emergency power generation devices;

[0033] The power supply equipment determination unit is further used to sort the emergency power generation equipment whose estimated remaining life value is greater than the average remaining life value among the multiple emergency power generation equipment in descending order according to the estimated power generation amount of the current control cycle, and select the top N emergency power generation equipment in the sorting as the first priority joint power supply equipment in the current control cycle; N is a preset value;

[0034] The power supply ratio optimization module is also used to optimize the power supply ratio of each first priority joint power supply device by using a preset optimization algorithm.

[0035] In a third aspect, the present invention provides a computer-readable storage medium, which, when instructions in the computer-readable storage medium are executed by at least one processor, prompts the at least one processor to execute the method for controlling joint power supply of multiple emergency power generation equipment.

[0036] In a fourth aspect, the present invention provides a computer device, comprising:

[0037] at least one processor;

[0038] at least one memory storing computer executable instructions,

[0039] Wherein, when the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to execute the combined power supply control method of multiple emergency power generation equipment.

[0040] The combined power supply control method and system of multiple emergency power generation equipment of the present invention determines at least two of the multiple emergency power generation equipment as combined power supply equipment in the current control cycle according to the estimated values ​​of the remaining life of the multiple emergency power generation equipment; and uses a preset optimization algorithm to optimize the power supply ratio of each combined power supply equipment to meet the total power supply demand, that is, the total power supply demand of the current control cycle is met on the basis of giving priority to the remaining life of each emergency power generation equipment, thereby achieving stable combined power supply of each emergency power generation equipment, and preventing the various emergency power generation equipment from being unable to supply power together due to the premature expiration of the life of a single emergency power generation equipment relative to other power generation equipment, thereby achieving the optimal overall comprehensive performance of each emergency power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 It is a flow chart of a method for controlling joint power supply of various emergency power generation equipment according to an embodiment of the present disclosure.

[0043] Figure 2 It is a schematic block diagram of a joint power supply control system of various emergency power generation equipment according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0045] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

[0046] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0047] Figure 1 FIG. 1 is a flow chart of a method for controlling the combined power supply of multiple emergency power generation equipment according to an embodiment of the present disclosure. Figure 1 As shown, the combined power supply control method of the multiple emergency power generation equipment includes:

[0048] Step 101: Receive the total power demand of the current control cycle. In specific operation, the total power demand can be determined according to the current demand of each load. The specific determination method is not the focus of this disclosure and can refer to the prior art.

[0049] Step 102: Based on the historically sensed environmental information and operating condition information of each emergency power generation device, use the trained machine learning algorithm to predict the estimated power generation and remaining life estimate of each emergency power generation device in the current control cycle.

[0050] Step 103: According to the estimated values ​​of the remaining life of the multiple emergency power generation devices, determine at least two of the multiple emergency power generation devices as joint power supply devices in the current control cycle.

[0051] Step 104: Utilize a preset optimization algorithm to optimize the power supply ratio of each joint power supply device, wherein the optimization algorithm converges when the difference between the total power generation of each joint power supply device and the total power supply demand of the current control cycle is minimized, and the total power generation of each joint power supply device is equal to the sum of the products of the estimated power generation of each joint power supply device and the power supply ratio of the corresponding joint power supply device.

[0052] This embodiment determines at least two of the multiple emergency power generation equipment as joint power supply equipment in the current control cycle based on the estimated values ​​of the remaining life of the multiple emergency power generation equipment; and uses a preset optimization algorithm to optimize the power supply ratio of each joint power supply equipment to meet the total power supply demand, that is, the total power supply demand in the current control cycle is met on the basis of giving priority to the remaining life of each emergency power generation equipment, thereby achieving stable joint power supply of each emergency power generation equipment, and preventing the various emergency power generation equipment from being unable to jointly supply power due to the premature expiration of the life of a single emergency power generation equipment, thereby achieving the optimal overall comprehensive performance of each emergency power supply.

[0053] The above-mentioned combined power supply control method for multiple emergency power generation equipment also includes at least one of the following multiple preferred embodiments:

[0054] The first type: the step of determining at least two of the multiple emergency power generation devices as joint power supply devices in the current control cycle according to the average value of the remaining life of the multiple emergency power generation devices comprises:

[0055] Calculating the average remaining life of the multiple emergency power generation equipment;

[0056] Determining whether the number of emergency power generation devices having estimated remaining life values ​​greater than the remaining life average value among the plurality of emergency power generation devices is greater than or equal to two;

[0057] When determining whether the number of emergency power generation devices among the multiple emergency power generation devices whose estimated remaining life is greater than the average remaining life is greater than or equal to two, the emergency power generation devices among the multiple emergency power generation devices whose estimated remaining life is greater than the average remaining life are used as joint power supply devices within the current control cycle.

[0058] The second type: The various emergency power generation equipment include wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generators, hydrogen energy equipment and fuel cell equipment.

[0059] The third type: the machine learning algorithm is a neural network algorithm. In specific operation, according to the historically sensed environmental information of each emergency power generation equipment (such as wind resource data, wind speed, wind direction, weather change information, etc.) and operating condition information (such as speed, torque, pitch angle, yaw angle, etc.), the specific implementation process of using the trained neural network to predict the estimated power generation and the estimated value of the remaining life of each emergency power generation equipment in the current control cycle can also refer to the existing technology.

[0060] The preset optimization algorithm is a genetic algorithm. The genetic algorithm is an existing algorithm structure, which is briefly described as follows:

[0061] A genetic algorithm model is used to randomly generate multiple individuals as the initial group;

[0062] Each individual is a multi-dimensional vector, and the dimension of the vector is the same as the number of emergency power generation equipment. For example, when the multiple emergency power generation equipment includes wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generator, hydrogen energy equipment and fuel cell equipment, the dimension of each individual is 6, which is expressed as a vector (G 1 ,G 2 ,G 3 ,G 4 ,G 5 ,G 6 ), the vector G 1 ,G 2 ,G 3 ,G 4 ,G 5 ,G 6 The power supply proportions corresponding to wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generators, hydrogen energy equipment and fuel cell equipment respectively;

[0063] If the estimated power generation of wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generator, hydrogen energy equipment and fuel cell equipment in the current control cycle is expressed as F 1 ,F 2 ,F 3 ,F 4 ,F 5 ,F 6 , the total power demand in the current control cycle is expressed as Q, then according to 1 / ((F 1 G 1 +F 2 G 2 +F 3 G3 +F 4 G 4 +F 5 G 5 +F 6 G 6 )-Q) Calculate the fitness of each individual;

[0064] The genetic algorithm model is used to continuously optimize the calculation until the genetic algorithm model converges, and the individuals with the maximum fitness are obtained as the optimal power supply ratio of wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generators, hydrogen energy equipment and fuel cell equipment.

[0065] In a fourth embodiment, after the step of calculating the average remaining life of the plurality of emergency power generation equipment, the step further includes:

[0066] When the number of emergency power generation devices whose estimated remaining life is greater than the average remaining life is less than two among the multiple emergency power generation devices, the emergency power generation device with the largest estimated remaining life is used as the joint power supply device in the current control cycle;

[0067] Fifth, the step of using the emergency power generation equipment whose estimated remaining life is greater than the average remaining life among the multiple emergency power generation equipment as the joint power supply equipment in the current control cycle includes:

[0068] The emergency power generation equipment whose estimated remaining lifespan is greater than the average remaining lifespan is sorted in descending order according to the estimated power generation amount of the current control cycle, and the top N emergency power generation equipment are selected as the first priority joint power supply equipment in the current control cycle; N is a preset value;

[0069] The step of optimizing the power supply ratio of each joint power supply device by using a preset optimization algorithm includes:

[0070] A preset optimization algorithm is used to optimize the power supply ratio of each first priority joint power supply device.

[0071] Figure 2 It is a schematic block diagram of a joint power supply control system of various emergency power generation equipment according to an embodiment of the present disclosure. Figure 1 The embodiment shown can be used to explain this embodiment. Figure 2 Shown: A combined power supply control system for multiple emergency power generation equipment, including:

[0072] The power supply demand receiving module 201 is used to receive the total power supply demand of the current control cycle;

[0073] The comprehensive performance prediction module 202 is used to predict the estimated power generation and the estimated remaining life of each emergency power generation device in the current control cycle based on the historically sensed environmental information and operating condition information of each emergency power generation device using a trained machine learning algorithm;

[0074] A power supply equipment determination module 203, configured to determine at least two of the multiple emergency power generation equipment as joint power supply equipment in a current control cycle according to the estimated remaining lifespans of the multiple emergency power generation equipment;

[0075] The power supply ratio optimization module 204 is used to optimize the power supply ratio of each joint power supply device by using a preset optimization algorithm, wherein the optimization algorithm converges when the difference between the total power generation of each joint power supply device and the total power supply demand of the current control cycle is minimized, and the total power generation of each joint power supply device is equal to the sum of the products of the estimated power generation of each joint power supply device and the power supply ratio of the corresponding joint power supply device.

[0076] Preferably, the power supply equipment determination module includes:

[0077] An average life calculation unit, used to calculate the average remaining life of the multiple emergency power generation equipment;

[0078] an equipment quantity determination unit, configured to determine whether the number of emergency power generation equipment having an estimated remaining life value greater than the average remaining life value among the plurality of emergency power generation equipment is greater than or equal to two;

[0079] A power supply equipment determination unit is used to, when determining whether the number of emergency power generation equipment whose estimated remaining life is greater than the average remaining life among the multiple emergency power generation equipment is greater than or equal to two, take the emergency power generation equipment whose estimated remaining life is greater than the average remaining life among the multiple emergency power generation equipment as a combined power supply equipment in the current control cycle.

[0080] Preferably, the multiple emergency power generation equipment includes wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generators, hydrogen energy equipment and fuel cell equipment; the machine learning algorithm is a neural network algorithm, and the preset optimization algorithm is a genetic algorithm;

[0081] The combined power supply control system further comprises:

[0082] An emergency power generation control module (not shown in the figure) is used to use the emergency power generation device with the largest estimated remaining life as a joint power supply device in the current control cycle when the number of emergency power generation devices whose estimated remaining life is greater than the average remaining life among the multiple emergency power generation devices is less than two.

[0083] Preferably, the power supply equipment determination unit is also used to sort the emergency power generation equipment whose estimated remaining life is greater than the average remaining life among the multiple emergency power generation equipment in descending order according to the estimated power generation in the current control cycle, and select the top N emergency power generation equipment in the sorting as the first priority joint power supply equipment in the current control cycle; N is a preset value; the power supply ratio optimization module is also used to optimize the power supply ratio of each first priority joint power supply equipment using a preset optimization algorithm.

[0084] This embodiment determines at least two of the multiple emergency power generation equipment as joint power supply equipment in the current control cycle based on the estimated values ​​of the remaining life of the multiple emergency power generation equipment; and uses a preset optimization algorithm to optimize the power supply ratio of each joint power supply equipment to meet the total power supply demand, that is, the total power supply demand in the current control cycle is met on the basis of giving priority to the remaining life of each emergency power generation equipment, thereby achieving stable joint power supply of each emergency power generation equipment, and preventing the various emergency power generation equipment from being unable to jointly supply power due to the premature expiration of the life of a single emergency power generation equipment, thereby achieving the optimal overall comprehensive performance of each emergency power supply.

[0085] The present invention also provides a computer device, comprising: at least one processor; at least one memory storing computer executable instructions, wherein the computer executable instructions, when executed by the at least one processor, prompt the at least one processor to execute the combined power supply control method for multiple emergency power generation equipment.

[0086] The present invention also provides a computer-readable medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor executes the combined power supply control method for multiple emergency power generation equipment.

[0087] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0088] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0089] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0090] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0091] It should be noted that not all steps and modules in the above-mentioned processes and system structure diagrams are necessary, and some steps or modules can be ignored according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or some components in multiple independent devices may be implemented together.

[0092] In the above embodiments, the hardware unit can be realized by mechanical means or electrical means. For example, a hardware unit can include permanent dedicated circuits or logic (such as special processors, FPGA or ASIC) to complete the corresponding operation. The hardware unit can also include programmable logic or circuits (such as general-purpose processors or other programmable processors), which can be temporarily set by software to complete the corresponding operation. Concrete implementation (mechanical means or dedicated permanent circuits or temporarily set circuits) can be determined based on cost and time considerations.

[0093] The present invention is shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the protection scope of the present invention.

Claims

1. A method for controlling the joint power supply of multiple emergency power generation equipment, characterized in that: include: Receive the total power demand of the current control cycle; Based on the historically sensed environmental information and operating condition information of each emergency power generation device, the trained machine learning algorithm is used to predict the estimated power generation and the estimated remaining life of each emergency power generation device in the current control cycle; Determining, according to the estimated remaining lifespans of the multiple emergency power generation devices, at least two of the multiple emergency power generation devices as joint power supply devices in the current control cycle; The power supply ratio of each joint power supply device is optimized by using a preset optimization algorithm, wherein the optimization algorithm converges when the difference between the total power generation of each joint power supply device and the total power supply demand of the current control cycle is minimized, and the total power generation of each joint power supply device is equal to the sum of the products of the power generation estimate of each joint power supply device and the power supply ratio of the corresponding joint power supply device. The step of determining at least two of the multiple emergency power generation devices as joint power supply devices in the current control cycle according to the average remaining lifespan of the multiple emergency power generation devices comprises: Calculating the average remaining life of the multiple emergency power generation equipment; Determining whether the number of emergency power generation devices having estimated remaining life values ​​greater than the average remaining life value among the plurality of emergency power generation devices is greater than or equal to two; When determining whether the number of emergency power generation devices whose estimated remaining life is greater than the average remaining life is greater than or equal to two among the multiple emergency power generation devices, the emergency power generation devices whose estimated remaining life is greater than the average remaining life are used as joint power supply devices in the current control cycle. The machine learning algorithm is a neural network algorithm, and the preset optimization algorithm is a genetic algorithm; The step of using the emergency power generation equipment whose estimated remaining life value is greater than the remaining life average value among the multiple emergency power generation equipment as the joint power supply equipment in the current control cycle comprises: The emergency power generation equipment whose estimated remaining life is greater than the average remaining life is sorted in descending order according to the estimated power generation in the current control cycle, and the first N emergency power generation equipment are selected as the first priority joint power supply equipment in the current control cycle; N is a preset value; the step of optimizing the power supply ratio of each joint power supply equipment by using a preset optimization algorithm includes: A preset optimization algorithm is used to optimize the power supply ratio of each first priority joint power supply device.

2. The combined power supply control method for multiple emergency power generation equipment according to claim 1 is characterized in that: The various emergency power generation equipment include wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generators, hydrogen energy equipment and fuel cell equipment.

3. The combined power supply control method for multiple emergency power generation equipment according to claim 2 is characterized in that: After the step of calculating the average remaining life of the plurality of emergency power generation equipment, the following step is further included: When the number of emergency power generation devices having estimated remaining life values ​​greater than the average remaining life value among the multiple emergency power generation devices is less than two, the emergency power generation device having the largest estimated remaining life value is used as the joint power supply device in the current control cycle.

4. A combined power supply control system for multiple emergency power generation equipment, characterized in that: include: A power supply demand receiving module is used to receive the total power supply demand of the current control cycle; The comprehensive performance prediction module is used to predict the estimated power generation and the estimated remaining life of each emergency power generation equipment in the current control cycle based on the historically sensed environmental information and operating condition information of each emergency power generation equipment using the trained machine learning algorithm; A power supply equipment determination module, configured to determine at least two of the multiple emergency power generation equipment as joint power supply equipment in a current control cycle according to the estimated remaining lifespans of the multiple emergency power generation equipment; The power supply ratio optimization module is used to optimize the power supply ratio of each joint power supply device by using a preset optimization algorithm, wherein the optimization algorithm converges when the difference between the total power generation of each joint power supply device and the total power supply demand of the current control cycle is minimized, and the total power generation of each joint power supply device is equal to the sum of the product of the power generation estimate of each joint power supply device and the power supply ratio of the corresponding joint power supply device. The power supply equipment determination module includes: An average life calculation unit, used to calculate the average remaining life of the multiple emergency power generation equipment; an equipment quantity determination unit, configured to determine whether the number of emergency power generation equipment having an estimated remaining life value greater than the average remaining life value among the plurality of emergency power generation equipment is greater than or equal to two; a power supply equipment determination unit, configured to, when determining whether the number of emergency power generation equipment whose estimated remaining life is greater than the average remaining life is greater than or equal to two among the multiple emergency power generation equipment, use the emergency power generation equipment whose estimated remaining life is greater than the average remaining life as a joint power supply equipment in a current control cycle, The various emergency power generation equipment include wind power generation equipment, photovoltaic power generation equipment, energy storage equipment, emergency diesel generators, hydrogen energy equipment and fuel cell equipment; the machine learning algorithm is a neural network algorithm, and the preset optimization algorithm is a genetic algorithm; The combined power supply control system further comprises: The emergency power generation control module is used to use the emergency power generation device with the largest estimated remaining life as the joint power supply device in the current control cycle when the number of emergency power generation devices with the estimated remaining life greater than the average remaining life is less than two among the multiple emergency power generation devices; The power supply equipment determination unit is further used to sort the emergency power generation equipment whose estimated remaining life value is greater than the average remaining life value among the multiple emergency power generation equipment in descending order according to the estimated power generation amount of the current control cycle, and select the top N emergency power generation equipment in the sorting as the first priority joint power supply equipment in the current control cycle; N is a preset value; The power supply ratio optimization module is also used to optimize the power supply ratio of each first priority joint power supply device by using a preset optimization algorithm.

5. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to execute the joint power supply control method for multiple emergency power generation equipment as described in any one of claims 1-3.

6. A computer device, characterized in that: include: at least one processor; at least one memory storing computer executable instructions, Wherein, when the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to execute the joint power supply control method for multiple emergency power generation equipment as described in any one of claims 1-3.

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