Intelligent management method and system for fire prevention and control of energy storage power station

Abstract

The invention relates to a fire prevention and control intelligent management method and system for energy storage power stations. The method comprises the following steps: acquiring real-time data of each region of each energy storage power station in real time; substituting the real-time data of each area of each energy storage power station into the fire prevention and control model, and outputting a group of prevention and control management schemes; according to the prevention and control management scheme, fire prevention and control management operation is executed on at least one energy storage power station matched with the prevention and control management scheme, and an execution result of the at least one energy storage power station and real-time data of each energy storage power station are obtained; substituting the execution result of the at least one energy storage power station and the real-time data of each energy storage power station into the fire prevention and control model, and selectively adjusting the fire prevention and control model; therefore, optimization of the fire prevention and control model is realized, the rationality of the prevention and control management scheme output by the fire prevention and control model is ensured, the fire risk of the energy storage power station is effectively prevented and controlled, and intelligent management of fire prevention and control of multiple energy storage power stations at the same time is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and in particular to the technical field of fire prevention and control intelligent management of energy storage power stations, and in particular to a fire prevention and control intelligent management method and system for energy storage power stations. Background Art

[0002] With the rapid development of renewable energy technology, more and more electric energy comes from renewable energy sources such as solar energy and wind energy. These renewable energy sources have certain safety risks during storage and transportation, especially in high temperature and high humidity environments, which can easily cause fires. Therefore, fire prevention and control issues in energy storage power stations are particularly important.

[0003] The existing prevention and control management methods mainly rely on manual inspection and monitoring, which has certain manpower and material costs, and cannot respond to and handle emergencies in a timely manner.

[0004] Therefore, the art needs a new intelligent management solution for fire prevention and control of energy storage power stations to solve the above problems. Summary of the invention

[0005] Based on this, the present invention proposes an intelligent management method and system for fire prevention and control in energy storage power stations, which solves the technical problems that the prevention and control management methods in the prior art mainly rely on manual inspection and monitoring, have certain manpower and material costs, and cannot respond to and handle emergencies in a timely manner.

[0006] According to a first aspect of the present invention, a fire prevention and control intelligent management method for an energy storage power station is provided. The method is applied to a fire prevention and control intelligent management system, wherein the system interacts with information between various areas in a plurality of energy storage power stations, and the method comprises the following steps:

[0007] Acquire real-time data of each area of ​​each energy storage power station in real time, wherein the real-time data includes real-time operation data of the power station and real-time prevention and control data of the power station;

[0008] Substituting the real-time data of each area of ​​each energy storage power station into the fire prevention and control model, so that according to the real-time data of each area of ​​each energy storage power station, it is judged whether each energy storage power station needs fire prevention and control, and according to the judgment result, multiple groups of prevention and control management plans are generated, and one group of prevention and control management plans is selected, wherein the prevention and control management plan includes the prevention and control combination parameters of at least one area of ​​at least one energy storage power station and the prevention and control execution time;

[0009] According to the prevention and control management scheme, perform fire prevention and control management operations on at least one energy storage power station that matches the prevention and control management scheme, and obtain the execution result of the at least one energy storage power station and the real-time data of each energy storage power station;

[0010] The execution result of the at least one energy storage power station and the real-time data of each energy storage power station are substituted into the fire prevention and control model, and the fire prevention and control model is selectively adjusted.

[0011] According to an embodiment of the present invention, a preset energy storage power station history database is stored in the fire prevention and control model. Before substituting the real-time data of each area of ​​each energy storage power station into the fire prevention and control model, the method further trains the fire prevention and control model through the following steps:

[0012] Taking the energy storage power station as a training sample, generating multiple sets of training sample sets according to a preset energy storage power station history database, wherein each set of training sample sets includes real-time training data of various regions of multiple training samples;

[0013] Train each set of training samples in turn:

[0014] According to one of the training sample sets, determine whether each training sample in the training sample set needs fire prevention and control:

[0015] If the judgment is yes, then generating multiple groups of prevention and control management plans based on the training samples that need fire prevention and control in the training sample set, and selecting and outputting one group of prevention and control management plans;

[0016] If the judgment is no, generating an estimated data set corresponding to the training sample set according to the training sample set, wherein the estimated data set includes estimated data of each area of ​​each training sample in the training sample set;

[0017] According to the estimated data set corresponding to the training sample set, a set of estimated prevention and control management solutions is selectively output, or, each training sample corresponding to the training sample set is outputted as not requiring fire prevention and control.

[0018] According to an embodiment of the present invention, the energy storage power station history database stores information of each energy storage power station and historical real-time data of each area of ​​each energy storage power station at each time, and the historical real-time data includes real-time operation data of the energy storage power station and real-time prevention and control data of the energy storage power station. The energy storage power station is used as a training sample, and multiple sets of training sample sets are generated according to the preset energy storage power station history database, including:

[0019] Using the energy storage power station as a training sample, randomly selecting multiple moments;

[0020] According to the multiple moments, selecting historical real-time data of each area of ​​each energy storage power station corresponding to each moment in a preset energy storage power station historical database;

[0021] The historical real-time data of each area of ​​each energy storage power station corresponding to each moment is used as the training real-time data of each area of ​​multiple training samples in the training sample set corresponding to each moment to obtain multiple groups of training sample sets.

[0022] According to an embodiment of the present invention, the generating of multiple groups of prevention and control management schemes based on the training samples requiring fire prevention and control in the training sample set, and selecting a group of prevention and control management schemes and outputting the selected group of prevention and control management schemes include:

[0023] Determining at least one training sample in the training sample set that requires fire prevention and control;

[0024] Determine the prevention and control parameters corresponding to the training sample according to the real-time data of each area of ​​the training sample, wherein the prevention and control parameters include at least one area in the training sample that requires fire prevention and control, the prevention and control type and the prevention and control level corresponding to each area, so as to obtain the prevention and control parameters of each training sample in the training sample set that requires fire prevention and control;

[0025] According to the prevention and control parameters of each training sample that requires fire prevention and control in the training sample set, multiple groups of prevention and control management plans are generated, and one group of prevention and control management plans is selected and output.

[0026] According to an embodiment of the present invention, the fire prevention and control model further stores a preset energy storage power station prevention and control resource database, and the energy storage power station prevention and control resource database stores historical real-time prevention and control resource data of each area of ​​each energy storage power station at each time. According to the prevention and control parameters of each training sample that needs fire prevention and control in the training sample set, multiple groups of prevention and control management schemes are generated, and a group of prevention and control management schemes are selected and output, including:

[0027] According to the time corresponding to the training sample set, the preset energy storage power station prevention and control resource database, and each training sample requiring fire prevention and control in the training sample set, the historical real-time prevention and control resource data of each training sample at the time;

[0028] According to the prevention and control parameters of each training sample that needs fire prevention and control in the training sample set, it is determined whether the historical real-time prevention and control resource data of each training sample matches the prevention and control parameters of the training sample:

[0029] If the judgment is yes, then generating multiple groups of prevention and control management plans according to the prevention and control parameters of the training samples and the historical real-time prevention and control resource data of the training samples;

[0030] According to multiple groups of prevention and control management plans, select a group of prevention and control management plans and output them;

[0031] or,

[0032] If the judgment is no, then according to the information of each training sample in the preset energy storage power station history database, the prevention and control parameters of the training sample requiring fire prevention and control, the time corresponding to the training sample set and the preset energy storage power station prevention and control resource database, determine a plurality of training samples corresponding to the training sample requiring fire prevention and control;

[0033] Generate multiple groups of prevention and control management plans according to multiple training samples corresponding to the training samples required for fire prevention and control;

[0034] Based on multiple groups of prevention and control management plans, one group of prevention and control management plans is selected and output.

[0035] According to an embodiment of the present invention, the fire prevention and control model further stores a preset prevention and control parameter comparison library, the prevention and control parameter comparison library includes at least one type of prevention and control resources corresponding to each prevention and control level and a numerical range corresponding to each type, and judging whether the historical real-time prevention and control resource data of each training sample matches the prevention and control parameters of the training sample according to the prevention and control parameters of each training sample that requires fire prevention and control in the training sample set includes:

[0036] According to the prevention and control parameters of each training sample requiring fire prevention and control in the training sample set, determining at least one type of required prevention and control resources corresponding to each training sample requiring fire prevention and control in the training sample set and a minimum value of each type;

[0037] If the historical real-time prevention and control resource data of the training sample that needs fire prevention and control in the training sample set meets all types of required prevention and control resources corresponding to the training sample, and the value of each type in the historical real-time prevention and control resource data exceeds the minimum value of the type, then it is determined that the historical real-time prevention and control resource data of the training sample matches the prevention and control parameters of the training sample;

[0038] Otherwise, it is determined that the historical real-time prevention and control resource data of the training sample does not match the prevention and control parameters of the training sample.

[0039] According to an embodiment of the present invention, the determining of the plurality of training samples corresponding to the training samples requiring fire prevention and control according to the information of each training sample in the preset energy storage power station history database, the prevention and control parameters of the training samples requiring fire prevention and control, the time corresponding to the training sample set, and the preset energy storage power station prevention and control resource database includes:

[0040] Determine multiple training samples around the training sample requiring fire prevention and control according to the information of each training sample in the preset energy storage power station history database;

[0041] Screening a plurality of training samples around the training sample requiring fire prevention and control to obtain a plurality of screened training samples around the training sample requiring fire prevention and control;

[0042] According to the multiple selected training samples around the training samples requiring fire prevention and control, the time corresponding to the training sample set, and the preset energy storage power station prevention and control resource database, the historical real-time prevention and control resource data of each selected training sample around the training samples requiring fire prevention and control at the time;

[0043] According to the historical real-time prevention and control resource data of each screened training sample around the training sample requiring fire prevention and control at the moment and the prevention and control parameters of the training sample requiring fire prevention and control, each screened training sample around the training sample requiring fire prevention and control is screened twice to obtain a plurality of re-screened training samples around the training sample requiring fire prevention and control;

[0044] The multiple re-screened training samples around the training sample requiring fire prevention and control are used as the multiple training samples corresponding to the training sample requiring fire prevention and control.

[0045] According to an embodiment of the present invention, selectively outputting a set of estimated prevention and control management solutions based on the estimated data set corresponding to the training sample set, or outputting that each training sample corresponding to the training sample set does not require fire prevention and control includes:

[0046] According to the estimated data set corresponding to the training sample set, it is determined whether each training sample in the estimated data set needs estimated prevention and control:

[0047] If the judgment is yes, then generating multiple groups of estimated prevention and control management plans according to the training samples requiring fire prevention and control in the estimated data set, and selecting and outputting one group of estimated prevention and control management plans;

[0048] If the judgment is no, then each training sample corresponding to the training sample set is outputted as not requiring fire prevention and control.

[0049] According to an embodiment of the present invention, the method generates multiple groups of estimated prevention and control management solutions based on the training samples requiring fire prevention and control in the estimated data set, and selects a group of estimated prevention and control management solutions and outputs the solutions, including:

[0050] Generate multiple groups of estimated prevention and control management plans based on the training samples requiring fire prevention and control in the estimated data set;

[0051] Simulate multiple groups of estimated prevention and control management plans to obtain simulation data for each group of estimated prevention and control management plans;

[0052] Generate the control efficiency and cost value corresponding to each group of estimated prevention and control management plans based on the simulation data of each group of estimated prevention and control management plans;

[0053] According to the preset prevention and control category priorities corresponding to the training samples, a group of estimated prevention and control management schemes with the highest prevention and control efficiency or the lowest prevention and control cost value are selectively selected as a group of estimated prevention and control management schemes selected and output.

[0054] According to a second aspect of the present invention, there is provided a fire prevention and control intelligent management system, the system comprising a communication device, a fire prevention and control model and a control device, the communication device being communicatively connected to each area in a plurality of energy storage power stations for realizing information interaction between the system and each area in the plurality of energy storage power stations, the fire prevention and control model storing a preset energy storage power station history database, a preset prevention and control parameter comparison library and a preset energy storage power station prevention and control resource database, the control device comprising a processor and a memory, the memory being suitable for storing a plurality of program codes, the program codes being suitable for being loaded and run by the processor to execute the fire prevention and control intelligent management method for an energy storage power station described in any one of the technical solutions of the above-mentioned fire prevention and control intelligent management method for an energy storage power station.

[0055] It can be seen from the above technical solutions that the fire prevention and control intelligent management method of the energy storage power station provided by the present invention has the following beneficial effects:

[0056] By substituting the real-time data of each area of ​​each energy storage power station obtained in real time into the fire prevention and control model, it is possible to judge whether each energy storage power station needs fire prevention and control based on the real-time data of each area of ​​each energy storage power station, and generate multiple groups of prevention and control management plans based on the judgment results, and select a group of prevention and control management plans, thereby realizing automatic generation and automatic screening of prevention and control management plans to obtain the optimal prevention and control management plan, and after the prevention and control management plan is executed, the execution result and real-time data of each energy storage power station are obtained and brought to the fire prevention and control model, and the fire prevention and control model is selectively adjusted to achieve optimization of the fire prevention and control model, thereby ensuring the rationality of the prevention and control management plan output by the fire prevention and control model, so that the fire prevention and control model can better adapt to the actual situation, thereby effectively preventing and controlling the fire risk of the energy storage power station, thereby realizing intelligent management of fire prevention and control of multiple energy storage power stations at the same time, avoiding the technical problem that the prevention and control management method in the existing technology mainly relies on manual inspection and monitoring, has certain manpower and material costs, and cannot respond to and handle emergencies in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which:

[0058] Figure 1 This is a schematic flow chart of the main steps of a fire prevention and control intelligent management method for an energy storage power station according to an embodiment of the present invention;

[0059] Figure 2 It is a flowchart of the main steps of training a fire prevention and control model of a fire prevention and control intelligent management method of an energy storage power station according to one embodiment of the present invention;

[0060] Figure 3 It is a schematic diagram of the main structural block diagram of a fire prevention and control intelligent management system according to an embodiment of the present invention.

[0061] List of reference numerals:

[0062] 300: Fire prevention and control intelligent management system; 301: Control device; 3011: Processor; 3012: Memory; 3013: Program code; 302: Fire prevention and control model; 3021: Energy storage power station historical database; 3022: Prevention and control parameter comparison library; 3023: Energy storage power station prevention and control resource database; 303: Communication device. DETAILED DESCRIPTION

[0063] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0064] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program codes, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and the like. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and may include only A, only B, or A and B. The singular terms "one" and "the" may also include plural forms.

[0065] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a fire prevention and control intelligent management method for an energy storage power station according to an embodiment of the present invention. Figure 1 As shown, a fire prevention and control intelligent management method for an energy storage power station in an embodiment of the present invention is applied to a fire prevention and control intelligent management system, and the system interacts with information between various areas in multiple energy storage power stations. The method mainly includes the following steps S101-step S104.

[0066] Step S101: acquiring real-time data of each area of ​​each energy storage power station in real time, wherein the real-time data includes real-time operation data of the power station and real-time prevention and control data of the power station;

[0067] Step S102: Substitute the real-time data of each area of ​​each energy storage power station into the fire prevention and control model, so that according to the real-time data of each area of ​​each energy storage power station, it is judged whether each energy storage power station needs fire prevention and control, and according to the judgment result, multiple groups of prevention and control management plans are generated, and one group of prevention and control management plans is selected, wherein the prevention and control management plan includes the prevention and control combination parameters of at least one area of ​​at least one energy storage power station and the prevention and control execution time;

[0068] like Figure 2 As shown, further, a preset energy storage power station historical database is stored in the fire prevention and control model, and the historical real-time data includes the real-time operation data of the energy storage power station and the real-time prevention and control data of the energy storage power station; before substituting the real-time data of each area of ​​each energy storage power station into the fire prevention and control model, the method further trains the fire prevention and control model through the following steps:

[0069] Taking the energy storage power station as a training sample, generating multiple sets of training sample sets according to a preset energy storage power station history database, wherein each set of training sample sets includes real-time training data of various regions of multiple training samples;

[0070] Train each set of training samples in turn:

[0071] According to one of the training sample sets, determine whether each training sample in the training sample set needs fire prevention and control:

[0072] If the judgment is yes, then generating multiple groups of prevention and control management plans based on the training samples that need fire prevention and control in the training sample set, and selecting and outputting one group of prevention and control management plans;

[0073] If the judgment is no, generating an estimated data set corresponding to the training sample set according to the training sample set, wherein the estimated data set includes estimated data of each area of ​​each training sample in the training sample set;

[0074] According to the estimated data set corresponding to the training sample set, a set of estimated prevention and control management solutions is selectively output, or, each training sample corresponding to the training sample set is outputted as not requiring fire prevention and control.

[0075] Specifically, generating an estimated data set corresponding to the training sample set according to the training sample set includes:

[0076] The training sample set is input into a preset energy storage power station estimated operation model, and the energy storage power station estimated operation model outputs an estimated data set corresponding to the training sample set.

[0077] Specifically, the preset energy storage power station estimated operation model can be a simulation technology in the prior art. The technical selection of the preset energy storage power station estimated operation model here is only an exemplary description. In actual testing, technical personnel in this field can make a selection according to actual needs. As long as it is possible to input the preset energy storage power station estimated operation model through the training sample set and output the estimated data set corresponding to the training sample set, it will not be repeated here.

[0078] Specifically, the energy storage power station history database stores information of each energy storage power station and historical real-time data of each area of ​​each energy storage power station at each time, and the historical real-time data includes real-time operation data of the energy storage power station and real-time prevention and control data of the energy storage power station. The energy storage power station is used as a training sample, and multiple sets of training sample sets are generated according to the preset energy storage power station history database, including:

[0079] Using the energy storage power station as a training sample, randomly selecting multiple moments;

[0080] According to the multiple moments, selecting historical real-time data of each area of ​​each energy storage power station corresponding to each moment in a preset energy storage power station historical database;

[0081] The historical real-time data of each area of ​​each energy storage power station corresponding to each moment is used as the training real-time data of each area of ​​multiple training samples in the training sample set corresponding to each moment to obtain multiple groups of training sample sets.

[0082] Specifically, the information of the energy storage power station includes at least the location information of the energy storage power station, the prevention and control unit cost, and the prevention and control speed.

[0083] Specifically, the method generates multiple groups of prevention and control management plans based on the training samples that need fire prevention and control in the training sample set, and selects a group of prevention and control management plans and outputs them, including:

[0084] Determining at least one training sample in the training sample set that requires fire prevention and control;

[0085] Determine the prevention and control parameters corresponding to the training sample according to the real-time data of each area of ​​the training sample, wherein the prevention and control parameters include at least one area in the training sample that requires fire prevention and control, the prevention and control type and the prevention and control level corresponding to each area, so as to obtain the prevention and control parameters of each training sample in the training sample set that requires fire prevention and control;

[0086] According to the prevention and control parameters of each training sample that requires fire prevention and control in the training sample set, multiple groups of prevention and control management plans are generated, and one group of prevention and control management plans is selected and output.

[0087] Specifically, the fire prevention and control model also stores a preset prevention and control parameter comparison library, which includes multiple prevention and control types, multiple prevention and control levels corresponding to each prevention and control type, and an operating data range and a prevention and control data range corresponding to each prevention and control level;

[0088] Determining the prevention and control parameters corresponding to the training samples according to the real-time data of each area of ​​the training samples includes:

[0089] The prevention and control parameters corresponding to the training samples are determined according to the real-time data of each area of ​​the training samples and a preset prevention and control parameter comparison library.

[0090] Specifically, the fire prevention and control model also stores a preset energy storage power station prevention and control resource database, which stores historical real-time prevention and control resource data of each area of ​​each energy storage power station at each time. According to the prevention and control parameters of each training sample that needs fire prevention and control in the training sample set, multiple groups of prevention and control management plans are generated, and a group of prevention and control management plans are selected and output including:

[0091] According to the time corresponding to the training sample set, the preset energy storage power station prevention and control resource database, and each training sample requiring fire prevention and control in the training sample set, the historical real-time prevention and control resource data of each training sample at the time;

[0092] According to the prevention and control parameters of each training sample that needs fire prevention and control in the training sample set, it is determined whether the historical real-time prevention and control resource data of each training sample matches the prevention and control parameters of the training sample:

[0093] If the judgment is yes, then generating multiple groups of prevention and control management plans according to the prevention and control parameters of the training samples and the historical real-time prevention and control resource data of the training samples;

[0094] According to multiple groups of prevention and control management plans, select a group of prevention and control management plans and output them;

[0095] or,

[0096] If the judgment is no, then according to the information of each training sample in the preset energy storage power station history database, the prevention and control parameters of the training sample requiring fire prevention and control, the time corresponding to the training sample set and the preset energy storage power station prevention and control resource database, determine a plurality of training samples corresponding to the training sample requiring fire prevention and control;

[0097] Generate multiple groups of prevention and control management plans according to multiple training samples corresponding to the training samples required for fire prevention and control;

[0098] Based on multiple groups of prevention and control management plans, one group of prevention and control management plans is selected and output.

[0099] Specifically, the control parameter comparison library also includes at least one type of control resources corresponding to each control level and a numerical range corresponding to each type. The control parameters of each training sample requiring fire control in the training sample set, judging whether the historical real-time control resource data of each training sample matches the control parameters of the training sample, includes:

[0100] According to the prevention and control parameters of each training sample requiring fire prevention and control in the training sample set, determining at least one type of required prevention and control resources corresponding to each training sample requiring fire prevention and control in the training sample set and a minimum value of each type;

[0101] If the historical real-time prevention and control resource data of the training sample that needs fire prevention and control in the training sample set meets all types of required prevention and control resources corresponding to the training sample, and the value of each type in the historical real-time prevention and control resource data exceeds the minimum value of the type, then it is determined that the historical real-time prevention and control resource data of the training sample matches the prevention and control parameters of the training sample;

[0102] Otherwise, it is determined that the historical real-time prevention and control resource data of the training sample does not match the prevention and control parameters of the training sample.

[0103] Specifically, the method for generating the multiple groups of prevention and control management solutions may adopt a trained neural network model or a trained machine learning technology. The selection of the method for generating the multiple groups of prevention and control management solutions here is only an exemplary description. In actual tests, those skilled in the art may make a selection according to actual needs. As long as it is possible to generate multiple groups of prevention and control management solutions through multiple training samples corresponding to the training samples requiring fire prevention and control, or to generate multiple groups of prevention and control management solutions through the prevention and control parameters of the training samples and the historical real-time prevention and control resource data of the training samples, it will not be repeated here.

[0104] Specifically, the determining of the plurality of training samples corresponding to the training samples requiring fire prevention and control according to the information of each training sample in the preset energy storage power station history database, the prevention and control parameters of the training samples requiring fire prevention and control, the time corresponding to the training sample set, and the preset energy storage power station prevention and control resource database includes:

[0105] Determine multiple training samples around the training sample requiring fire prevention and control according to the information of each training sample in the preset energy storage power station history database;

[0106] Screening a plurality of training samples around the training sample requiring fire prevention and control to obtain a plurality of screened training samples around the training sample requiring fire prevention and control;

[0107] According to the multiple selected training samples around the training samples requiring fire prevention and control, the time corresponding to the training sample set, and the preset energy storage power station prevention and control resource database, the historical real-time prevention and control resource data of each selected training sample around the training samples requiring fire prevention and control at the time;

[0108] According to the historical real-time prevention and control resource data of each screened training sample around the training sample requiring fire prevention and control at the moment and the prevention and control parameters of the training sample requiring fire prevention and control, each screened training sample around the training sample requiring fire prevention and control is screened twice to obtain a plurality of re-screened training samples around the training sample requiring fire prevention and control;

[0109] The multiple re-screened training samples around the training sample requiring fire prevention and control are used as the multiple training samples corresponding to the training sample requiring fire prevention and control.

[0110] Specifically, according to the information of each training sample in the preset energy storage power station history database, determining multiple training samples around the training sample requiring fire prevention and control includes:

[0111] According to the information of each training sample in the preset energy storage power station history database, the location information of the training sample requiring fire prevention and control and the location information of each training sample are determined,

[0112] If the distance between any training sample and the training sample requiring fire prevention and control is lower than a preset first distance threshold, the training sample is used as one of the training samples around the training sample requiring fire prevention and control;

[0113] Otherwise, the step of using the training sample as one of the training samples around the training sample requiring fire prevention and control is not performed.

[0114] Specifically, the preset first distance threshold may be 1 kilometer or 500 meters. The selection of the preset first distance threshold here is only an exemplary description. In actual testing, those skilled in the art may make a selection according to actual needs, which will not be elaborated here.

[0115] Specifically, a plurality of training samples around the training sample requiring fire prevention and control are screened once, and the screened plurality of training samples around the training sample requiring fire prevention and control are obtained, including:

[0116] Determine the prevention and control speed and the unit cost of each training sample around the training sample requiring fire prevention and control according to the information of each training sample in the preset energy storage power station history database;

[0117] Determine the time required for the prevention and control transportation of each training sample according to the distance between each training sample and the training sample requiring fire prevention and control and the prevention and control speed of each training sample around the training sample requiring fire prevention and control;

[0118] According to the time required for the prevention and control transportation of each training sample and the unit cost of prevention and control, the price required for the prevention and control transportation of each training sample is obtained;

[0119] If the time required for the prevention and control transportation of the training sample exceeds the preset prevention and control transportation time threshold corresponding to the highest level of prevention and control type and the highest level of prevention and control level corresponding to the training sample requiring fire prevention and control, or if the price required for the prevention and control transportation of the training sample exceeds the preset prevention and control transportation price threshold corresponding to the training sample requiring fire prevention and control, then the training sample is eliminated;

[0120] Otherwise, retain the training sample;

[0121] In order to obtain multiple screened training samples around the training samples that require fire prevention and control.

[0122] Specifically, the preset prevention and control transportation time threshold and the preset prevention and control transportation price threshold can be set by the management personnel themselves. In actual testing, technical personnel in this field can choose according to actual needs, which will not be repeated here.

[0123] Specifically, the historical real-time prevention and control resource data of each screened training sample at the moment and the prevention and control parameters of the training sample requiring fire prevention and control are used to screen each screened training sample around the training sample requiring fire prevention and control, and the multiple re-screened training samples around the training sample requiring fire prevention and control are obtained, including:

[0124] According to the prevention and control parameters of the training samples requiring fire prevention and control in the training sample set, determining at least one type of prevention and control resources required corresponding to the training samples requiring fire prevention and control in the training sample set and a minimum value of each type;

[0125] Determine at least one type of prevention and control resources lacking in the training samples requiring fire prevention and control and the lowest difference of each type according to the value of each type in the historical real-time prevention and control resource data, at least one type of required prevention and control resources corresponding to the training samples requiring fire prevention and control in the training sample set, and the lowest value of each type;

[0126] If the historical real-time prevention and control resource data of the training sample meets all types of prevention and control resources that are missing in the training sample that requires fire prevention and control, and the value of each type in the historical real-time prevention and control resource data of the training sample exceeds the minimum difference of the type, the training sample is eliminated;

[0127] Otherwise, retain the training sample;

[0128] In order to obtain multiple re-screened training samples around the training samples that require fire prevention and control.

[0129] Specifically, selectively outputting a set of estimated prevention and control management solutions according to the estimated data set corresponding to the training sample set, or outputting that each training sample corresponding to the training sample set does not require fire prevention and control includes:

[0130] According to the estimated data set corresponding to the training sample set, it is determined whether each training sample in the estimated data set needs estimated prevention and control:

[0131] If the judgment is yes, then generating multiple groups of estimated prevention and control management plans according to the training samples requiring fire prevention and control in the estimated data set, and selecting and outputting one group of estimated prevention and control management plans;

[0132] If the judgment is no, then each training sample corresponding to the training sample set is outputted as not requiring fire prevention and control.

[0133] Specifically, according to the training samples requiring fire prevention and control in the estimated data set, multiple groups of estimated prevention and control management plans are generated, and one group of estimated prevention and control management plans is selected and outputted, including:

[0134] Generate multiple groups of estimated prevention and control management plans based on the training samples requiring fire prevention and control in the estimated data set;

[0135] Simulate multiple groups of estimated prevention and control management plans to obtain simulation data for each group of estimated prevention and control management plans;

[0136] Generate the control efficiency and cost value corresponding to each group of estimated prevention and control management plans based on the simulation data of each group of estimated prevention and control management plans;

[0137] According to the preset prevention and control category priorities corresponding to the training samples, a group of estimated prevention and control management schemes with the highest prevention and control efficiency or the lowest prevention and control cost value are selectively selected as a group of estimated prevention and control management schemes selected and output.

[0138] Specifically, according to the preset prevention and control category priority corresponding to the training sample, a group of estimated prevention and control management solutions with the highest prevention and control efficiency or the lowest prevention and control cost value are selectively selected as a group of estimated prevention and control management solutions selected and outputted, including:

[0139] When the preset prevention and control category priority corresponding to the training sample is prevention and control efficiency, a group of estimated prevention and control management schemes with the highest prevention and control efficiency are selected as a group of estimated prevention and control management schemes and output;

[0140] or,

[0141] When the preset prevention and control category priority corresponding to the training sample is the prevention and control cost, a group of estimated prevention and control management solutions with the lowest prevention and control cost value is selected as a group of estimated prevention and control management solutions selected and outputted.

[0142] Specifically, the simulation method for simulating multiple groups of estimated prevention and control management schemes can adopt the simulation technology in the prior art. The selection of the simulation method for simulating multiple groups of estimated prevention and control management schemes here is only an exemplary description. In actual tests, technical personnel in this field can make a selection according to actual needs. As long as it is possible to simulate multiple groups of estimated prevention and control management schemes and obtain simulation data for each group of estimated prevention and control management schemes, it will not be repeated here.

[0143] Specifically, the prevention and control efficiency corresponding to each group of estimated prevention and control management plans is obtained through the following formula:

[0144] Estimated control efficiency corresponding to the control management plan = control success range value / control execution time;

[0145] In addition, the prevention and control cost value corresponding to each set of estimated prevention and control management plans is obtained through the following formula:

[0146] The prevention and control cost value corresponding to each set of estimated prevention and control management plans = the quantity consumed by each prevention and control type * the sum of the unit values ​​of the prevention and control types.

[0147] In the above embodiment, during model training, multiple sets of training sample sets are obtained through a preset energy storage power station history database, and random acquisition of training sample sets is realized to ensure that the model can accurately process data when processing new data, and the independence of training is ensured by sequentially processing each set of training sample sets to avoid training confusion, thereby causing abnormalities in the fire prevention and control model. According to one set of training sample sets, it is judged whether each training sample in the training sample set needs fire prevention and control. When the judgment is yes, multiple sets of prevention and control management plans are generated according to the training samples that need fire prevention and control in the training sample set, and a set of prevention and control management plans is selected and output, or, when the judgment is no, an estimated data set corresponding to the training sample set is generated according to the training sample set, and then a set of estimated prevention and control management plans is selectively output, or each training sample corresponding to the output training sample set does not need fire prevention and control, thereby realizing automatic generation and screening of prevention and control management plans or estimated prevention and control management plans, so as to ensure that the prevention and control management plan or estimated prevention and control management plan that matches the training sample set and is the best is selected, so that the fire prevention and control model is effectively trained and applied, thereby realizing effective management and control of various energy storage power stations by the fire prevention and control model.

[0148] Step S103: According to the prevention and control management scheme, a fire prevention and control management operation is performed on at least one energy storage power station that matches the prevention and control management scheme, and an execution result of the at least one energy storage power station and real-time data of each energy storage power station are obtained;

[0149] Step S104: Substitute the execution result of the at least one energy storage power station and the real-time data of each energy storage power station into the fire prevention and control model, and selectively adjust the fire prevention and control model.

[0150] Specifically, substituting the execution result of the at least one energy storage power station and the real-time data of each energy storage power station into the fire prevention and control model, and selectively adjusting the fire prevention and control model includes:

[0151] According to the execution results of all energy storage power stations, the execution success rate of all energy storage power stations is obtained;

[0152] Based on the real-time data of each energy storage power station, the average prevention and control success rate of all energy storage power stations is obtained;

[0153] If the execution success rate is lower than a preset execution success rate threshold, or the average prevention and control success rate is lower than a preset average prevention and control success rate threshold, the fire prevention and control model is adjusted, that is, the fire prevention and control model is retrained;

[0154] Otherwise, the fire prevention and control model is not adjusted.

[0155] Specifically, the preset execution success rate threshold can be 95% or 98%, and the preset average prevention and control success rate threshold can be 90% or 95%. The selection of the preset execution success rate threshold and the preset average prevention and control success rate threshold here is only an exemplary description. In actual testing, those skilled in the art can make a selection according to actual needs, which will not be repeated here.

[0156] In the above embodiment, by substituting the execution results of at least one energy storage power station and the real-time data of each energy storage power station into the fire prevention and control model, the fire prevention and control model is selectively adjusted, that is, the fire prevention and control model is retrained to achieve automatic inspection of the implementation results of the fire prevention and control model, and then the fire prevention and control model is selectively retrained, so that the fire prevention and control model can continue to improve, thereby improving the accuracy of the fire prevention and control management plan generated by the fire prevention and control model.

[0157] Based on the above steps S101 to S104, by substituting the real-time data of each area of ​​each energy storage power station obtained in real time into the fire prevention and control model, it is determined whether each energy storage power station needs fire prevention and control according to the real-time data of each area of ​​each energy storage power station, and according to the judgment result, multiple groups of prevention and control management plans are generated, and a group of prevention and control management plans are selected, thereby realizing automatic generation and automatic screening of prevention and control management plans to obtain the optimal prevention and control management plan, and after the prevention and control management plan is executed, the execution result and real-time data of each energy storage power station are obtained and brought to the fire prevention and control model, and the fire prevention and control model is selectively adjusted to achieve optimization of the fire prevention and control model, thereby ensuring the rationality of the prevention and control management plan output by the fire prevention and control model, so that the fire prevention and control model can better adapt to the actual situation, thereby effectively preventing and controlling the fire risk of the energy storage power station, thereby realizing intelligent management of fire prevention and control of multiple energy storage power stations at the same time, avoiding the technical problem that the prevention and control management method in the prior art mainly relies on manual inspection and monitoring, has certain manpower and material costs, and cannot respond to and handle emergencies in a timely manner.

[0158] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0159] Furthermore, the present invention also provides a fire prevention and control intelligent management system.

[0160] See attached Figure 3 , Figure 3 FIG. 1 is a main structural block diagram of a fire prevention and control intelligent management system according to an embodiment of the present invention. Figure 3As shown, a fire prevention and control intelligent management system 300 in an embodiment of the present invention mainly includes a communication device 303, a fire prevention and control model 302 and a control device 301. The communication device 303 is connected to each area in a plurality of energy storage power stations for communication, and is used to realize information interaction between the system and each area in the plurality of energy storage power stations. The fire prevention and control model 302 stores a preset energy storage power station history database 3021, a preset prevention and control parameter comparison library 3022 and a preset energy storage power station prevention and control resource database 3023. The control device 301 includes a processor 3011 and a memory 3012. The memory 3012 can be configured to store a program code 3013 for executing the fire prevention and control intelligent management method of the energy storage power station in the above method embodiment. The processor 3011 can be configured to execute the program code 3013 in the memory 3012, and the program code 3013 includes but is not limited to executing the program code 3013 for the fire prevention and control intelligent management method of the energy storage power station in the above method embodiment. For the convenience of explanation, only the part related to the embodiment of the present invention is shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device 301 may be a control device including various electronic devices.

[0161] Specifically, the energy storage power station historical database 3021 stores information of each energy storage power station and historical real-time data of each area of ​​each energy storage power station at each time;

[0162] The control parameter comparison library 3022 includes multiple control types, multiple control levels corresponding to each control type, an operation data range and a control data range corresponding to each control level, and the control parameter comparison library 3022 also includes at least one type of control resource corresponding to each control level and a numerical range corresponding to each type;

[0163] The energy storage power station prevention and control resource database 3023 stores historical real-time prevention and control resource data of each area of ​​each energy storage power station at each time.

[0164] Specifically, the communication device 303 can be various communication devices of different communication types. The selection of the communication device 303 here is only an exemplary description. In actual testing, technical personnel in this field can make a selection according to actual needs, as long as the communication device 303 can be used to communicate with various areas in multiple energy storage power stations. It will not be repeated here.

[0165] In one implementation, the description of the specific implementation functions may refer to steps S101 to S104.

[0166] The fire prevention and control intelligent management system 300 is used to execute Figure 1The embodiment of the intelligent management method for fire prevention and control in the energy storage power station shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technicians in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the intelligent management system 300 for fire prevention and control can refer to the contents described in the embodiment of the intelligent management method for fire prevention and control in the energy storage power station, which will not be repeated here.

[0167] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor 3011, the steps of each of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electric carrier signal and telecommunication signal.

[0168] Furthermore, the fire prevention and control intelligent management system 300 of the present invention also includes a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program code 3013 for executing the fire prevention and control intelligent management method of the energy storage power station of the above method embodiment, and the program code 3013 can be loaded and run by the processor 3011 to implement the fire prevention and control intelligent management method of the energy storage power station. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0169] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor 3011 itself, or a part of the software, a part of the hardware, or a part of the combination of software and hardware in the processor 3011. Therefore, the number of each module in the figure is only schematic.

[0170] It is understood by those skilled in the art that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0171] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A fire prevention and control intelligent management method for an energy storage power station, characterized in that: The method is applied to a fire prevention and control intelligent management system, wherein the system interacts with various areas in a plurality of energy storage power stations, and the method comprises the following steps: Acquire real-time data of each area of ​​each energy storage power station in real time, wherein the real-time data includes real-time operation data of the power station and real-time prevention and control data of the power station; Substituting the real-time data of each area of ​​each energy storage power station into the fire prevention and control model, so that according to the real-time data of each area of ​​each energy storage power station, it is judged whether each energy storage power station needs fire prevention and control, and according to the judgment result, multiple groups of prevention and control management plans are generated, and one group of prevention and control management plans is selected, wherein the prevention and control management plan includes the prevention and control combination parameters of at least one area of ​​at least one energy storage power station and the prevention and control execution time; According to the prevention and control management scheme, perform fire prevention and control management operations on at least one energy storage power station that matches the prevention and control management scheme, and obtain the execution result of the at least one energy storage power station and the real-time data of each energy storage power station; The execution result of the at least one energy storage power station and the real-time data of each energy storage power station are substituted into the fire prevention and control model, and the fire prevention and control model is selectively adjusted.

2. The fire prevention and control intelligent management method of an energy storage power station according to claim 1 is characterized in that: The fire prevention and control model stores a preset energy storage power station history database. Before substituting the real-time data of each area of ​​each energy storage power station into the fire prevention and control model, the method further trains the fire prevention and control model through the following steps: Taking the energy storage power station as a training sample, generating multiple sets of training sample sets according to a preset energy storage power station history database, wherein each set of training sample sets includes real-time training data of various regions of multiple training samples; Train each set of training samples in turn: According to one of the training sample sets, determine whether each training sample in the training sample set needs fire prevention and control: If the judgment is yes, then generating multiple groups of prevention and control management plans based on the training samples that need fire prevention and control in the training sample set, and selecting and outputting one group of prevention and control management plans; If the judgment is no, generating an estimated data set corresponding to the training sample set according to the training sample set, wherein the estimated data set includes estimated data of each area of ​​each training sample in the training sample set; According to the estimated data set corresponding to the training sample set, a set of estimated prevention and control management solutions is selectively output, or, each training sample corresponding to the training sample set is outputted as not requiring fire prevention and control.

3. The fire prevention and control intelligent management method of an energy storage power station according to claim 2 is characterized in that: The energy storage power station history database stores information of each energy storage power station and historical real-time data of each area of ​​each energy storage power station at each time, and the historical real-time data includes real-time operation data of the energy storage power station and real-time prevention and control data of the energy storage power station. The energy storage power station is used as a training sample, and multiple sets of training sample sets are generated according to the preset energy storage power station history database, including: Using the energy storage power station as a training sample, randomly selecting multiple moments; According to the multiple moments, selecting historical real-time data of each area of ​​each energy storage power station corresponding to each moment in a preset energy storage power station historical database; The historical real-time data of each area of ​​each energy storage power station corresponding to each moment is used as the training real-time data of each area of ​​multiple training samples in the training sample set corresponding to each moment to obtain multiple groups of training sample sets.

4. The fire prevention and control intelligent management method of an energy storage power station according to claim 3 is characterized in that: The method generates multiple groups of prevention and control management plans based on the training samples that need fire prevention and control in the training sample set, and selects a group of prevention and control management plans and outputs them, including: Determining at least one training sample in the training sample set that requires fire prevention and control; Determine the prevention and control parameters corresponding to the training sample according to the real-time data of each area of ​​the training sample, wherein the prevention and control parameters include at least one area in the training sample that requires fire prevention and control, the prevention and control type and the prevention and control level corresponding to each area, so as to obtain the prevention and control parameters of each training sample in the training sample set that requires fire prevention and control; According to the prevention and control parameters of each training sample that requires fire prevention and control in the training sample set, multiple groups of prevention and control management plans are generated, and one group of prevention and control management plans is selected and output.

5. The fire prevention and control intelligent management method of an energy storage power station according to claim 4 is characterized in that: The fire prevention and control model also stores a preset energy storage power station prevention and control resource database, which stores historical real-time prevention and control resource data of each area of ​​each energy storage power station at each time. According to the prevention and control parameters of each training sample that needs fire prevention and control in the training sample set, multiple groups of prevention and control management plans are generated, and a group of prevention and control management plans are selected and output, including: According to the time corresponding to the training sample set, the preset energy storage power station prevention and control resource database, and each training sample requiring fire prevention and control in the training sample set, the historical real-time prevention and control resource data of each training sample at the time; According to the prevention and control parameters of each training sample that needs fire prevention and control in the training sample set, it is determined whether the historical real-time prevention and control resource data of each training sample matches the prevention and control parameters of the training sample: If the judgment is yes, then generating multiple groups of prevention and control management plans according to the prevention and control parameters of the training samples and the historical real-time prevention and control resource data of the training samples; According to multiple groups of prevention and control management plans, select a group of prevention and control management plans and output them; or, If the judgment is no, then according to the information of each training sample in the preset energy storage power station history database, the prevention and control parameters of the training sample requiring fire prevention and control, the time corresponding to the training sample set and the preset energy storage power station prevention and control resource database, determine a plurality of training samples corresponding to the training sample requiring fire prevention and control; Generate multiple groups of prevention and control management plans according to multiple training samples corresponding to the training samples required for fire prevention and control; Based on multiple groups of prevention and control management plans, one group of prevention and control management plans is selected and output.

6. The fire prevention and control intelligent management method of an energy storage power station according to claim 5, characterized in that: The fire prevention and control model also stores a preset prevention and control parameter comparison library, which includes at least one type of prevention and control resources corresponding to each prevention and control level and a numerical range corresponding to each type. The method of judging whether the historical real-time prevention and control resource data of each training sample that needs fire prevention and control in the training sample set matches the prevention and control parameters of the training sample includes: According to the prevention and control parameters of each training sample requiring fire prevention and control in the training sample set, determining at least one type of required prevention and control resources corresponding to each training sample requiring fire prevention and control in the training sample set and a minimum value of each type; If the historical real-time prevention and control resource data of the training sample that needs fire prevention and control in the training sample set meets all types of required prevention and control resources corresponding to the training sample, and the value of each type in the historical real-time prevention and control resource data exceeds the minimum value of the type, then it is determined that the historical real-time prevention and control resource data of the training sample matches the prevention and control parameters of the training sample; Otherwise, it is determined that the historical real-time prevention and control resource data of the training sample does not match the prevention and control parameters of the training sample.

7. The fire prevention and control intelligent management method of an energy storage power station according to claim 6 is characterized in that: The determining of the plurality of training samples corresponding to the training samples requiring fire prevention and control according to the information of each training sample in the preset energy storage power station history database, the prevention and control parameters of the training samples requiring fire prevention and control, the time corresponding to the training sample set, and the preset energy storage power station prevention and control resource database includes: Determine multiple training samples around the training sample requiring fire prevention and control according to the information of each training sample in the preset energy storage power station history database; Screening a plurality of training samples around the training sample requiring fire prevention and control to obtain a plurality of screened training samples around the training sample requiring fire prevention and control; According to the multiple selected training samples around the training samples requiring fire prevention and control, the time corresponding to the training sample set, and the preset energy storage power station prevention and control resource database, the historical real-time prevention and control resource data of each selected training sample around the training samples requiring fire prevention and control at the time; According to the historical real-time prevention and control resource data of each screened training sample around the training sample requiring fire prevention and control at the moment and the prevention and control parameters of the training sample requiring fire prevention and control, each screened training sample around the training sample requiring fire prevention and control is screened twice to obtain a plurality of re-screened training samples around the training sample requiring fire prevention and control; The multiple re-screened training samples around the training sample requiring fire prevention and control are used as the multiple training samples corresponding to the training sample requiring fire prevention and control.

8. The fire prevention and control intelligent management method of an energy storage power station according to claim 7, characterized in that: The selectively outputting a set of estimated prevention and control management solutions according to the estimated data set corresponding to the training sample set, or outputting that each training sample corresponding to the training sample set does not require fire prevention and control includes: According to the estimated data set corresponding to the training sample set, it is determined whether each training sample in the estimated data set needs estimated prevention and control: If the judgment is yes, then generating multiple groups of estimated prevention and control management plans according to the training samples requiring fire prevention and control in the estimated data set, and selecting and outputting one group of estimated prevention and control management plans; If the judgment is no, then each training sample corresponding to the training sample set is outputted as not requiring fire prevention and control.

9. The fire prevention and control intelligent management method of an energy storage power station according to claim 8, characterized in that: The method generates multiple groups of estimated prevention and control management plans based on the training samples that need fire prevention and control in the estimated data set, and selects and outputs a group of estimated prevention and control management plans, including: Generate multiple groups of estimated prevention and control management plans based on the training samples requiring fire prevention and control in the estimated data set; Simulate multiple groups of estimated prevention and control management plans to obtain simulation data for each group of estimated prevention and control management plans; Generate the control efficiency and control cost value corresponding to each group of estimated prevention and control management plans based on the simulation data of each group of estimated prevention and control management plans; According to the preset prevention and control category priorities corresponding to the training samples, a group of estimated prevention and control management schemes with the highest prevention and control efficiency or the lowest prevention and control cost value are selectively selected as a group of estimated prevention and control management schemes selected and output.

10. A fire prevention and control intelligent management system, characterized in that: The system includes a communication device, a fire prevention and control model, and a control device. The communication device is communicatively connected to each area in a plurality of energy storage power stations to realize information interaction between the system and each area in the plurality of energy storage power stations. The fire prevention and control model stores a preset energy storage power station history database, a preset prevention and control parameter comparison library, and a preset energy storage power station prevention and control resource database. The control device includes a processor and a memory. The memory is suitable for storing a plurality of program codes. The program codes are suitable for being loaded and run by the processor to execute the fire prevention and control intelligent management method for the energy storage power station described in any one of claims 1 to 9.

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