Method and device for optimizing the layout of fire extinguishing detection device in battery

Abstract

This application proposes a method and device for optimizing the layout of fire detection devices in a battery, which relates to the technical field of battery layout optimization. The method includes: obtaining the sensor types and corresponding installation costs related to fire detection, and determining the locations where the sensors can be installed; obtaining the installation costs of the sensor types when installed at various locations, and determining the total cost based on the installation costs and the costs of the battery pack configuration; optimizing the sensor types and battery pack configurations installed at various locations with the goal of minimizing the total cost under the constraints of constraints, and generating a recommended solution; installing sensors at various locations and setting the battery pack configuration according to the recommended solution. By obtaining the sensor types, installation costs, and costs of the battery pack configuration, and determining the goal of minimizing the total cost under the constraints, the layout of the fire detection devices in the battery is optimized, avoiding the cost waste caused by blind installation, and improving the layout efficiency and economic benefits of the fire detection devices.

Description

Technical Field

[0001] The present application relates to the technical field of battery layout optimization, and in particular to a method and device for optimizing the layout of a fire extinguishing detection device in a battery. Background Art

[0002] The integration of electrochemical energy storage (EES) systems in a variety of applications, from portable electronics to electric vehicles and large-scale grid energy storage, is crucial in the current energy transition towards more sustainable energy sources. These systems rely heavily on efficient battery management to optimize performance, extend life and ensure safety. A key aspect of battery management is the strategic placement of sensors and configuration of the battery pack, which directly impacts the operating efficiency and safety of the system. Recent advances in battery technology have led to a wide variety of electrochemical cell types and applications, each of which presents unique challenges regarding energy density, charging rate and thermal management. These challenges require innovative approaches to sensor placement and battery configuration to maximize system performance while minimizing risk and cost.

[0003] Conventional approaches to sensor placement and battery configuration often fail to address the dynamic and complex nature of modern EES systems. These limitations include inability to adapt to varying battery geometries, insufficient sensor coverage, and inefficient response to real-time changes in battery status. Summary of the Invention

[0004] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the first objective of the present application is to propose a method for optimizing the layout of a fire extinguishing detection device in a battery.

[0006] The second object of this application is to provide a device.

[0007] The third objective of this application is to provide an electronic device.

[0008] The fourth object of this application is to provide a computer-readable storage medium.

[0009] A fifth object of this application is to provide a computer program product.

[0010] To achieve the above objectives, the first embodiment of the present application proposes a method for optimizing the layout of a fire extinguishing detection device in a battery, comprising:

[0011] Obtain the sensor types and corresponding installation costs related to fire detection, and determine where the sensors can be installed;

[0012] Obtaining the installation cost of the sensor type when installed at various locations, and determining a total cost based on the installation cost and the cost of battery pack configuration;

[0013] Determining constraints based on operating conditions of the sensors in the battery, and optimizing the sensor types and battery pack configurations installed at various locations with the goal of minimizing the total cost under the constraints, to generate a recommended solution;

[0014] Install sensors at various locations and set up battery pack configurations according to the recommendations.

[0015] The total cost is formulated as:

[0016]

[0017] Where Z is the total cost, x i,t is the first decision variable, indicating that sensor type t is installed at location i. If not installed, it is 0, and if installed, it is 1; k is the second decision variable, which represents the selected battery pack configuration k. If configuration k is selected, it is 1, otherwise it is 0; c i,t is the sensor cost of installing sensor type t at location i; b k is the cost of selecting battery configuration k; w i,t represents the weight of the sensor of sensor type t installed at position i; represents the weight for selecting battery configuration k; N is the total number of possible sensor installation locations; T is the total number of sensor types; and K is the total number of battery pack configurations.

[0018] The constraints include:

[0019] Coverage constraints:

[0020]

[0021] Among them, S j,t is the set of locations of sensor type t associated with cell j, where J represents the set of cells. This constraint ensures that each cell is covered by at least one sensor.

[0022] Battery pack configuration selection constraints:

[0023]

[0024] Among them, this constraint is used to ensure that a battery pack configuration is selected;

[0025] Multi-type sensor coverage constraints:

[0026]

[0027] Where M represents the maximum number of sensors of each type installed on each battery unit. This constraint is used to limit the coverage requirements of multiple types of sensors.

[0028] Three-dimensional space coverage constraints:

[0029]

[0030] Among them, m 3D,t The minimum number of sensors of each type t that need to be installed is used to ensure that in a specific area P3D of three-dimensional space, each sensor type t must be installed at least m 3D,t sensors to achieve comprehensive coverage;

[0031] Battery capacity constraints:

[0032]

[0033] Among them, V max is the maximum battery storage capacity that the system can accommodate, V k is the battery energy storage capacity of the selected battery configuration k. This constraint is used to ensure that if battery configuration k is selected, the sum of the energy storage capacities of the sensor layouts at the relevant positions in the system must be greater than or equal to the specific energy storage capacity function f(V k );

[0034] Dependency constraints for sensor and battery pack configuration:

[0035]

[0036] Among them, K i,t is a battery pack configuration set that contains all battery pack configurations that are compatible with position i and sensor type t. This constraint is used to ensure that the sensor configuration must depend on the selected battery pack configuration;

[0037] Battery pack configuration performance constraints:

[0038] V k ≥V min ,if y k =1

[0039] Among them, V min is the minimum battery energy storage capacity that the system can accept. This constraint is used to ensure that the battery performance of battery pack configuration k meets the minimum requirements;

[0040] Cost-effectiveness constraints for battery pack configuration:

[0041] b k / V k ≤C max ,if yk =1

[0042] Among them, b k is the cost of selecting battery configuration k, C max is the maximum cost-effectiveness ratio, and this constraint is used to ensure that cost-effectiveness is maximized;

[0043] Safety constraints of battery pack configuration mode:

[0044]

[0045] Among them, m safety,t is the safety requirement value of sensor type t. This constraint is used to ensure that if the battery pack configuration k is selected, then in the region P safety The number of safety layouts of sensors in the system must be greater than or equal to the specific safety requirement value m. safety,t ;

[0046] Environmental adaptability constraints:

[0047]

[0048] Among them, P env Represents the set of locations related to environmental adaptability, m env,t The constraint condition is used to ensure that for each sensor type t, at least m sensors need to be installed at locations related to environmental adaptability. env,t sensors.

[0049] The constraints also include:

[0050] Nonlinear constraints:

[0051]

[0052] Among them, g i,t,k is a nonlinear function that describes the adaptability or efficiency of sensor type t at position i when battery pack configuration k is selected. This constraint is used to ensure that the model accurately captures the complex interaction between sensor placement and battery configuration;

[0053] False alarm rate constraints:

[0054]

[0055] Among them, p i,t is the false alarm rate of sensor type t at location i, τ is the minimum acceptable false alarm rate level of the system, and this constraint is used to ensure that the overall false alarm rate of the system does not exceed the minimum acceptable false alarm rate level τ of the system.

[0056] The optimizing the sensor types and battery pack configurations installed at various locations with the goal of minimizing the total cost under the constraints, and generating a recommended solution, includes:

[0057] An optimization solver is used to solve a mixed integer nonlinear programming model for optimizing sensor placement and battery pack configuration, generating recommendations.

[0058] Optionally, before using an optimization solver to solve the mixed integer nonlinear programming model for sensor placement and battery pack configuration, also include:

[0059] Perform input data preprocessing and data validation to ensure that the data input to the optimization solver is validated and correctly formatted.

[0060] The steps of installing sensors at various locations and setting battery pack configurations according to the recommended solution include:

[0061] A sensor of a corresponding type is installed at a position where the first decision variable in the recommendation scheme is 1, and a battery pack configuration is determined according to the value of the second decision variable in the recommendation scheme.

[0062] To achieve the above-mentioned objectives, a second embodiment of the present application proposes a device for optimizing the layout of a fire extinguishing detection device in a battery, comprising:

[0063] An acquisition module is used to obtain the sensor types and corresponding installation costs related to fire detection and determine the locations where the sensors can be installed;

[0064] a cost calculation module, configured to obtain the installation cost of the sensor type when installed at various locations, and determine the total cost based on the installation cost and the cost of the battery pack configuration;

[0065] an optimization module, configured to determine constraints based on operating conditions of the sensors in the battery, and optimize the sensor types and battery pack configurations installed at various locations with the goal of minimizing the total cost under the constraints, thereby generating a recommended solution;

[0066] A configuration module is used to install sensors at various locations and set battery pack configurations according to the recommended solution.

[0067] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0068] The memory stores computer-executable instructions;

[0069] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0070] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0071] To achieve the above-mentioned objectives, the fifth embodiment of the present application proposes a computer program product, which implements any one of the methods in the first aspect when executed by a processor.

[0072] The method, device, electronic device and storage medium for optimizing the layout of fire detection devices in batteries provided in the present application achieve the minimization of the overall system cost, including the cost of sensor and battery configuration, by strategically optimizing the sensor layout and battery pack configuration; by considering the three-dimensional space conditions and different energy storage capacities, multi-dimensional space analysis is achieved, avoiding the shortcomings of single-dimensional optimization, and improving the applicability and flexibility of the battery management system in different technologies and operating environments; by integrating adaptive configuration strategies, it is possible to respond to the specific operating requirements of different battery types, avoid the problems caused by static configuration, and improve the system's predictive ability and operating efficiency; and the present application has significant improvements in cost efficiency and system reliability, providing a solid framework for enhancing battery management systems in different technologies and operating environments.

[0073] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0075] Figure 1 A flow chart of a method for optimizing the layout of a fire extinguishing detection device in a battery provided in an embodiment of the present application;

[0076] Figure 2 A schematic structural diagram of a device for optimizing the layout of a fire detection device in a battery provided in an embodiment of the present application. DETAILED DESCRIPTION

[0077] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0078] Conventional approaches to sensor placement and battery configuration often fail to address the dynamic and complex nature of modern EES systems. These limitations include inability to adapt to varying battery geometries, insufficient sensor coverage, and inefficient response to real-time changes in battery status.

[0079] To address this issue, the present invention provides a method for optimizing the layout of a fire detection device in a battery. Figure 1 This is a flow chart of a method for optimizing the layout of a fire detection device in a battery provided by an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0080] Step 101: Obtain the sensor types and corresponding installation costs related to fire extinguishing detection, and determine the locations where the sensors can be installed.

[0081] There are many types of sensors related to fire extinguishing detection, such as smoke sensors, gas sensors, temperature sensors, flame sensors, etc., which are not limited in the embodiments of the present application.

[0082] Installation costs can be divided into direct costs, indirect costs, and maintenance costs. Direct costs include the purchase price of the sensor itself and installation materials (such as cables, brackets, etc.). Indirect costs may include labor costs during the installation process, system debugging costs, and possible auxiliary equipment (such as controllers, alarms, etc.). Maintenance costs refer to the costs of long-term maintenance, regular inspections, and replacement of components.

[0083] It is understandable that the location where the sensor can be installed needs to be set according to the actual scenario, and this application does not provide specific instructions for this.

[0084] Step 102 : Obtain the installation cost of the sensor type when installed at various locations, and determine the total cost based on the installation cost and the cost of the battery pack configuration.

[0085] In the embodiment of the present application, based on the data obtained in step 101, the installation cost of the sensor type when installed at each location can be further obtained. By combining this with the cost of the battery pack configuration, the total cost can be determined, which is expressed as:

[0086]

[0087] Where Z is the total cost, x i,t is the first decision variable, indicating that sensor type t is installed at location i. If not installed, it is 0, and if installed, it is 1; k is the second decision variable, which represents the selected battery pack configuration k. If configuration k is selected, it is 1, otherwise it is 0; c i,t is the sensor cost of installing sensor type t at location i; b kis the cost of selecting battery configuration k; w i,t represents the weight of the sensor of sensor type t installed at position i; represents the weight for selecting battery configuration k; N is the total number of possible sensor installation locations; T is the total number of sensor types; and K is the total number of battery pack configurations.

[0088] As a possible implementation, the weight w i,t and This can be determined through data analysis or expert systems to ensure that the weight assignments accurately reflect the relative importance of different configurations or sensor arrangements.

[0089] Step 103 , determining constraints based on the operating conditions of the sensors in the battery, and optimizing the sensor types and battery pack configurations installed at various locations with the goal of minimizing total cost under the constraints, and generating a recommended solution.

[0090] It can be understood that optimizing the sensor types and battery pack configurations installed at various locations with the goal of minimizing the total cost under the constraints is essentially building a mixed integer nonlinear programming model for optimizing the sensor placement and battery pack configuration.

[0091] The objective function of the model is to minimize the total cost of sensor installation and the cost of selecting battery configuration, that is, to minimize the total cost, which is expressed as:

[0092]

[0093] The model includes a comprehensive set of constraints to ensure the functionality, safety, and efficiency of the battery management system, including:

[0094] Coverage constraints:

[0095]

[0096] Among them, S j,t is the set of locations of sensor type t associated with battery cell j, J represents the set of battery cells, and this constraint is used to ensure that each battery cell is covered by at least one sensor.

[0097] Battery pack configuration selection constraints:

[0098]

[0099] The constraint condition is used to ensure that a battery pack configuration is selected.

[0100] Multi-type sensor coverage constraints:

[0101]

[0102] Where M represents the maximum number of sensors of each type installed on each battery cell. This constraint is used to limit the coverage requirements of multiple types of sensors, that is, to limit the maximum number of sensors of each type t installed on each battery cell j.

[0103] Three-dimensional space coverage constraints:

[0104]

[0105] Among them, m 3D,t For each sensor type t the minimum number of sensors that need to be installed.

[0106] When considering the three-dimensional space coverage problem, it is necessary to arrange sensors reasonably to ensure that the key areas or target areas in the entire three-dimensional space are covered. This constraint can be used to ensure that in a specific area P3D of the three-dimensional space, each sensor type t must be installed at least m 3D,t sensors to ensure full coverage of the corresponding area.

[0107] Battery capacity constraints:

[0108]

[0109] Among them, V max is the maximum battery storage capacity that the system can accommodate, V k is the battery energy storage capacity of the selected battery pack configuration k.

[0110] In this constraint, the layout of the sensors needs to be adjusted to suit the energy storage capacity of the battery according to the selected battery pack configuration k. Specifically, for the selected battery pack configuration k, this constraint is used to ensure that: if the battery pack configuration k (i.e., y k =1), the total energy storage capacity of the sensor layout at the relevant position in the system must be greater than or equal to the specific energy storage capacity function f(V k ).

[0111] The number of sensor layouts increases as the energy storage capacity of the selected battery pack configuration decreases. That is, when the selected battery pack configuration has a lower energy storage capacity, the number of sensors needs to be increased to ensure the coverage and monitoring capabilities of the system.

[0112] Dependency constraints for sensor and battery pack configuration:

[0113]

[0114] Among them, K i,tis a battery pack configuration set that includes all battery pack configurations that are compatible with position i and sensor type t. This constraint is used to ensure that the sensor configuration depends on the selected battery pack configuration.

[0115] Battery pack configuration performance constraints:

[0116] V k ≥V min ,if y k =1

[0117] Among them, V min is the minimum battery energy storage capacity that the system can accept. This constraint is used to ensure that the battery performance of battery pack configuration k meets the minimum requirements.

[0118] Cost-effectiveness constraints for battery pack configuration:

[0119] b k / V k ≤C max ,if y k =1

[0120] Among them, b k is the cost of selecting battery configuration k, C max is the maximum cost-effectiveness ratio, and this constraint is used to ensure that the cost-effectiveness is maximized.

[0121] Safety constraints of battery pack configuration mode:

[0122]

[0123] Among them, m safety,t is the safety requirement value of sensor type t.

[0124] In this constraint, it is necessary to ensure that the selected combination is optimal in terms of safety. Specifically, for the selected battery pack configuration k, this constraint is used to ensure that: if the battery pack configuration k (i.e. y k =1), then the sensor layout at the safety-related positions in the system must meet certain safety requirements, that is, in area P safety The number of safety layouts of sensors in the system must be greater than or equal to the specific safety requirement value m. safety,t .

[0125] Environmental adaptability constraints:

[0126]

[0127] Among them, P env Represents the set of locations related to environmental adaptability, m env,tThe constraint condition is used to ensure that for each sensor type t, at least m sensors need to be installed at locations related to environmental adaptability. env,t sensors.

[0128] In addition, to capture the nonlinear characteristics within the model, especially the dependency between the sensor configuration and the battery pack layout, the embodiment of the present application further proposes the following nonlinear constraints:

[0129]

[0130] Among them, g i,t,k is a nonlinear function that describes the adaptability or efficiency of sensor type t at location i when battery pack configuration k is selected. This constraint is used to ensure that the model accurately captures the complex interaction between sensor placement and battery configuration.

[0131] This nonlinear relationship can be determined through experimental data or simulation analysis, ensuring that the model accurately captures the complex interaction between sensor placement and battery configuration.

[0132] In addition, to improve the operational efficiency and security of the system, this application also introduces a constraint to reduce the false positive rate within the model, as follows:

[0133] False alarm rate constraints:

[0134]

[0135] Among them, p i,t is the false alarm rate of sensor type t at location i, τ is the minimum acceptable false alarm rate level of the system, and this constraint is used to ensure that the overall false alarm rate of the system does not exceed the minimum acceptable false alarm rate level τ of the system.

[0136] It can be understood that by implementing the false alarm rate constraint condition, the overall false alarm rate of the system is kept at a low level, thereby enhancing the reliability and security of the system.

[0137] In the embodiment of the present application, further, based on the objective function and constraints of the mixed integer nonlinear programming model shown above, an optimization solver can be used to solve it and obtain a recommended solution.

[0138] During the solution process, the solution is gradually improved through multiple iterations. Each iteration adjusts and optimizes based on the previous results. The goal is to explore various scenarios and configurations, and ultimately adjust parameters and constraints as needed to find the optimal solution as the recommended solution.

[0139] In one possible embodiment, the optimization solver CPLEX is used to solve a mixed integer nonlinear programming (MINLP) model for optimizing sensor placement and battery pack configuration.

[0140] It should be noted that before using the optimization solver to solve the mixed integer nonlinear programming model of sensor layout and battery pack configuration, the input data needs to be preprocessed and validated to ensure that all data input into the optimization solver, including cost, capacity, and space configuration, are verified and correctly formatted.

[0141] Preprocessing and data validation are key steps in the optimization process. They can detect and correct data problems that may make the model infeasible in advance, reduce the time the solver spends processing invalid or redundant data, thereby speeding up the solution, and ensure the consistency and rationality of the input data, thereby improving the credibility of the final results.

[0142] As a possible implementation method, the preprocessing stage includes data cleaning, data conversion, etc. Data cleaning is used to remove or correct obviously erroneous data, such as outliers or missing values, and ensure that all data fields conform to the expected format; data conversion is used to ensure the consistency of all data units and avoid problems caused by inconsistent units, thereby converting the data into a form suitable for solver processing, such as normalized or standardized values.

[0143] As a possible implementation method, the data verification stage includes consistency checks, rationality checks, model validation, etc. The consistency check is used to check whether there is missing data, ensure that all necessary fields have values, and verify whether there are logical contradictions between the data, for example, the cost should not be negative; the rationality check is used to check whether the data is within a reasonable range; model validation is used to ensure that all constraints are reasonable and feasible, and to confirm that the definition of the objective function is correct and matches the actual problem.

[0144] Step 104 : Install sensors at various locations and set battery pack configurations according to the recommended solution.

[0145] Finally, according to the recommended solution obtained in step 103 , sensors of corresponding types are installed at positions where the first decision variable in the recommended solution is 1, and the battery pack configuration is determined according to the value of the second decision variable in the recommended solution.

[0146] Furthermore, in one embodiment of this application, experiments used datasets obtained from real-world scenarios involving different types of electrochemical cells and sensor technologies. The results demonstrated the effectiveness of the proposed model in optimizing battery packs and sensor placement.

[0147] In order to implement the above embodiment, the present application also proposes a device for optimizing the layout of a fire extinguishing detection device in a battery.

[0148] Figure 2 10 is a structural diagram of a device for optimizing the layout of a fire extinguishing detection device in a battery provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0149] An acquisition module 100 is used to obtain the sensor types and corresponding installation costs related to fire extinguishing detection, and determine the locations where the sensors can be installed;

[0150] A cost calculation module 200 is used to obtain the installation cost of the sensor type when installed at various locations, and determine the total cost based on the installation cost and the cost of the battery pack configuration;

[0151] an optimization module 300 for determining constraints based on the operating conditions of the sensors in the battery, and optimizing the sensor types and battery pack configurations installed at various locations with the goal of minimizing total cost under the constraints, and generating a recommended solution;

[0152] The configuration module 400 is used to install sensors at various locations and set battery pack configurations according to the recommended solution.

[0153] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0154] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0155] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0156] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0157] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0158] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0159] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0160] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0161] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0162] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0163] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0164] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0165] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0166] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for optimizing the layout of a fire extinguishing detection device in a battery, characterized in that: The following steps are involved: Obtain the sensor types and corresponding installation costs related to fire detection, and determine where the sensors can be installed; Obtaining the installation cost of the sensor type when installed at various locations, and determining a total cost based on the installation cost and the cost of battery pack configuration; Determining constraints based on operating conditions of the sensors in the battery, and optimizing the sensor types and battery pack configurations installed at various locations with the goal of minimizing the total cost under the constraints, to generate a recommended solution; Install sensors at various locations and set up battery pack configurations according to the recommendations.

2. The method according to claim 1, characterized in that The total cost is formulated as: Where Z is the total cost, x i，t is the first decision variable, indicating that sensor type t is installed at location i. If not installed, it is 0, and if installed, it is 1; k is the second decision variable, which represents the selected battery pack configuration k. If configuration k is selected, it is 1, otherwise it is 0; c i，t is the sensor cost of installing sensor type t at location i; b k is the cost of selecting battery configuration k; w i，t represents the weight of the sensor of sensor type t installed at position i; represents the weight for selecting battery configuration k; N is the total number of possible sensor installation locations; T is the total number of sensor types; and K is the total number of battery pack configurations.

3. The method according to claim 2, characterized in that The constraints include: Coverage constraints: Among them, s j，t is the set of locations of sensor type t associated with cell j, where J represents the set of cells. This constraint ensures that each cell is covered by at least one sensor. Battery pack configuration selection constraints: Among them, this constraint is used to ensure that a battery pack configuration is selected; Multi-type sensor coverage constraints: Where M represents the maximum number of sensors of each type installed on each battery unit. This constraint is used to limit the coverage requirements of multiple types of sensors. Three-dimensional space coverage constraints: Among them, m 3D，t The constraint is used to ensure that in a specific area P3D of three-dimensional space, each sensor type t must be installed at least m 3D，t sensors to achieve comprehensive coverage; Battery capacity constraints: Among them, V max is the maximum battery storage capacity that the system can accommodate, V k is the battery energy storage capacity of the selected battery configuration k. This constraint is used to ensure that if battery configuration k is selected, the sum of the energy storage capacities of the sensor layouts at the relevant positions in the system must be greater than or equal to the specific energy storage capacity function f(V k ); Dependency constraints for sensor and battery pack configuration: Among them, K i，t is a battery pack configuration set that contains all battery pack configurations that are compatible with position i and sensor type t. This constraint is used to ensure that the sensor configuration must depend on the selected battery pack configuration; Battery pack configuration performance constraints: V k ≥V min ,if y k =1 Among them, V min is the minimum battery energy storage capacity that the system can accept. This constraint is used to ensure that the battery performance of battery pack configuration k meets the minimum requirements; Cost-effectiveness constraints for battery pack configuration: b k / V k ≤C max ,if y k =1 Among them, b k is the cost of selecting battery configuration k, C max is the maximum cost-effectiveness ratio, and this constraint is used to ensure that cost-effectiveness is maximized; Safety constraints for battery pack configuration mode: Among them, m safety，t is the safety requirement value of sensor type t. This constraint is used to ensure that if the battery pack configuration k is selected, then in the region P safety The number of safety layouts of sensors in the system must be greater than or equal to the specific safety requirement value m. safety，t ; Environmental adaptability constraints: Among them, P env Represents the set of locations related to environmental adaptability, m env，t The constraint condition is used to ensure that for each sensor type t, at least m sensors need to be installed at locations related to environmental adaptability. env,t sensors.

4. The method according to claim 3, characterized in that The constraints also include: Nonlinear constraints: Among them, g i,t,k is a nonlinear function that describes the adaptability or efficiency of sensor type t at position i when battery pack configuration k is selected. This constraint is used to ensure that the model accurately captures the complex interaction between sensor placement and battery configuration; False alarm rate constraints: Among them, p i,t is the false alarm rate of sensor type t at location i, τ is the minimum acceptable false alarm rate level of the system, and this constraint is used to ensure that the overall false alarm rate of the system does not exceed the minimum acceptable false alarm rate level τ of the system.

5. The method according to claim 4, characterized in that The optimizing the sensor types and battery pack configurations installed at various locations with the goal of minimizing the total cost under the constraints, and generating a recommended solution, includes: An optimization solver is used to solve a mixed integer nonlinear programming model for optimizing sensor placement and battery pack configuration, generating recommendations.

6. The method according to claim 5, characterized in that Before using an optimization solver to solve a mixed-integer nonlinear programming model for sensor placement and battery pack configuration, it also includes: Perform input data preprocessing and data validation to ensure that the data input to the optimization solver is validated and correctly formatted.

7. The method according to claim 6, characterized in that The steps of installing sensors at various locations and setting battery pack configurations according to the recommended solution include: A sensor of a corresponding type is installed at a position where the first decision variable in the recommendation scheme is 1, and a battery pack configuration is determined according to the value of the second decision variable in the recommendation scheme.

8. A device for optimizing the layout of a fire extinguishing detection device in a battery, characterized in that: include: An acquisition module is used to obtain the sensor types and corresponding installation costs related to fire detection and determine the locations where the sensors can be installed; a cost calculation module, configured to obtain the installation cost of the sensor type when installed at various locations, and determine the total cost based on the installation cost and the cost of the battery pack configuration; an optimization module, configured to determine constraints based on operating conditions of the sensors in the battery, and optimize the sensor types and battery pack configurations installed at various locations with the goal of minimizing the total cost under the constraints, thereby generating a recommended solution; A configuration module is used to install sensors at various locations and set battery pack configurations according to the recommended solution.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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