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

Abstract

The invention provides a method and device for layout optimization of a fire extinguishing detection device in a battery, and relates to the technical field of battery layout optimization, and the method comprises the steps: obtaining the type of a sensor related to fire extinguishing detection and the corresponding installation cost, and determining the position where the sensor can be installed; acquiring the installation cost when the sensor type is installed at each position, and determining the total cost according to the installation cost and the battery pack configuration cost; optimizing the types of sensors installed at each position and the configuration of the battery pack by taking the minimization of the total cost as a target under the constraint of the constraint condition, and generating a recommendation scheme; sensors are installed at all positions according to the recommended scheme, and battery pack configuration is set. By acquiring the sensor type, the installation cost and the battery pack configuration cost and determining the total cost minimization target under the constraint condition, the optimization of arranging the fire extinguishing detection device in the battery is realized, the cost waste caused by blind installation is avoided, and the arrangement efficiency and the economic benefit of the fire extinguishing detection device are improved.

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 critical 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 affects the operating efficiency and safety of the system. In recent years, the development of battery technology has led to a variety of electrochemical cell types and applications, each of which poses unique challenges regarding energy density, charging rate and thermal management. These challenges require innovative sensor placement and battery configuration methods 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 accommodate different 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 some 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 the present application is to provide a device.

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

[0008] A fourth objective of the present application is to provide a computer-readable storage medium.

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

[0010] To achieve the above-mentioned purpose, 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 types of sensors related to fire detection and the corresponding installation costs, and determine where the sensors can be installed;

[0012] Obtaining the installation cost of the sensor type when installed at each position, and determining the total cost according to the installation cost and the cost of the battery pack configuration;

[0013] Determine constraints according to the operating conditions of the sensor in the battery, and optimize the sensor type and battery pack configuration installed at each location with the goal of minimizing the total cost under the constraints of 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 position i. If not installed, it is 0, and if installed, it is 1; k is the second decision variable, indicating the selected battery pack configuration k, which is 1 if configuration k is selected, otherwise 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; K is the total number of battery pack configurations.

[0018] The constraints include:

[0019] Override constraints:

[0020]

[0021] 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;

[0022] Battery pack configuration selection constraints:

[0023]

[0024] Among them, the constraint condition 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 needs to be installed is used to ensure that in a specific area P3D of three-dimensional space, each sensor type t must be installed with at least m 3D,t sensors for full 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 configuration:

[0035]

[0036] Among them, K i,t is a battery pack configuration set, which 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 the cost-effectiveness is maximized;

[0043] Safety constraints for 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 battery pack configuration k is selected, then 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 ;

[0046] Environmental adaptability constraints:

[0047]

[0048] Among them, P env represents the set of locations related to environmental adaptability, m env,t represents the minimum requirement for environmental adaptability of sensors of type t. This constraint 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 position 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 of the constraints, and generating a recommended solution, includes:

[0057] A mixed integer nonlinear programming model for optimizing sensor placement and battery pack configuration is solved using an optimization solver to generate 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 into the optimization solver is validated and formatted correctly.

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

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

[0062] To achieve the above-mentioned purpose, the 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, used to acquire the sensor types and corresponding installation costs related to fire extinguishing detection, and determine the locations where the sensors can be installed;

[0064] A cost calculation module, used to obtain the installation cost of the sensor type when it is installed at various positions, and determine the total cost according to the installation cost and the cost of the battery pack configuration;

[0065] An optimization module, used for determining constraint conditions according to the operating conditions of the sensor 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 constraint of the constraint conditions, and 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, the third aspect of the present application provides an electronic device, including: 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 aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution 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 purpose, the fifth aspect of the present application proposes a computer program product, which implements any method in the first aspect when executed by a processor.

[0072] The method, device, electronic device and storage medium for optimizing the layout of the fire extinguishing detection device in the battery provided in the present application achieve the minimization of the overall system cost, including the cost of the sensor and battery configuration, by strategically optimizing the sensor layout and battery pack configuration; by considering the three-dimensional space situation 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, avoiding the problems caused by static configuration, and improving 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 the battery management system 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 the 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 schematic 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 diagram of the structure of a fire extinguishing detection device in a battery with optimized layout provided in an embodiment of the present application. DETAILED DESCRIPTION

[0077] Embodiments of the present application are described in detail below, and examples of the embodiments 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 accommodate different battery geometries, insufficient sensor coverage, and inefficient response to real-time changes in battery status.

[0079] To address this problem, the present application provides a method for optimizing the layout of a fire extinguishing detection device in a battery. 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. Figure 1 As shown, the method comprises the following steps:

[0080] Step 101 : obtaining the sensor types and corresponding installation costs related to fire extinguishing detection, and determining 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 parts.

[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 , obtaining the installation cost of the sensor type when installed at each position, and determining the total cost according to the installation cost and the cost of the battery pack configuration.

[0085] In the embodiment of the present application, according to the data obtained in step 101, the installation cost of the sensor type when installed at each position can be further obtained, and the total cost can be determined by combining it with the cost of the battery pack configuration, and the expression is:

[0086]

[0087] Among them, Z is the total cost, x i,t is the first decision variable, indicating that sensor type t is installed at position i. If not installed, it is 0, and if installed, it is 1; k is the second decision variable, indicating the selected battery pack configuration k, which is 1 if configuration k is selected, otherwise 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; 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 an expert system to ensure that the weight assignments accurately reflect the relative importance of different configurations or sensor arrangements.

[0089] Step 103 , determining constraints according to 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 the 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 that optimizes 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, and its expression is:

[0092]

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

[0094] Override 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 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 The minimum number of sensors that need to be installed for each sensor type is t.

[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 at least m sensors of each type t are installed in a specific area P3D of the three-dimensional space. 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 sum of the energy storage capacities of the sensors at the relevant locations in the system must be greater than or equal to the specific energy storage capacity function f(V k ).

[0111] The number of sensors to be arranged 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 configuration:

[0113]

[0114] Among them, K i,tis a battery configuration set, which contains all battery 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 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 for 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,trepresents the minimum requirement for environmental adaptability of sensors of type t. This constraint 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, in order to capture the nonlinear characteristics in the model, especially the dependency between the sensor configuration and the battery pack arrangement, the embodiment of the present application also 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, in order to improve the operational efficiency and security of the system, this application also introduces a constraint to reduce the false alarm 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 position 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 to obtain a recommended solution.

[0138] During the solution process, the solution is gradually improved through multiple iterations. Each iteration is adjusted and optimized based on the previous results. The goal is to explore various scenarios and configurations, and finally 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 the sensor arrangement 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 arrangement 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 validated and formatted correctly.

[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 check, rationality check, model verification, 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; the model verification 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 , installing sensors at various locations and setting battery pack configurations according to the recommended solution.

[0145] Finally, according to the recommended solution obtained in step 103, a sensor of a corresponding type is installed at a position 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 the present application, experiments used data sets obtained from real-world scenarios involving different types of electrochemical cells and sensor technologies. The results demonstrated the effectiveness of the model proposed in the present application in optimizing battery packs and sensor arrangements.

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

[0148] Figure 2 A schematic diagram of a fire extinguishing detection device in a battery with optimized layout is provided in an embodiment of the present application. Figure 2 As shown, the device comprises:

[0149] An acquisition module 100 is used to acquire 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, for obtaining the installation cost of the sensor type when installed at various positions, and determining the total cost according to the installation cost and the cost of the battery pack configuration;

[0151] an optimization module 300 for determining constraints according to the operating conditions of the sensors in the battery, and optimizing the types of sensors installed at various locations and the configuration of the battery pack with the goal of minimizing the 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 the battery pack configuration 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 should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0158] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0159] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means 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 representations 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, without contradiction.

[0160] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0161] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment 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 not be performed in 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 technicians in the technical field to which the embodiments of the present application belong.

[0162] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, 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 in other suitable ways if necessary, and then stored in a computer memory.

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

[0164] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0165] In addition, each functional unit in each embodiment 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 one 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 can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify 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 types of sensors related to fire detection and the corresponding installation costs, and determine where the sensors can be installed; Obtaining the installation cost of the sensor type when installed at each position, and determining the total cost according to the installation cost and the cost of the battery pack configuration; Determine constraints according to the operating conditions of the sensor in the battery, and optimize the sensor type and battery pack configuration installed at each location with the goal of minimizing the total cost under the constraints of 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 position i. If not installed, it is 0, and if installed, it is 1; k is the second decision variable, indicating the selected battery pack configuration k, which is 1 if configuration k is selected, otherwise 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; K is the total number of battery pack configurations.

3. The method according to claim 2, characterized in that The constraints include: Override constraints: 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; Battery pack configuration selection constraints: Among them, the constraint condition 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 is the minimum number of sensors of each type t that need to be installed. This constraint is used to ensure that in a specific area P3D of three-dimensional space, each sensor type t must be installed with at least m 3D，t sensors for full 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 configuration: Among them, K i，t is a battery pack configuration set, which 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 the 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 battery pack configuration k is selected, then 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 ; Environmental adaptability constraints: Among them, P env represents the set of locations related to environmental adaptability, m env，t represents the minimum requirement for environmental adaptability of sensors of type t. This constraint 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 position 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 of the constraints, and generating a recommended solution, includes: A mixed integer nonlinear programming model for optimizing sensor placement and battery pack configuration is solved using an optimization solver to generate recommendations.

6. The method according to claim 5, characterized in that Before using the optimization solver to solve the 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 into the optimization solver is validated and formatted correctly.

7. The method according to claim 6, characterized in that The step of installing sensors at various locations and setting battery pack configurations according to the recommended solution includes: A sensor of a corresponding type is installed at a position where the first decision variable in the recommendation scheme is 1, and the 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, used to acquire the sensor types and corresponding installation costs related to fire extinguishing detection, and determine the locations where the sensors can be installed; A cost calculation module, used to obtain the installation cost of the sensor type when it is installed at various positions, and determine the total cost according to the installation cost and the cost of the battery pack configuration; An optimization module, configured to determine constraint conditions according to operating conditions of the sensor 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 constraint conditions, and generate 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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