A simple model construction method for the integrated layout of the top-end equipment of a hydropower plant house

Abstract

The application provides a simple model construction method for the integrated layout of end equipment of a hydropower station plant house roof, collects lighting, sprinkling and smoke sensing parameters and geometric sizes of lamps, sprinklers and smoke sensors as constraints; quantifies the minimum safety distance of physical and functional interference among the three according to specifications; constructs a collaborative arrangement model in the two-dimensional projection space of the roof, takes the constraints and safety distances as inputs, and iteratively finds a Pareto optimal coordinate set through multi-objective optimization; and finally selects an optimal solution to generate a layout scheme containing coordinates and direction angles and visually outputs. The application can realize efficient and accurate layout of end equipment of a hydropower station plant house roof, improve lighting uniformity, sprinkling coverage effect and fire detection reliability, optimize the safety distance between equipment, and ensure the safety of plant operation and the convenience of maintenance.

Description

Technical Field

[0001] This invention relates to the fields of lighting engineering and fire protection equipment technology, and more specifically, to a simplified model construction method for the integrated layout of terminal equipment on the roof of a hydropower plant. Background Technology

[0002] In the construction and maintenance of hydropower plant buildings, the layout design of rooftop terminal equipment is a crucial step. This equipment includes lighting fixtures, sprinkler heads, and smoke detectors, which play a vital role in ensuring the normal operation of the plant, fire safety, and personnel safety. Traditional layout design methods rely primarily on manual experience, arranging equipment piecemeal in accordance with design specifications and product technical manuals. While this method can meet basic functional requirements to some extent, it has several limitations. First, manual layout struggles to accurately account for the complex interactions between equipment, such as physical and functional interference, easily leading to unreasonable spacing and affecting normal operation and maintenance. Second, traditional methods cannot effectively optimize the overall performance of the equipment layout, such as lighting uniformity, sprinkler coverage, and smoke detector detection efficiency, potentially resulting in resource waste or safety hazards. Furthermore, with the increasing scale of hydropower plant buildings and the growing variety of equipment, manual layout is inefficient and struggles to meet the demands of rapid construction and efficient operation and maintenance in modern hydropower plants.

[0003] Existing layout methods cannot accurately quantify the interference distance between devices, resulting in insufficient safety and reliability of device layout; they lack effective multi-objective optimization methods and cannot simultaneously take into account the optimal layout of multiple functions such as lighting, sprinkler and fire detection; moreover, traditional methods are difficult to adapt to complex factory environments and diverse equipment parameters, and cannot achieve efficient and accurate layout design. Summary of the Invention

[0004] This invention provides a simplified model construction method for the integrated layout of terminal equipment on the roof of a hydropower station powerhouse, comprising: Step S10: Obtain the lighting parameters and geometric dimensions of the lighting fixtures, the spraying parameters and geometric dimensions of the sprinkler heads, and the smoke detection parameters and geometric dimensions of the smoke detectors, and store the lighting parameters, spraying parameters, smoke detection parameters, and geometric dimensions as equipment constraints in the database. Step S20: Based on design specifications and product technical manuals, quantitatively extract the physical interference distance thresholds and functional interference distance thresholds between each pair of lighting fixtures, sprinkler heads, and smoke detectors, and collectively refer to the physical interference distance thresholds and functional interference distance thresholds as the minimum safe distance set; Step S30: Using the two-dimensional projection plane of the ceiling as the layout space, establish a collaborative layout model. The input of the collaborative layout model is the equipment constraints and the minimum safe distance set. The output of the collaborative layout model is the optimal coordinate set of lighting fixtures, sprinkler heads, and smoke detectors within the layout space. The construction and solution process of the collaborative layout model includes initializing the population, setting a multi-objective optimization function, and finding the optimal solution set that satisfies all constraints through iterative calculation. Step S40: Select an optimal solution from the set of optimal solutions, generate an integrated layout scheme that includes the device positioning coordinates and orientation angles, and output the integrated layout scheme in the form of a visualized two-dimensional or three-dimensional drawing.

[0005] Further, step S10 includes the following steps: Step S101: Collect basic equipment parameters, including the rated illuminance value, beam angle distribution and luminous flux output of lighting fixtures, the rated working pressure, flow characteristic curve and spray angle range of sprinkler heads, and the sensitivity level, detection radius and response time threshold of smoke detectors. Step S102: Measure the geometric features of the equipment, including the outline dimensions and mounting base interface dimensions of the lighting fixtures, the nozzle outer diameter and splash plate structural dimensions of the sprinkler heads, and the detector diameter and installation method dimensions of the smoke detectors. Step S103: Perform environmental adaptability correction. Based on the environmental characteristics of the hydropower plant, the basic parameters are corrected, including applying humidity influence coefficient and vibration attenuation coefficient to the lighting fixture parameters, water flow stability coefficient and pressure fluctuation compensation coefficient to the sprinkler head parameters, and electromagnetic compatibility coefficient to the smoke detector parameters. Step S104: Establish a parameter database, classify and store the equipment parameters after environmental adaptability correction according to equipment type, and establish a mapping relationship between equipment parameters and plant environmental conditions.

[0006] Further, step S20 includes the following steps: Step S201: Determine the physical interference distance threshold. This is done by analyzing the equipment's geometry to determine the foundation installation spacing, and then calculating the physical interference distance threshold based on the minimum operating space required for equipment installation and maintenance access requirements. The calculation formula is as follows:

[0007] in, Basic installation spacing, To allow for a safety margin in spacing, The installation difficulty level of the hydropower station, Spacing for construction error compensation; Step S202: Determine the functional interference distance threshold. By analyzing the functional characteristics of the equipment, determine the heat-affected zone distance, water mist-affected zone distance, and light interference distance, and calculate the functional interference distance threshold. The calculation formula is as follows:

[0008] in, For heat-affected distance, For light interference distance, α, β, and γ represent the distance of influence of water mist, and α, β, and γ are the functional interference weight coefficients under the specific environment of the hydropower station. Step S203: Establish a minimum safety distance set. By comparing the physical interference distance threshold and the functional interference distance threshold, the larger of the two values ​​is taken as the basic safety distance. Then, the safety redundancy requirements specific to the hydropower station powerhouse are added to form the final minimum safety distance set.

[0009] Further, in step S30, the initialization of the population includes: Within the layout space, based on the building structure column grid and equipment functional zoning of the hydropower station powerhouse, priority areas for the arrangement of lighting fixtures, mandatory areas for the arrangement of sprinkler heads, and areas for the full coverage of smoke detectors are pre-defined. Using the Latin hypercube sampling method, initial device location coordinates that satisfy the device constraints are generated in the priority arrangement area, the forced arrangement area, and the full coverage arrangement area, respectively, forming an initial population.

[0010] Furthermore, in step S30, the multi-objective optimization function consists of the following three sub-objective functions: The first sub-objective function F1 is used to minimize the sum of the deviations between the actual distances between all device pairs and the corresponding minimum safe distance set; The second sub-objective function F2 is used to maximize the illuminance uniformity of the lighting fixtures on a specified reference plane. The third sub-objective function F3 is used to minimize the detection blind zone area of ​​all smoke detectors in the layout space; The multi-objective optimization function is expressed as simultaneously optimizing the first sub-objective function F1, the second sub-objective function F2, and the third sub-objective function F3.

[0011] Furthermore, the formula for calculating the first sub-objective function F1 is as follows:

[0012] Where n is the total number of devices. This represents the set of minimum safe distances between device i and device j. This represents the actual Euclidean distance between device i and device j.

[0013] Furthermore, in the second sub-objective function F2, the formula for calculating the illuminance uniformity is:

[0014] Where U represents the uniformity of illuminance. To specify the minimum illuminance value for grid points on the reference plane, The average illuminance value for all grid points on a specified reference plane is calculated based on the inverse square law and the cosine law using the lighting parameters of the lighting fixture and its coordinate position.

[0015] Furthermore, in step S30, the step of finding the optimal solution set that satisfies all constraints through iterative calculation is performed using a non-dominated sorting genetic algorithm with an elite retention strategy. The process includes the following steps: Step S381: For each individual layout scheme in the current population, calculate its dominance relationship based on the multi-objective optimization function value of the hydropower plant equipment layout, and divide the entire population into multiple non-dominated levels. Step S382: Within the same non-dominated level, calculate the crowding degree of each individual for the three objective functions of lighting uniformity, equipment safety distance, and detection coverage; Step S383: Merge the parent and offspring populations, perform non-dominated sorting and crowding calculation, prioritize individuals at higher non-dominated levels, and for individuals at the same level, select individuals that are more evenly distributed across multiple optimization objectives of the hydropower plant equipment layout to form a new parent population. Step S384: Perform device type-based grouping selection, arithmetic crossover considering layout area constraints, and boundary mutation operations subject to minimum safe distance on the new parent population to generate a new offspring population; Step S385: In each iteration, based on the layout characteristics of the hydropower plant equipment, the adaptive penalty function method is used to handle violations of the minimum safety distance set, and the penalty function coefficients are dynamically adjusted with the number of iterations; Step S386: Repeat steps S381 to S385 until the preset maximum number of iterations is reached, or the optimal solution set of the population on the three optimization objectives of the hydropower plant equipment layout no longer shows significant improvement over multiple consecutive generations.

[0016] Furthermore, in step S40, selecting an optimal solution from the optimal solution set is performed using a multi-attribute decision-making method based on entropy weights, the process of which includes the following steps: Step S391: Take the candidate solutions in the optimal solution set as the evaluation object, and use the sub-objective function values ​​of the multi-objective optimization function of the hydropower plant equipment layout as the evaluation index to construct a decision matrix; Step S392: Perform vector normalization on the decision matrix to eliminate the differences in the dimensions and orders of magnitude of the three indicators: lighting uniformity, equipment safety distance, and detection coverage, and obtain a standardized decision matrix; Step S393: Based on the principle of information entropy, calculate the entropy value of each evaluation index based on the distribution characteristics of the three optimization target values ​​of the hydropower plant equipment layout, and then determine the objective weight of each evaluation index. Step S394: Multiply each column element in the standardized decision matrix by the objective weight of the corresponding evaluation index calculated in step S393 to form a weighted standardized matrix; Step S395: In the weighted normalization matrix, find the optimal values ​​for the three indicators of lighting uniformity, equipment safety distance and detection coverage to form a positive ideal solution, and find the worst values ​​for the three indicators to form a negative ideal solution. Step S396: Calculate the Euclidean distance between each alternative solution for the powerhouse equipment layout of a hydropower station and the positive ideal solution and the negative ideal solution respectively, and then calculate the relative proximity between each alternative solution and the positive ideal solution; Step S397: Compare the relative proximity of all alternative solutions for the layout of hydropower plant equipment, and select the alternative solution with the largest relative proximity value as the optimal solution.

[0017] Furthermore, following step S40, step S50 is also included: The integrated layout scheme generated in step S40 is imported into the pre-built hydropower plant building information model; Lighting simulation, sprinkler coverage simulation, and smoke detection simulation were performed in the building information model environment. By comprehensively analyzing the output results of the three performance simulations, it is determined whether the integrated layout scheme simultaneously meets the lighting requirements, fire extinguishing requirements, and fire early warning requirements. If the verification fails, the verification result is fed back to step S30 and used as a new constraint condition to solve the model again.

[0018] The embodiments of the present invention have at least the following beneficial effects: 1. By accurately acquiring the lighting, sprinkler, and smoke detection parameters and geometric dimensions of the equipment and storing them in the database as constraints, combined with the quantitatively extracted minimum safety distance set, the physical and functional interference between equipment can be fully considered, effectively avoiding mutual interference between equipment, ensuring the safety and reliability of the equipment in actual operation, and solving the safety hazards caused by unreasonable equipment layout in the existing technology.

[0019] 2. Based on multi-objective optimization functions and iterative calculations to find the optimal solution set, while simultaneously optimizing equipment spacing, lighting uniformity, and detection blind zone area, the optimal configuration of lighting fixtures, sprinkler heads, and smoke detectors in the layout space is achieved. This improves the overall performance and resource utilization efficiency of the equipment layout and solves the problems of resource waste and low layout efficiency caused by the inability of traditional methods to meet multiple functional requirements.

[0020] 3. An iterative calculation is performed using a non-dominated sorting genetic algorithm with an elite retention strategy, combined with an adaptive penalty function method to dynamically adjust the penalty function coefficients. This method can efficiently handle complex constraints and quickly converge to the optimal solution set that satisfies all constraints. At the same time, the optimal solution is selected from the optimal solution set through a multi-attribute decision-making method based on entropy weight, ensuring the scientificity and rationality of the layout scheme. This solves the problems of low layout design efficiency and difficulty in adapting to complex environments and diverse equipment parameters in existing technologies. Attached Figure Description

[0021] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a flowchart illustrating a simplified model construction method for the integrated layout of terminal equipment on the roof of a hydropower station, as provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0023] In the traditional layout of terminal equipment on the roof of existing hydropower plant buildings, existing technologies struggle to accurately quantify the physical and functional interference distance thresholds between lighting fixtures, sprinkler heads, and smoke detectors, leading to inaccurate definition of safety boundaries between equipment. Physical interference involves installation space conflicts, while functional interference includes thermal effects and water mist interference. Furthermore, the lack of a multi-objective optimization mechanism prevents the coordinated optimization of lighting uniformity, sprinkler coverage efficiency, and blind spot control. In addition, traditional layout methods are insufficiently adaptable to the complex environmental conditions of hydropower plants, such as high humidity and vibration, as well as the diversity of equipment parameters, affecting the safety, functional coordination, and implementation efficiency of equipment layout.

[0024] This application proposes a simplified model construction method for the integrated layout of terminal equipment on the roof of a hydropower station powerhouse, including the following steps: Step S10: Obtain the lighting parameters and geometric dimensions of the lighting fixtures, the spray parameters and geometric dimensions of the sprinkler heads, and the smoke detection parameters and geometric dimensions of the smoke detectors, and store the lighting parameters, spray parameters, smoke detection parameters, and geometric dimensions as equipment constraints in the database. Step S20: Based on design specifications and product technical manuals, quantitatively extract the physical interference distance thresholds and functional interference distance thresholds between each pair of lighting fixtures, sprinkler heads, and smoke detectors, and collectively refer to the physical interference distance thresholds and functional interference distance thresholds as the minimum safe distance set; Step S30: Using the two-dimensional projection plane of the ceiling as the layout space, establish a collaborative layout model. The input of the collaborative layout model is the equipment constraints and the minimum safe distance set. The output of the collaborative layout model is the optimal coordinate set of lighting fixtures, sprinkler heads, and smoke detectors in the layout space. The construction and solution process of the collaborative layout model includes initializing the population, setting a multi-objective optimization function, and finding the optimal solution set that satisfies all constraints through iterative calculation. Step S40: Select an optimal solution from the optimal solution set, generate an integrated layout scheme that includes the device positioning coordinates and orientation angles, and output the integrated layout scheme in the form of a visualized two-dimensional or three-dimensional drawing.

[0025] Equipment constraints refer to the set of lighting parameters and geometric dimensions of lighting fixtures, spray parameters and geometric dimensions of sprinkler heads, and smoke detection parameters and geometric dimensions of smoke detectors. These constraints can be achieved by manually entering parameters or automatically retrieving them from the equipment database. For example, operators can manually input data or obtain equipment parameters by calling standard data interfaces.

[0026] The minimum safe distance set refers to the collective term for physical interference distance thresholds and functional interference distance thresholds. It can be quantitatively extracted based on design specifications and product technical manuals, for example, by directly obtaining threshold data from industry standard manuals or by statistical analysis using historical layout cases. Therefore, the collaborative layout model uses the two-dimensional projection plane of the ceiling as the layout space. Its inputs are equipment constraints and the minimum safe distance set, and its output is the optimal set of coordinates for lighting fixtures, sprinkler heads, and smoke detectors within the layout space. Specifically, the initial population can be generated using a random distribution method within the layout space to generate initial equipment position coordinates, for example, by using a uniform random number generator to determine coordinate points within the projection plane.

[0027] The multi-objective optimization function can be configured to optimize multiple objectives simultaneously, such as by directly processing multiple optimization objectives using a multi-objective evolutionary algorithm. Through iterative calculation, the optimal solution set can be found, for example, using a particle swarm optimization algorithm for iterative solution. Finally, the integrated layout scheme generates a scheme containing device positioning coordinates and orientation angles, and outputs it in the form of visualized two-dimensional or three-dimensional drawings. The core innovation of this embodiment lies in solving the problems of inaccurate inter-device interference distance quantization, insufficient multi-objective optimization, and poor adaptability to complex environments and diverse device parameters in traditional layout methods by integrating device parameter acquisition, interference distance quantization, and multi-objective collaborative optimization mechanisms.

[0028] For example, in the specific implementation of the roof layout of a hydropower station powerhouse, the lighting parameters of the lighting fixtures include a rated illuminance of 380 lux and a beam angle distribution of 105 degrees, with a geometric dimension of 190 mm in diameter; the spray parameters of the sprinkler heads include a rated working pressure of 0.42 MPa and a spray angle range of 88 degrees, with a geometric dimension of 24 mm in outer diameter; the smoke detector parameters include a sensitivity level of A and a detection radius of 8.5 m, with a geometric dimension of 98 mm in diameter. Based on the design specifications, the physical interference distance threshold between the lighting fixtures and the sprinkler heads is determined to be 460 mm, the functional interference distance threshold is calculated to be 340 mm, and the minimum safe distance is the larger of the two, 460 mm. Within the two-dimensional projection plane of the ceiling, the initial population generates initial device position coordinates in the layout space using the Latin hypercube sampling method; the multi-objective optimization function is set to simultaneously optimize the sum of the deviations between the actual distance and the minimum safe distance between devices, the illuminance uniformity on the reference plane, and the detection blind zone area; through iterative calculation, the optimal solution set that satisfies all constraints is obtained; finally, the optimal solution is selected to generate a layout scheme containing device positioning coordinates, and output in the form of three-dimensional drawings.

[0029] This application further proposes that step S10 includes the following steps: Step S101: Collect basic equipment parameters, including the rated illuminance value, beam angle distribution and luminous flux output of lighting fixtures, the rated working pressure, flow characteristic curve and spray angle range of sprinkler heads, and the sensitivity level, detection radius and response time threshold of smoke detectors. Step S102: Measure the geometric features of the equipment, including the outline dimensions and mounting base interface dimensions of the lighting fixtures, the nozzle outer diameter and splash plate structural dimensions of the sprinkler heads, and the detector diameter and installation method dimensions of the smoke detectors. Step S103: Perform environmental adaptability correction. Based on the environmental characteristics of the hydropower plant, the basic parameters are corrected, including applying humidity influence coefficient and vibration attenuation coefficient to the lighting fixture parameters, water flow stability coefficient and pressure fluctuation compensation coefficient to the sprinkler head parameters, and electromagnetic compatibility coefficient to the smoke detector parameters. Step S104: Establish a parameter database, classify and store the equipment parameters after environmental adaptability correction according to equipment type, and establish a mapping relationship between equipment parameters and plant environmental conditions.

[0030] Acquiring basic equipment parameters refers to obtaining quantitative indicators of the core functional characteristics of the equipment. This can be achieved using technical specifications provided by the equipment manufacturer, laboratory test data, or on-site measured data. The purpose is to provide accurate functional constraints for subsequent layout optimization. Measuring the geometric characteristics of the equipment can be understood as determining the boundary conditions of the physical space occupied by the equipment. This can be achieved using 3D scanners, caliper measurements, or CAD model extraction. The purpose is to accurately quantify the space range required for equipment installation and avoid physical interference. Performing environmental adaptability correction refers to the process of dynamically adjusting equipment parameters according to specific working environments. This can be achieved by combining environmental sensor monitoring data with a preset correction coefficient table. The purpose is to eliminate the interference of special environmental factors of hydropower stations on equipment performance and make the parameters more consistent with the actual operating state. Establishing a parameter database refers to building a structured data storage system. This can be achieved using a relational database management system or a NoSQL database. The purpose is to achieve efficient management of equipment parameters and intelligent matching of environmental conditions, supporting dynamic parameter calls from the layout model.

[0031] When collecting parameters for lighting fixtures, we acquire their rated illuminance, beam angle distribution, and luminous flux output, among other lighting characteristics. We also measure their geometric features, such as their external dimensions and mounting base interface dimensions. For the high-humidity environment of hydropower stations, we apply a humidity influence coefficient to the lighting fixture parameters. This coefficient can be dynamically adjusted based on real-time data from environmental humidity sensors. The corrected parameters are stored in a lighting equipment parameter table, and a mapping relationship is established between the parameters and the humidity levels of different areas of the plant. For sprinkler heads, we collect their rated operating pressure, flow characteristic curves, and spray angle range. We measure the sprinkler head outer diameter and splash plate structural dimensions, apply a water flow stability coefficient to compensate for the impact of water flow fluctuations in the hydropower station, and finally classify and store the parameters, establishing an environmental mapping relationship.

[0032] This application further proposes that step S20 includes the following steps: Step S201: Determine the physical interference distance threshold. This is done by analyzing the equipment's geometry to determine the foundation installation spacing, and then calculating the physical interference distance threshold based on the minimum operating space required for equipment installation and maintenance access requirements. The calculation formula is as follows: ,in Basic installation spacing, To allow for a safety margin in spacing, The installation difficulty level of the hydropower station, Spacing for construction error compensation; Step S202: Determine the functional interference distance threshold. By analyzing the functional characteristics of the equipment, determine the heat-affected zone distance, water mist-affected zone distance, and light interference distance, and calculate the functional interference distance threshold. The calculation formula is as follows: ,in For heat-affected distance, For light interference distance, α, β, and γ represent the distance of influence of water mist, and α, β, and γ are the functional interference weight coefficients under the specific environment of the hydropower station. Step S203: Establish a minimum safety distance set. By comparing the physical interference distance threshold and the functional interference distance threshold, the larger of the two values ​​is taken as the basic safety distance. Then, the safety redundancy requirements specific to the hydropower station powerhouse are added to form the final minimum safety distance set.

[0033] In practical applications, the physical interference distance threshold This refers to the minimum distance required to avoid physical collisions between devices. It can be calculated based on the device's geometry, minimum operating space, and maintenance access requirements, specifically through the formula... The purpose of quantification is to adapt to the complex construction conditions of hydropower plant buildings and avoid equipment collisions or maintenance difficulties due to insufficient installation space or errors.

[0034] Functional Interference Distance Threshold This can be understood as the minimum distance required for devices to function without interfering with each other. It can be achieved using a weighted combination of thermal impact distance, water mist impact distance, and optical interference distance, specifically through the formula... Quantification is performed; the purpose of introducing this feature is to reflect the unique environmental impacts of high temperature, water mist diffusion, and lighting interference in hydropower plant buildings, making the functional distance calculation more consistent with real-world operating scenarios. Specifically, the minimum safe distance set refers to the final safe distance that integrates physical installation constraints and functional mutual exclusion requirements. It can be achieved by taking the larger value between the physical interference distance threshold and the functional interference distance threshold and superimposing a safety redundancy. The purpose of introducing this feature is to improve the reliability of the safe distance through a dual verification mechanism, providing precise constraints for subsequent layout optimization.

[0035] In the scenario of hydropower station powerhouse roof layout, the calculation of the distance between lighting fixtures and sprinkler heads first involves determining the foundation installation distance by measuring the geometric dimensions of the equipment. In conjunction with the evaluation of the minimum operating space dimensions based on the column grid structure of the powerhouse, an installation difficulty coefficient for hydropower stations, reflecting the complexity of construction, is introduced. And the construction error compensation spacing considering installation errors The physical interference distance threshold is calculated. Simultaneously, the impact of water mist generated by the sprinkler heads on smoke detectors was analyzed, and the influence distance of the water mist was quantified. And combined with the thermal effect distance of the lighting fixtures' heat radiation on the sprinkler heads The functional interference distance threshold is obtained by weighting the functional interference weight coefficients β and γ from the environmental calibration of the hydropower station. Then, the two thresholds are compared, and the larger value is taken as the basic safety distance. Safety redundancy is then added according to the fire protection level requirements of the factory building to form a minimum safety distance set, which is used to constrain the subsequent layout optimization process.

[0036] Furthermore, in step S30, the initialization of the population includes: Within the layout space, based on the building structure column grid and equipment functional zoning of the hydropower station powerhouse, priority areas for the arrangement of lighting fixtures, mandatory areas for the arrangement of sprinkler heads, and areas for the full coverage of smoke detectors are pre-defined. Using the Latin hypercube sampling method, initial device location coordinates that satisfy the device constraints are generated in the priority arrangement area, the forced arrangement area, and the full coverage arrangement area, respectively, forming an initial population.

[0037] The structural column grid refers to the grid system formed by the regularly arranged columns of the supporting structure in a hydropower plant. It can be rectangular or irregular, depending on the plant's architectural design. Its purpose is to clearly define the distribution of structural obstacles and avoid physical interference between equipment installation locations and load-bearing columns. Equipment functional zoning can be understood as spatial areas divided according to the functional characteristics of equipment, such as work areas, equipment areas, or passageways. Its purpose is to match equipment layout with the actual usage needs of the plant. The priority placement area for lighting fixtures refers to the recommended installation range set for continuous illumination coverage. It can be divided into continuous strips or discrete dot matrix areas, specifically determined by predicting low-illuminance areas using lighting simulation software, with the aim of improving lighting uniformity. Sprinkler heads... Mandatory coverage areas refer to critical areas that must be covered according to fire safety regulations. These areas can be defined using closed polygons or gridded regions, such as above flammable material storage areas. Their purpose is to ensure the mandatory implementation of fire extinguishing functions. Full coverage areas for smoke detectors refer to detection ranges that must be completely covered without blind spots. These areas can be set using layered grids or dynamic boundary regions, specifically calculated through smoke diffusion models. Their purpose is to ensure the comprehensiveness of fire early warning. Latin hypercube sampling is a stratified random sampling technique that can employ stratified uniform distribution or space-filling sampling strategies. Specifically, it is achieved by dividing a multidimensional space into equally probable sub-intervals and sampling independently. Its purpose is to efficiently explore feasible solution spaces with limited samples, avoiding the clustering effects of traditional random sampling.

[0038] In the layout of the roof of a hydropower station, the column grid distribution was identified based on the architectural drawings, and the roof was divided into a lighting priority zone centered on the work platform, a forced sprinkler zone centered on the oil storage area, and a full detection coverage zone covering the entire roof plane. In the lighting priority zone, Latin hypercube sampling was used to generate coordinates for lighting fixtures, ensuring that the coordinate points were evenly distributed and avoided the column grid projection area. In the forced sprinkler zone, the coordinates of sprinkler heads were generated through sampling, ensuring that each coordinate point was located above the flammable material storage area. In the full detection coverage zone, the coordinates of smoke detectors were generated through sampling, ensuring that the coordinate points covered all corners of the roof. All coordinate generation processes were boundary-checked based on the equipment geometry and minimum safety distance set to ensure that the initial layout met the equipment constraints.

[0039] Furthermore, in step S30, the multi-objective optimization function consists of the following three sub-objective functions: The first sub-objective function F1 is used to minimize the sum of the deviations between the actual distances between all device pairs and the corresponding minimum safe distance set; The second sub-objective function F2 is used to maximize the illuminance uniformity of the lighting fixtures on a specified reference plane. The third sub-objective function F3 is used to minimize the blind zone area of ​​all smoke detectors in the layout space. The multi-objective optimization function is expressed as simultaneously optimizing the first sub-objective function F1, the second sub-objective function F2, and the third sub-objective function F3.

[0040] The first sub-objective function F1 is the objective function used to quantify the safety of equipment layout. It can be achieved by accumulating the absolute value of the deviation between the actual distance between equipment pairs and the minimum safe distance set or by weighted accumulation. Its purpose is to strictly constrain the equipment spacing to avoid the risk of physical and functional interference.

[0041] The second sub-objective function F2 can be understood as an objective function characterizing the optimization of lighting quality. Specifically, it can be achieved based on the ratio of the minimum illuminance value to the average illuminance value or other uniformity evaluation indicators. Its purpose is to improve the visual comfort and operational safety of the working area of ​​the hydropower plant.

[0042] The third sub-objective function F3 refers to the objective function characterizing the fire detection coverage performance. It can be implemented by calculating the area of ​​the unmonitored area or by evaluating the coverage efficiency based on the detection radius. Its purpose is to enhance the reliability and timeliness of fire early warning. In addition, the multi-objective optimization function is represented by simultaneously optimizing multiple sub-objective functions. It can be implemented by Pareto optimization, weighted sum method or objective programming method. Its purpose is to balance different performance dimensions and avoid the optimization process from focusing too much on a single indicator.

[0043] The first sub-objective function F1, based on the minimum safe distance set extracted in step S20, calculates the cumulative deviation between the actual distance between equipment pairs and the safe threshold, ensuring that the layout strictly meets the physical and functional interference constraints unique to the hydropower station environment. The second sub-objective function F2, combined with the lighting parameters of the lighting fixtures obtained in step S10, optimizes the illuminance distribution on the designated reference plane of the powerhouse, ensuring uniform light coverage of the working area. The third sub-objective function F3 integrates the smoke detection parameters and geometric dimensions of the smoke detectors, minimizing the area of ​​uncovered regions to ensure no blind spots in smoke monitoring. These three sub-objective functions are simultaneously incorporated into the optimization framework, generating an optimal solution set through iterative calculations. This enables the equipment layout to achieve a dynamic balance between safe distance, lighting uniformity, and detection coverage, thereby overcoming the performance imbalance problem caused by traditional single-objective optimization.

[0044] In the layout of the hydropower station's powerhouse roof, lighting fixtures are positioned above the main working areas, sprinkler heads are evenly distributed across the roof grid nodes, and smoke detectors cover the area around critical equipment and at passageway corners. During optimization, the first sub-objective function F1 ensures that the distance between the lighting fixtures and sprinkler heads is greater than the heat-affected zone and the water mist effect distance, preventing water mist from interfering with the heat dissipation or optical path of the fixtures. The second sub-objective function F2 adjusts the coordinate positions of the lighting fixtures to achieve uniform illuminance distribution on the work surface, reducing areas of alternating light and dark. The third sub-objective function F3 optimizes the installation angle and position of the detectors to reduce blind spots caused by large equipment. By simultaneously optimizing these three objective functions, the resulting layout scheme achieves comprehensive coordination in terms of equipment safety spacing, lighting uniformity, and detection coverage.

[0045] Furthermore, the formula for calculating the first sub-objective function F1 is as follows:

[0046] Where n is the total number of devices. This represents the set of minimum safe distances between device i and device j. This represents the actual Euclidean distance between device i and device j.

[0047] Specifically, the first sub-objective function F1 refers to the objective function used to quantify the degree of violation of equipment layout safety distance constraints. It can be implemented using a deviation accumulation mechanism under a multi-objective optimization framework, with the aim of accurately assessing the compliance of equipment spacing in the layout scheme. The total number of equipment n can be understood as the total number of lighting fixtures, sprinkler heads, and smoke detectors in the ceiling layout space. It can be determined by dynamic counting based on actual layout requirements, with the aim of limiting the summation range to cover all equipment pair combinations. The minimum safety distance set... This refers to the minimum distance threshold that must be met between device i and device j. It can be achieved based on the larger of the physical interference distance threshold and the functional interference distance threshold determined in step S20, aiming to reflect the unique environmental constraints of the hydropower plant. (Actual Euclidean distance) Specifically, it is the straight-line distance between device i and device j on the two-dimensional projection plane of the ceiling, which can be calculated by coordinate position, with the aim of providing an objective measure of geometric relationship; The function can be understood as a threshold judgment mechanism that generates a positive deviation only when the actual distance is less than the minimum safe distance. It can be implemented using conditional judgment logic, with the aim of avoiding invalid calculations and highlighting the severity of safety violations.

[0048] This formula utilizes the max(0,·) function to ensure that the deviation value is only included when the actual distance is lower than the minimum safe distance, thus strictly adhering to the unidirectional characteristic of the safe distance constraint; by covering all possible device pair combinations within the layout space with double summation symbols, it ensures that no global interference risk is overlooked in the assessment; minimum safe distance set The introduction of this feature allows the deviation calculation to closely align with the unique physical and functional interference thresholds of hydropower plant buildings, such as considering the impact of environmental factors like humidity and vibration on equipment spacing; actual Euclidean distance. The adoption of this method directly relates to the coordinate position of the equipment on the two-dimensional projection plane, providing an intuitive geometric basis for deviation calculation. In the iterative optimization of the collaborative layout model, this function value serves as a key optimization index to guide the population to evolve in a direction that satisfies safety constraints. When the F1 value approaches zero, it indicates that all equipment pairs meet the minimum safety distance requirement, thereby effectively ensuring the safety of equipment layout when generating the optimal solution set.

[0049] The second sub-objective function F2 is used to maximize the illuminance uniformity of the lighting fixtures on a specified reference plane. However, in its implementation, there is a lack of specific calculation methods for illuminance uniformity, which makes it impossible to accurately quantify the lighting uniformity, thereby affecting the accurate evaluation of lighting performance and the reliability of layout schemes in multi-objective optimization.

[0050] In response, this application further proposes the following formula for calculating illuminance uniformity in the second sub-objective function F2:

[0051] Where U represents the uniformity of illuminance. To specify the minimum illuminance value for grid points on the reference plane, The average illuminance value for all grid points on a specified reference plane is calculated based on the inverse square law and the cosine law using the lighting parameters of the lighting fixtures and their coordinate positions.

[0052] In practical applications, illuminance uniformity U refers to a quantitative index of the uniformity of lighting distribution. It can be expressed as the ratio of the minimum illuminance value to the average illuminance value, aiming to objectively assess the uniformity of the lighting layout. This refers to the illuminance value at the grid point with the lowest illuminance on a specified reference plane. It can be achieved by calculating the illuminance point-by-point after dividing the reference plane into a grid and taking the minimum value. The purpose is to ensure that the weakest areas of illumination meet the requirements. Specifically, Illuminance is the arithmetic mean of the illuminance values ​​at all grid points on a specified reference plane. It can be achieved by summing the illuminance values ​​at each grid point and dividing by the total number of grid points. The purpose is to establish a benchmark for overall lighting intensity. In practical applications, illuminance calculation refers to determining the illuminance at each point in space based on the lighting parameters and coordinate positions of the lighting fixtures. This can be achieved using a combination of methods: calculating distance attenuation based on the inverse square law and calculating angle attenuation based on the cosine law. The aim is to accurately simulate the distribution characteristics of light in space.

[0053] In the layout design of the hydropower station powerhouse roof, this application uses the ground of the working area as a designated reference plane and divides it into a regular grid. The lighting parameters of the luminaires include luminous flux and beam angle, and their coordinate positions are determined by the layout scheme. During calculation, for each grid point, the illuminance attenuation is calculated using the inverse square law based on the distance from the luminaire to that point, and the angle attenuation is calculated using the cosine law based on the incident angle of the light. Then, the contributions of all luminaires to that point are summed to obtain the total illuminance. Finally, the minimum illuminance value among all grid points is taken as the total illuminance. The average illuminance of all grid points is taken as ,calculate As an indicator of illuminance uniformity.

[0054] Furthermore, in step S30, the optimal solution set satisfying all constraints is found through iterative calculation using a non-dominated sorting genetic algorithm with an elite retention strategy. The process includes the following steps: Step S381: For each individual layout scheme in the current population, calculate its dominance relationship based on the multi-objective optimization function value of the hydropower plant equipment layout, and divide the entire population into multiple non-dominated levels. Step S382: Within the same non-dominated level, calculate the crowding degree of each individual for the three objective functions of lighting uniformity, equipment safety distance, and detection coverage; Step S383: Merge the parent and offspring populations, perform non-dominated sorting and crowding calculation, prioritize individuals at higher non-dominated levels, and for individuals at the same level, select individuals that are more evenly distributed across multiple optimization objectives of the hydropower plant equipment layout to form a new parent population. Step S384: Perform device type-based grouping selection, arithmetic crossover considering layout area constraints, and boundary mutation operations subject to minimum safe distance on the new parent population to generate a new offspring population; Step S385: In each iteration, based on the layout characteristics of the hydropower plant equipment, the adaptive penalty function method is used to handle violations of the minimum safety distance set, and the penalty function coefficients are dynamically adjusted with the number of iterations; Step S386: Repeat steps S381 to S385 until the preset maximum number of iterations is reached, or the optimal solution set of the population on the three optimization objectives of the hydropower plant equipment layout no longer shows significant improvement over multiple consecutive generations.

[0055] The non-dominated sorting genetic algorithm with elite preservation strategy refers to a multi-objective optimization algorithm framework. It can be implemented by combining Pareto sorting with crowding comparison, aiming to maintain population diversity and accelerate convergence. The non-dominated hierarchy can be understood as a solution set hierarchy divided according to the dominance relationship of the multi-objective optimization function values. It can be layered using a fast non-dominated sorting algorithm, aiming to efficiently identify globally high-quality solutions. Crowding specifically refers to an index that measures the distribution density of individuals in the objective space. It can be calculated by the sum of the distance differences between adjacent individuals in the objective function value, aiming to prevent the algorithm from converging prematurely to a local region. The adaptive penalty function method can be understood as a mechanism for dynamically adjusting the penalty strength for constraint violation. It can adjust the penalty coefficient using an exponential decay function based on iterative algebra, aiming to balance constraint satisfaction with the stage requirements of objective optimization.

[0056] Within the two-dimensional projection plane of the hydropower station's roof, the layout schemes of lighting fixtures, sprinkler heads, and smoke detectors are encoded as population individuals. After initializing the population, the function values ​​of each individual on three objectives—lighting uniformity, equipment safety distance, and detection coverage—are calculated. Based on the function values, non-dominated sorting is performed, dividing the population into multiple levels, and the crowding degree of each objective dimension is calculated within the same level. After merging the parent and offspring populations, individuals with higher non-dominated levels are selected first, and for individuals within the same level, those with uniform objective distribution are selected to form a new parent population.

[0057] The new parent population is grouped by equipment type and a selection operation is performed. The lighting fixture group focuses on the lighting uniformity index, the sprinkler head group focuses on the safety distance index, and the smoke detector group focuses on the detection coverage index. Arithmetic crossover is performed within the column grid boundary to generate offspring coordinates. During mutation operations, the movement range of equipment is limited to not exceeding the minimum safety distance threshold. In each iteration, a dynamically adjusted penalty term is applied to layout schemes that violate the minimum safety distance. The initial penalty coefficient is small to allow exploration, and it gradually increases with the number of iterations. The above process is repeated until the solution set of the population on the three optimization objectives is stable, and a high-quality optimal solution set that satisfies the constraints is output.

[0058] Furthermore, the multi-attribute decision-making method based on entropy weights refers to a decision-making method that objectively determines the weights of evaluation indicators using information entropy theory. It can be implemented using the entropy weight TOPSIS method, aiming to avoid biases caused by subjective weighting and ensure that weight allocation is strictly based on data characteristics. The decision matrix can be understood as a structured data table, where rows represent different layout schemes and columns represent evaluation indicators. It can be constructed and stored using spreadsheet software or database tables, aiming to systematically organize the performance data of alternative solutions. Vector normalization specifically refers to standardizing each column of the decision matrix, which can be implemented using vector modulus normalization or max-min normalization methods, aiming to eliminate dimensional differences between different indicators. The information entropy principle for calculating entropy values ​​refers to determining information entropy based on the degree of variation of indicator values, which can be calculated using the Shannon entropy formula, aiming to objectively reflect the importance of each indicator.

[0059] Objective weights can be understood as data-driven indicator weights, which can be obtained by normalizing the entropy value using 1-entropy, aiming to highlight key indicators with large variability. The weighted standardization matrix is ​​specifically the product of the normalized matrix and the weight vector, which can be achieved using matrix multiplication, aiming to strengthen the influence of important indicators. A positive ideal solution refers to a virtual solution that achieves optimal values ​​across all indicators, which can be constructed based on the maximum value in the standardization matrix, aiming to provide an optimal reference benchmark. A negative ideal solution refers to a virtual solution that achieves worst values ​​across all indicators, which can be constructed based on the minimum value in the standardization matrix, aiming to provide a worst-case reference benchmark. Euclidean distance specifically refers to calculating the geometric distance between the candidate solution and the ideal solution, which can be calculated using the Euclidean distance formula, aiming to quantify the deviation of the solution from the ideal state. Relative proximity is the ratio of the distance between the candidate solution and the positive ideal solution to the total distance, which can be calculated using the distance ratio formula, aiming to comprehensively evaluate the overall performance balance of the solution.

[0060] By constructing a decision matrix to structure the performance data of the optimal solution set, vector normalization is then performed to eliminate the inherent differences in the dimensions and orders of magnitude of the three indicators: lighting uniformity, equipment safety distance, and detection coverage, ensuring fair comparison of heterogeneous indicators. Next, the entropy value of each evaluation indicator is calculated based on the principle of information entropy to objectively determine the weights and avoid subjective interference from human weighting. Then, a weighted standardization matrix is ​​formed to highlight the influence intensity of key performance indicators. On this basis, positive and negative ideal solutions are identified as absolute reference benchmarks for evaluating solutions. The Euclidean distance between each candidate solution and the ideal solution is calculated, and the relative closeness is determined to scientifically measure the comprehensive merits of the solutions. Finally, the maximum value is selected as the optimal solution by comparing the relative closeness, ensuring that the selected scheme achieves the best synergistic effect on multiple objectives such as lighting uniformity, equipment safety distance, and detection coverage, thereby effectively solving the performance imbalance problem caused by multi-objective conflicts.

[0061] In the layout design of the roof equipment of a hydropower station, multiple alternative solutions in the optimal solution set are used as evaluation objects. A decision matrix is ​​constructed using lighting uniformity, equipment safety distance, and detection coverage as evaluation indicators. After vector normalization of the decision matrix, the objective weights of each indicator are calculated based on the principle of information entropy. After forming a weighted standardized matrix, the positive and negative ideal solutions are determined. The Euclidean distance between each alternative solution and the ideal solution is calculated to obtain the relative proximity. The alternative solution with the largest relative proximity is selected as the optimal layout scheme. This scheme ensures uniform lighting, meets safety distance requirements, and achieves full detection coverage.

[0062] This application also includes step S50: Import the integrated layout scheme generated in step S40 into the pre-built hydropower plant building information model; Perform lighting simulation, sprinkler coverage simulation, and smoke detection simulation in a building information modeling environment; By comprehensively analyzing the output results of the three performance simulations, it can be determined whether the integrated layout scheme simultaneously meets the lighting requirements, fire extinguishing requirements, and fire early warning requirements. If the verification fails, the verification result is fed back to step S30 and used as a new constraint condition to solve the model again.

[0063] The pre-built hydropower plant building information model refers to a digital three-dimensional model that includes the plant's geometry, material properties, and environmental parameters. This model can be implemented using a BIM technology platform, such as Revit or OpenBIM standards, to provide a high-precision spatial data foundation to support subsequent simulation verification. Lighting simulation, sprinkler coverage simulation, and smoke detection simulation can be understood as a physics engine-based simulation process. Lighting simulation can specifically employ ray tracing algorithms or radiance methods, sprinkler coverage simulation can use computational fluid dynamics models, and smoke detection simulation can use particle diffusion or field models. The aim is to accurately capture the impact of dynamic environmental factors on equipment performance. Comprehensive analysis of the output results of the three performance simulations refers to multi-dimensional cross-evaluation of the simulation data, which can be achieved using decision matrices or fuzzy comprehensive evaluation methods. The aim is to quantitatively judge the synergistic performance of the layout scheme in terms of lighting uniformity, fire suppression coverage, and detection coverage. Feeding the verification results back to step S30 as new constraints means transforming the violation parameters identified in the simulation into constraint parameters for the optimized model. This can be achieved using an adaptive penalty function mechanism or constraint relaxation techniques, with the aim of dynamically adjusting the safety distance threshold to adapt to actual environmental conditions.

[0064] As a specific implementation method, the solution of this application is implemented as follows: During the hydropower plant design phase, the equipment layout scheme generated in S40 is imported into the building information model constructed based on BIM technology; DIALux software is used to perform lighting simulation to evaluate lighting uniformity, Fire Dynamics Simulator is used to perform sprinkler coverage simulation to verify the fire extinguishing range, and PyroSim is used to conduct smoke detection simulation to check the early warning coverage; the three simulation results are cross-validated through a custom-developed analysis module. When a blind zone is identified in the smoke detector, the spatial coordinates of the area and the blind zone range are transformed into additional constraints of the minimum safe distance set, and fed back to step S30 to re-execute the optimal solution set solution until the layout scheme simultaneously meets the requirements of lighting, fire extinguishing and fire early warning.

[0065] The above technical solutions effectively solve the safety and functionality deficiencies caused by the lack of actual environmental simulation verification of the layout scheme. They avoid safety hazards such as uneven lighting, blind spots in sprinkler coverage, or detection failure caused by simplification of the model and neglect of dynamic environmental changes. They significantly improve the reliability and adaptability of the hydropower plant equipment layout under real operating conditions.

[0066] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A simplified model construction method for the integrated layout of terminal equipment on the roof of a hydropower station powerhouse, characterized in that, Includes the following steps: Step S10: Obtain the lighting parameters and geometric dimensions of the lighting fixtures, the spraying parameters and geometric dimensions of the sprinkler heads, and the smoke detection parameters and geometric dimensions of the smoke detectors, and store the lighting parameters, spraying parameters, smoke detection parameters, and geometric dimensions as equipment constraints in the database. Step S20: Based on design specifications and product technical manuals, quantitatively extract the physical interference distance thresholds and functional interference distance thresholds between each pair of lighting fixtures, sprinkler heads, and smoke detectors, and collectively refer to the physical interference distance thresholds and functional interference distance thresholds as the minimum safe distance set; Step S30: Using the two-dimensional projection plane of the ceiling as the layout space, establish a collaborative layout model. The input of the collaborative layout model is the equipment constraints and the minimum safe distance set. The output of the collaborative layout model is the optimal coordinate set of lighting fixtures, sprinkler heads, and smoke detectors within the layout space. The construction and solution process of the collaborative layout model includes initializing the population, setting a multi-objective optimization function, and finding the optimal solution set that satisfies all constraints through iterative calculation. Step S40: Select an optimal solution from the set of optimal solutions, generate an integrated layout scheme that includes the device positioning coordinates and orientation angles, and output the integrated layout scheme in the form of a visualized two-dimensional or three-dimensional drawing.

2. The method as described in claim 1, characterized in that, Step S10 includes: Step S101: Collect basic equipment parameters, including the rated illuminance value, beam angle distribution and luminous flux output of lighting fixtures, the rated working pressure, flow characteristic curve and spray angle range of sprinkler heads, and the sensitivity level, detection radius and response time threshold of smoke detectors. Step S102: Measure the geometric features of the equipment, including the outline dimensions and mounting base interface dimensions of the lighting fixtures, the nozzle outer diameter and splash plate structural dimensions of the sprinkler heads, and the detector diameter and installation method dimensions of the smoke detectors. Step S103: Perform environmental adaptability correction. Based on the environmental characteristics of the hydropower plant, the basic parameters are corrected, including applying humidity influence coefficient and vibration attenuation coefficient to the lighting fixture parameters, water flow stability coefficient and pressure fluctuation compensation coefficient to the sprinkler head parameters, and electromagnetic compatibility coefficient to the smoke detector parameters. Step S104: Establish a parameter database, classify and store the equipment parameters after environmental adaptability correction according to equipment type, and establish a mapping relationship between equipment parameters and plant environmental conditions.

3. The method as described in claim 1, characterized in that, Step S20 includes: Step S201: Determine the physical interference distance threshold. This is done by analyzing the equipment's geometry to determine the foundation installation spacing, and then calculating the physical interference distance threshold based on the minimum operating space required for equipment installation and maintenance access requirements. The calculation formula is as follows: in, Basic installation spacing, To allow for a safety margin in spacing, The installation difficulty level of the hydropower station, Spacing for construction error compensation; Step S202: Determine the functional interference distance threshold. By analyzing the functional characteristics of the equipment, determine the heat-affected zone distance, water mist-affected zone distance, and light interference distance, and calculate the functional interference distance threshold. The calculation formula is as follows: in, For heat-affected distance, For light interference distance, α, β, and γ represent the distance of influence of water mist, and α, β, and γ are the functional interference weight coefficients under the specific environment of the hydropower station. Step S203: Establish a minimum safety distance set. By comparing the physical interference distance threshold and the functional interference distance threshold, the larger of the two values ​​is taken as the basic safety distance. Then, the safety redundancy requirements specific to the hydropower station powerhouse are added to form the final minimum safety distance set.

4. The method as described in claim 1, characterized in that, In step S30, the initialization of the population includes: Within the layout space, based on the building structure column grid and equipment functional zoning of the hydropower station powerhouse, priority areas for the arrangement of lighting fixtures, mandatory areas for the arrangement of sprinkler heads, and areas for the full coverage of smoke detectors are pre-defined. Using the Latin hypercube sampling method, initial device location coordinates that satisfy the device constraints are generated in the priority arrangement area, the forced arrangement area, and the full coverage arrangement area, respectively, forming an initial population.

5. The method as described in claim 1, characterized in that, In step S30, the multi-objective optimization function consists of the following three sub-objective functions: The first sub-objective function F1 is used to minimize the sum of the deviations between the actual distances between all device pairs and the corresponding minimum safe distance set; The second sub-objective function F2 is used to maximize the illuminance uniformity of the lighting fixtures on a specified reference plane. The third sub-objective function F3 is used to minimize the detection blind zone area of ​​all smoke detectors in the layout space; The multi-objective optimization function is expressed as simultaneously optimizing the first sub-objective function F1, the second sub-objective function F2, and the third sub-objective function F3.

6. The method as described in claim 5, characterized in that, The formula for calculating the first sub-objective function F1 is: Where n is the total number of devices. This represents the set of minimum safe distances between device i and device j. This represents the actual Euclidean distance between device i and device j.

7. The method as described in claim 5, characterized in that, In the second sub-objective function F2, the formula for calculating the illuminance uniformity is: Where U represents the uniformity of illuminance. To specify the minimum illuminance value for grid points on the reference plane, The average illuminance value for all grid points on a specified reference plane is calculated based on the inverse square law and the cosine law using the lighting parameters of the lighting fixture and its coordinate position.

8. The method as described in claim 1, characterized in that, In step S30, the process of finding the optimal solution set that satisfies all constraints through iterative calculation is performed using a non-dominated sorting genetic algorithm with an elite retention strategy. The process includes: Step S381: For each individual layout scheme in the current population, calculate its dominance relationship based on the multi-objective optimization function value of the hydropower plant equipment layout, and divide the entire population into multiple non-dominated levels. Step S382: Within the same non-dominated level, calculate the crowding degree of each individual for the three objective functions of lighting uniformity, equipment safety distance, and detection coverage; Step S383: Merge the parent and offspring populations, perform non-dominated sorting and crowding calculation, prioritize individuals at higher non-dominated levels, and for individuals at the same level, select individuals that are more evenly distributed across multiple optimization objectives of the hydropower plant equipment layout to form a new parent population. Step S384: Perform device type-based grouping selection, arithmetic crossover considering layout area constraints, and boundary mutation operations subject to minimum safe distance on the new parent population to generate a new offspring population; Step S385: In each iteration, based on the layout characteristics of the hydropower plant equipment, the adaptive penalty function method is used to handle violations of the minimum safety distance set, and the penalty function coefficients are dynamically adjusted with the number of iterations; Step S386: Repeat steps S381 to S385 until the preset maximum number of iterations is reached, or the optimal solution set of the population on the three optimization objectives of the hydropower plant equipment layout no longer shows significant improvement over multiple consecutive generations.

9. The method as described in claim 1, characterized in that, In step S40, an optimal solution is selected from the optimal solution set using a multi-attribute decision-making method based on entropy weights, including: Step S391: Take the candidate solutions in the optimal solution set as the evaluation object, and use the sub-objective function values ​​of the multi-objective optimization function of the hydropower plant equipment layout as the evaluation index to construct a decision matrix; Step S392: Perform vector normalization on the decision matrix to eliminate the differences in the dimensions and orders of magnitude of the three indicators: lighting uniformity, equipment safety distance, and detection coverage, and obtain a standardized decision matrix; Step S393: Based on the principle of information entropy, calculate the entropy value of each evaluation index based on the distribution characteristics of the three optimization target values ​​of the hydropower plant equipment layout, and then determine the objective weight of each evaluation index. Step S394: Multiply each column element in the standardized decision matrix by the objective weight of the corresponding evaluation index calculated in step S393 to form a weighted standardized matrix; Step S395: In the weighted normalization matrix, find the optimal values ​​for the three indicators of lighting uniformity, equipment safety distance and detection coverage to form a positive ideal solution, and find the worst values ​​for the three indicators to form a negative ideal solution. Step S396: Calculate the Euclidean distance between each alternative solution for the powerhouse equipment layout of a hydropower station and the positive ideal solution and the negative ideal solution respectively, and then calculate the relative proximity between each alternative solution and the positive ideal solution; Step S397: Compare the relative proximity of all alternative solutions for the layout of hydropower plant equipment, and select the alternative solution with the largest relative proximity value as the optimal solution.

10. The method as described in claim 1, characterized in that, Following step S40, step S50 is also included: The integrated layout scheme generated in step S40 is imported into the pre-built hydropower plant building information model; Lighting simulation, sprinkler coverage simulation, and smoke detection simulation were performed in the building information model environment. By comprehensively analyzing the output results of the three performance simulations, it is determined whether the integrated layout scheme simultaneously meets the lighting requirements, fire extinguishing requirements, and fire early warning requirements. If the verification fails, the verification result is fed back to step S30 and used as a new constraint condition to solve the model again.

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