A method and system for optimizing spatial layout of multi-dimensional sound scene ecological elements

Abstract

The application provides a kind of multi-dimensional sound scene ecological element space layout optimization method and system, belongs to mechanical vibration measurement technical field field, this method includes integration three-dimensional point cloud and environmental parameter construction digital basement, establishes the native plant model containing growth and acoustic characteristics, generates the space sound effect contribution degree pressure field representing the noise reduction potential by reverse sound wave ray tracing, constructs the plant growth agent driven by the pressure field, runs the symbiotic simulation engine to generate the morphological dynamic evolution sequence, calculates the acoustic performance, ecological stability and landscape robustness index, and uses it as the fitness evaluation basis of evolution algorithm, iteratively optimizes the initial planting layout, the application realizes the coupling of acoustic physical simulation and plant growth competition logic, is used to guide the long-term planning and species configuration of ecological garden, improves the sound environment quality and succession stability of landscape.

Description

Technical Field

[0001] This invention relates to the field of digital modeling and multi-objective algorithm optimization, and in particular to a method and system for optimizing the spatial layout of multi-dimensional soundscape ecological elements. Background Technology

[0002] Multidimensional soundscape ecological design, as an important branch of modern landscape architecture and environmental engineering, emphasizes the scientific arrangement of native plant communities to achieve multiple goals such as reducing urban noise, enhancing spatial auditory aesthetics, and protecting biodiversity. With the development of digital twin technology, using computer simulation to perform high-precision modeling and performance prediction of environmental elements in virtual space has become an important way to improve the scientific nature of landscape planning.

[0003] Most existing layout schemes are based on static design logic, referencing the standard morphological parameters of individual plants and manually arranging points in two-dimensional or three-dimensional planes. The prediction of noise reduction effect relies on static reduction models based on forest belt width or fixed sound absorption coefficients, ignoring the significant natural succession attributes of native plant communities. It is difficult to accurately predict the morphological changes of individual plants due to spatial competition during long-term growth, resulting in a significant deviation between the acoustic planning in the initial design stage and the actual effectiveness after the growth period. Existing plant growth models are often isolated from environmental physical needs, ignoring the physical shielding effect of plants and the psychoacoustic masking effect brought about by attracting biological sound. In complex urban ecological garden planning, there is a lack of multi-objective automatic optimization mechanisms, making it difficult to automatically select the best species configuration and planting scheme that can maintain community stability and maximize the acoustic barrier function throughout the long community succession cycle. Summary of the Invention

[0004] This invention provides a method and system for optimizing the spatial layout of multi-dimensional soundscape ecological elements; Firstly, a method for optimizing the spatial layout of multi-dimensional soundscape ecological elements is provided, the method comprising: A digital environmental baseline model is generated by acquiring 3D point cloud data of the area to be optimized and local environmental parameters, and a native plant feature model is constructed by extracting parameters such as growth rate, crown expansion morphology, sound absorption coefficient and scattering intensity of native plants. The location information of noise sources and acoustic sensitive points in the area to be optimized are identified to obtain sound scene evaluation points. The sound scene evaluation points are then mapped to the digital environment base model for inverse acoustic ray tracing to generate a spatial sound effect contribution pressure field that characterizes the noise reduction potential of each point in space. Extract the plant coordinates and species types from the initial planting layout scheme, load the native plant feature model into the digital environment base model, and generate a plant growth agent driven by the spatial sound effect contribution pressure field. The goal-oriented symbiotic simulation engine is run to drive the plant growth agent to perceive the score of the spatial sound effect contribution pressure field and adjust the growth direction and leaf cluster density, generating a dynamic evolution sequence containing the evolution of plant morphology over time. The dynamic evolution sequence is subjected to acoustic loss calculation, biomass change analysis and coverage fluctuation detection, generating sequence acoustic performance index, ecological stability index and landscape robustness index respectively. The acoustic performance index, ecological stability index, and landscape robustness index of the sequence are used as the fitness evaluation criteria for the evolutionary algorithm to iteratively optimize the initial planting layout scheme and output the optimized planting layout scheme.

[0005] Optionally, the step of generating a spatial sound effect contribution pressure field characterizing the noise reduction potential of each point in space specifically includes: establishing a three-dimensional sound field propagation topology map based on the noise source location information and acoustic sensitive point location information; calculating the propagation energy ratio of sound waves in each voxel grid in the digital environment base model to obtain a path energy density map; obtaining the average sound absorption efficiency parameters of different height layers in the native plant feature model; using the average sound absorption efficiency parameters to perform weighted correction on the path energy density map; generating noise reduction sensitivity gradient values ​​for each spatial unit; and performing spatial interpolation and normalization processing on the noise reduction sensitivity gradient values ​​to generate a continuously distributed spatial sound effect contribution pressure field.

[0006] Optionally, the step of driving the plant growth agent to perceive the score of the spatial sound effect contribution pressure field and adjust the growth direction and leaf cluster density specifically includes: obtaining the survival feedback value by acquiring the coordinates of the plant growth agent and the amount of biological resources acquired in its vicinity; retrieving the value of the coordinates in the spatial sound effect contribution pressure field in real time to obtain the engineering feedback value; weighting the survival feedback value and the engineering feedback value through a fusion decision algorithm to generate an agent growth action command, wherein the agent growth action command includes the branch angle adjustment amount and the lateral branch growth increment; updating the three-dimensional mesh morphology of the plant growth agent in the digital environment base model according to the agent growth action command; generating a single growth cycle plant morphology snapshot; and forming the dynamic evolution sequence by continuously iterating the morphology snapshot.

[0007] Optionally, the step of weighting and evaluating the survival feedback value and the engineering feedback value using a fusion decision algorithm to generate the agent's growth action instructions specifically includes calculating the spatial overlap between the plant growth agent and neighboring agents to obtain a competition intensity index, allocating weight coefficients between the survival target and the engineering target according to the competition intensity index to obtain a dynamic weight ratio, multiplying the survival feedback value and the engineering feedback value using the dynamic weight ratio to generate a comprehensive growth benefit value, searching all virtual growth paths within a preset angle range and calculating the corresponding predicted benefit value, selecting the largest of the predicted benefit values ​​as the optimized path direction, and generating the agent's growth action instructions accordingly.

[0008] Optionally, the steps of performing acoustic loss calculation, biomass change analysis, and coverage fluctuation detection on the dynamic evolution sequence to generate sequence acoustic performance index, ecological stability index, and landscape robustness index respectively include retrieving the three-dimensional point cloud morphology of the community at different successional stages in the dynamic evolution sequence, and performing sound wave reflection and absorption simulation in combination with the sound absorption coefficient in the native plant characteristic model to obtain the sound pressure level attenuation component of each successional stage, and accumulating all the sound pressure level attenuation components according to the time weighting function to generate the sequence acoustic performance index characterizing long-term noise reduction robustness.

[0009] Optionally, the process of generating the sequence acoustic performance index further includes identifying the canopy layering structure and community canopy closure parameters in the three-dimensional point cloud morphology of the community, predicting the call frequency and sound power of the invited organisms through a preset species attraction model to obtain the predicted biological sound spectrum, superimposing the predicted biological sound spectrum onto the residual noise spectrum after processing the sound pressure level attenuation component, calculating the psychoacoustic masking value to obtain the sound comfort correction coefficient, and using the sound comfort correction coefficient to perform gain compensation on the sequence acoustic performance index to generate a comprehensive soundscape index that considers psychoacoustic gain.

[0010] Optionally, the steps of generating ecological stability indicators and landscape robustness indicators specifically include monitoring the life state parameters of each plant growth agent in the dynamic evolution sequence, statistically analyzing the proportion of surviving plants at the end of succession to obtain a community stability score, generating the ecological stability indicator based on the community stability score, measuring the ratio of the community projection area to the total area of ​​the target area at each stage in the dynamic evolution sequence to obtain a dynamic coverage sequence, calculating the variance of the dynamic coverage sequence to obtain a spatial continuity evaluation quantity, taking the reciprocal of the spatial continuity evaluation quantity and mapping it to a standard score range, and generating the landscape robustness indicator characterizing visual morphological stability.

[0011] Optionally, the step of iteratively optimizing the initial planting layout scheme specifically includes: serializing and encoding the plant coordinates, spacing, and species ratio in the initial planting layout scheme to generate an initial population gene; performing multiple rounds of simulated evolutionary operations on the initial population gene; calculating the non-dominated ranking level of individuals based on the sequence acoustic performance index, ecological stability index, and landscape robustness index in each round to obtain the evolutionary selection probability; retaining the preferred individuals based on the evolutionary selection probability and performing gene crossover and mutation until the index change is lower than the preset fluctuation threshold for stopping iteration; and outputting the optimized planting layout scheme that conforms to the Pareto optimal solution set.

[0012] Optionally, after outputting the optimized planting layout scheme, the method further includes extracting all plant coordinate points, species list, and suggested initial planting spacing from the optimized planting layout scheme to obtain planting engineering parameters. The planting engineering parameters are then registered with the digital environment base model, and combined with the prediction view generated by the dynamic evolution sequence, a refined planting guideline including a construction layout diagram, a seedling table, and a growth prediction report is generated.

[0013] The second aspect includes a multi-dimensional data construction module for generating the digital environment base model and constructing the native plant characteristic model; a pressure field mapping module for analyzing soundscape evaluation points and generating the spatial sound effect contribution pressure field; a symbiotic simulation engine module for loading the initial planting layout scheme and running the plant growth agent to output the dynamic evolution sequence; an efficiency evaluation module for analyzing the dynamic evolution sequence and generating the sequence acoustic efficiency index, ecological stability index, and landscape robustness index; and a multi-objective optimization module for iterative optimization based on the sequence acoustic efficiency index, ecological stability index, and landscape robustness index, and outputting the optimized planting layout scheme.

[0014] A third party provides an electronic device comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the method and system for optimizing the spatial layout of multi-dimensional soundscape ecological elements as described in the first aspect.

[0015] In one possible design, the electronic device described in the second aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the second aspect and other electronic devices.

[0016] In the embodiments of the present invention, the electronic device described in the second aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0017] Fourthly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer causes the computer to perform the method and system for optimizing the spatial layout of multi-dimensional soundscape ecological elements as described in the first aspect.

[0018] In summary, the above system has the following technical effects: This invention achieves deep coupling calculation of ecological succession process and acoustic physical characteristics by constructing a digital twin environmental base and a bioacoustic feature library of native plants. It accurately simulates the dynamic spatial occupancy state of plant communities from the initial planting stage to maturity. By calculating the physical noise attenuation and bio-sound psychological masking effect at different growth stages, it ensures the continuity and reliability of soundscape optimization effect throughout the entire life cycle. It introduces a multi-agent competitive growth model guided by the spatial sound effect contribution pressure field, so that plant growth decisions are not only constrained by natural resources such as light and nutrients, but also shaped by the inward shaping of global noise reduction objectives. It automatically selects planting structures that can maintain community stability and maximize acoustic barrier function in natural competition. It uses a multi-objective genetic algorithm to intelligently iterate and optimize the initial planting location, plant spacing and species ratio. Within the same digital framework, it coordinates three often conflicting objectives: acoustic gain, ecological diversity and landscape robustness. Through digital dynamic pre-simulation, it significantly reduces the later maintenance risk of ecological landscape projects and provides technical support for quantitative evaluation and automated design of the scientific layout of multi-dimensional soundscape elements in modern cities. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an embodiment of a method and system for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0021] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0022] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0023] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0024] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0025] In the embodiments of this invention, "protocol" may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol in a future optimization method and system for the spatial layout of multi-dimensional soundscape ecological elements. The embodiments of this invention do not specifically limit this.

[0026] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0027] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0028] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0029] To facilitate understanding of the embodiments of this application, Figure 1 The control system shown in the diagram serves as an example to illustrate the communication system applicable to the embodiments of this application. The following details a method and system for optimizing the spatial layout of multi-dimensional soundscape ecological elements, used to execute the method provided in the embodiments of this invention. This method includes acquiring three-dimensional point cloud data of the area to be optimized and generating a digital environmental base model using local environmental parameters; extracting parameters such as growth rate, crown expansion morphology, sound absorption coefficient, and scattering intensity of native plants to construct a native plant feature model; identifying the location information of noise sources and acoustically sensitive points within the area to be optimized to obtain soundscape evaluation points; mapping the soundscape evaluation points to the digital environmental base model for inverse acoustic ray tracing; generating a spatial sound effect contribution pressure field characterizing the noise reduction potential of each spatial point; extracting plant coordinates and species types from the initial planting layout scheme; and then applying this information to the digital environmental base model. A native plant feature model is loaded into the environmental base model to generate a plant growth agent driven by the spatial sound effect contribution pressure field. A goal-oriented symbiotic simulation engine is run to drive the plant growth agent to perceive the score of the spatial sound effect contribution pressure field and adjust its growth direction and leaf cluster density. A dynamic evolution sequence containing the evolution of plant morphology over time is generated. Acoustic loss calculation, biomass change analysis and coverage fluctuation detection are performed on the dynamic evolution sequence to generate sequence acoustic performance index, ecological stability index and landscape robustness index respectively. The sequence acoustic performance index, ecological stability index and landscape robustness index are used as the fitness evaluation basis of the evolutionary algorithm to iteratively optimize the initial planting layout scheme and output the optimized planting layout scheme.

[0030] Optionally, the step of generating a spatial sound effect contribution pressure field characterizing the noise reduction potential of each point in space specifically includes establishing a three-dimensional sound field propagation topology map based on the noise source location information and acoustically sensitive point location information, calculating the proportion of propagation energy of sound waves in each voxel grid in the digital environment substrate model to obtain a path energy density map, and using an energy accumulation algorithm in the calculation process. The specific calculation formula is as follows:

[0031] Indicates the first The spatial acoustic contribution pressure value of a three-dimensional voxel grid represents the potential energy contribution density of that spatial location to blocking noise propagation. This represents the total number of effective sound propagation paths. This indicates the total number of center frequency bands analyzed. Indicates the first The initial acoustic power carried by a sound propagation path, measured in watts. Indicates the first The human hearing correction factor corresponding to each frequency band is called the A-weighting factor, which is a dimensionless value. Indicates the distance from the noise source location to the... The Euclidean distance between the centers of the individual pixel grid, measured in meters, is used to represent the geometric attenuation of sound energy with increasing distance. For Boolean path intersection operators, when the... The propagation path of a bar of sound passes through the first bar in geometric space. The value is 1 when there is a single-cell grid, and 0 otherwise. The average sound absorption efficiency parameters of different height layers in the native plant characteristic model are obtained, and the path energy density map is weighted and corrected using the average sound absorption efficiency parameters to generate the noise reduction sensitivity gradient value of each spatial unit. The noise reduction sensitivity gradient value is then spatially interpolated and normalized to generate a continuously distributed spatial sound effect contribution pressure field.

[0032] Optionally, the steps of driving the plant growth agent to perceive the score of the spatial sound effect contribution pressure field and adjust the growth direction and leaf cluster density specifically include obtaining the survival feedback value by acquiring the coordinates of the plant growth agent and the amount of biological resources acquired in its vicinity, and retrieving the value of the coordinates in the spatial sound effect contribution pressure field in real time to obtain the engineering feedback value. The survival feedback value and the engineering feedback value are weighted and evaluated by a fusion decision algorithm to generate the agent's growth action instructions. The agent's growth action instructions include the branch angle adjustment amount and the lateral branch growth increment. The three-dimensional mesh morphology of the plant growth agent in the digital environment base model is updated according to the agent's growth action instructions to generate a single growth cycle plant morphology snapshot and form a dynamic evolution sequence through continuous iteration of morphology snapshots.

[0033] Optionally, the step of generating agent growth action instructions by weighting the survival feedback value and the engineering feedback value through a fusion decision algorithm specifically includes calculating the spatial overlap between the plant growth agent and neighboring agents to obtain a competition intensity index. The formula for calculating the competition intensity index is as follows:

[0034] In the formula This index represents the intensity of competition currently experienced by the plant's growth agent. Its value is determined by the size of surrounding plants and the distance, reflecting the intensity of competition for biological resources at that location. This represents the total number of neighborhood agents found within the radius. For the first The real-time vertical projection area of ​​the canopy of each neighboring agent, in square meters. This parameter describes the proportion of the upper light-receiving surface occupied by the neighboring plants. For the first The current physical growth height of a neighborhood intelligent agent, in meters. For the target intelligent agent and the first The geometric spacing between neighboring agents, in meters, is used to represent the law that competitive pressure decreases proportionally to the square of the distance. The system uses a preset reference length characteristic value, in meters, which is taken as the standard average height of an adult individual of this species. By introducing this parameter, the unit dimension in the formula is eliminated, making... It becomes a dimensionless numerical value that participates in subsequent logical operations; Based on the competition intensity index, a dynamic weight ratio is obtained by assigning weight coefficients between the survival objective and the engineering objective. The survival feedback value and the engineering feedback value are multiplied by the dynamic weight ratio to generate a comprehensive growth benefit value. All virtual growth paths within a preset angle range are searched and the corresponding predicted benefit value is calculated. The largest of the predicted benefit values ​​is selected as the optimized path direction, and the agent growth action instructions are generated accordingly.

[0035] Optionally, the process involves calculating acoustic loss, analyzing biomass changes, and detecting cover fluctuations in the dynamic evolution sequence to generate sequence acoustic performance indicators, ecological stability indicators, and landscape robustness indicators. Specifically, this includes retrieving the three-dimensional point cloud morphology of the community at different successional stages in the dynamic evolution sequence, simulating sound wave reflection and absorption using the sound absorption coefficient in the native plant characteristic model, obtaining the sound pressure level attenuation components at each successional stage, and cumulatively calculating all sound pressure level attenuation components according to a time-weighted function. The calculation formula is as follows: ;

[0036] In the formula This represents the acoustic performance index of the sequence, and its value serves as the core weighting factor for judging the acoustic merits of the initial planting layout scheme. This represents the total number of time points sampled in the dynamic evolution sequence. Indicates the first The noise reduction value of the community at each sampling node to sensitive points, in decibels. Indicates the first The simulation interval span for each sampling node, in days. The nominal noise reduction performance benchmark value preset for the project, in decibels. The total time period for community simulation evolution is expressed in days. Generate sequential acoustic performance indicators that characterize long-term noise reduction robustness.

[0037] Optionally, the process of generating the sequence acoustic performance index also includes identifying the canopy layering structure and community canopy closure parameters in the three-dimensional point cloud morphology of the community, predicting the call frequency and sound power of the invited organisms through a preset species attraction model to obtain the predicted biological sound spectrum, superimposing the predicted biological sound spectrum onto the residual noise spectrum after sound pressure level attenuation component processing, calculating the psychoacoustic masking value to obtain the sound comfort correction coefficient, and using the sound comfort correction coefficient to perform gain compensation on the sequence acoustic performance index to generate a comprehensive soundscape index that takes into account psychoacoustic gain.

[0038] Optionally, the steps for generating ecological stability indicators and landscape robustness indicators specifically include monitoring the life state parameters of each plant growth agent in the dynamic evolution sequence, statistically analyzing the proportion of surviving plants at the end of succession to obtain a community stability score, generating ecological stability indicators based on the community stability score, measuring the ratio of the community projection area to the total area of ​​the target area at each stage in the dynamic evolution sequence to obtain a dynamic coverage sequence, calculating the variance of the dynamic coverage sequence to obtain a spatial continuity evaluation quantity, taking the reciprocal of the spatial continuity evaluation quantity and mapping it to a standard score range, and generating a landscape robustness indicator characterizing visual morphological stability.

[0039] Optionally, the iterative optimization step of the initial planting layout scheme specifically includes: serializing and encoding the plant coordinates, spacing and species ratio in the initial planting layout scheme to generate an initial population gene; performing multiple rounds of simulated evolution operations on the initial population gene; calculating the non-dominated ranking level of individuals based on the sequence acoustic performance index, ecological stability index and landscape robustness index in each round to obtain the evolutionary selection probability; retaining the best individuals based on the evolutionary selection probability and performing gene crossover and mutation until the change in the index is lower than the preset fluctuation threshold for stopping the iteration; and outputting an optimized planting layout scheme that conforms to the Pareto optimal solution set.

[0040] Optionally, after outputting the optimized planting layout scheme, the method also includes extracting the coordinate points of all plants, species list, and suggested initial planting spacing in the optimized planting layout scheme to obtain planting engineering parameters. The planting engineering parameters are then registered with the digital environment base model and combined with the prediction view generated by the dynamic evolution sequence to generate a refined planting guideline that includes a construction layout diagram, seedling table, and growth prediction report.

[0041] Optionally, it includes a multi-dimensional data construction module for generating a digital environment base model and constructing a native plant characteristic model; a pressure field mapping module for analyzing soundscape evaluation points and generating a pressure field for spatial sound effect contribution; a symbiotic simulation engine module for loading an initial planting layout scheme and running a plant growth agent to output a dynamic evolution sequence; an efficiency evaluation module for analyzing the dynamic evolution sequence and generating sequence acoustic efficiency indicators, ecological stability indicators, and landscape robustness indicators; and a multi-objective optimization module for iterative optimization based on sequence acoustic efficiency indicators, ecological stability indicators, and landscape robustness indicators, and outputting an optimized planting layout scheme.

[0042] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0043] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0044] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0045] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0046] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0047] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0048] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0049] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0050] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0051] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0052] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the spatial layout of multi-dimensional soundscape ecological elements, characterized in that, The method includes: A digital environmental baseline model is generated by acquiring 3D point cloud data of the area to be optimized and local environmental parameters, and a native plant feature model is constructed by extracting parameters such as growth rate, crown expansion morphology, sound absorption coefficient and scattering intensity of native plants. The location information of noise sources and acoustic sensitive points in the area to be optimized are identified to obtain sound scene evaluation points. The sound scene evaluation points are then mapped to the digital environment base model for inverse acoustic ray tracing to generate a spatial sound effect contribution pressure field that characterizes the noise reduction potential of each point in space. Extract the plant coordinates and species types from the initial planting layout scheme, load the native plant feature model into the digital environment base model, and generate a plant growth agent driven by the spatial sound effect contribution pressure field. The goal-oriented symbiotic simulation engine is run to drive the plant growth agent to perceive the score of the spatial sound effect contribution pressure field and adjust the growth direction and leaf cluster density to generate a dynamic evolution sequence containing the evolution of plant morphology over time. Acoustic loss calculation, biomass change analysis, and cover fluctuation detection were performed on the dynamic evolution sequence to generate sequence acoustic performance index, ecological stability index, and landscape robustness index, respectively. The acoustic performance index, ecological stability index, and landscape robustness index of the sequence are used as the fitness evaluation criteria for the evolutionary algorithm to iteratively optimize the initial planting layout scheme and output the optimized planting layout scheme.

2. The method for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to claim 1, characterized in that, The step of generating a spatial sound effect contribution pressure field characterizing the noise reduction potential of each point in space specifically includes: A three-dimensional sound field propagation topology map is established based on the noise source location information and acoustic sensitive point location information. The path energy density map is obtained by calculating the propagation energy ratio of sound waves in each voxel grid in the digital environment substrate model. The average sound absorption efficiency parameters of different height layers in the native plant feature model are obtained, and the path energy density map is weighted and corrected using the average sound absorption efficiency parameters to generate the noise reduction sensitivity gradient value of each spatial unit. Spatial interpolation and normalization are performed on the noise reduction sensitivity gradient value to generate a continuously distributed spatial sound effect contribution pressure field.

3. The method for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to claim 1, characterized in that, The steps of the intelligent agent driving plant growth to perceive the contribution of spatial sound effects to the pressure field and adjust the growth direction and leaf cluster density specifically include: The survival feedback value is obtained by acquiring the coordinates of the plant growth agent and the amount of biological resources acquired in its vicinity, and the engineering feedback value is obtained by retrieving the value of the coordinates in the spatial sound effect contribution pressure field in real time. The survival feedback value and the engineering feedback value are weighted and evaluated by a fusion decision algorithm to generate agent growth action instructions, wherein the agent growth action instructions include branch angle adjustment amount and lateral branch growth increment. The plant growth agent updates the three-dimensional mesh morphology of the digital environment base model according to the growth action instructions of the agent, generates a single growth cycle of plant morphology snapshot, and forms the dynamic evolution sequence by continuously iterating the morphology snapshot.

4. The method for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to claim 1, characterized in that, The step of generating agent growth action instructions by weighting and evaluating survival feedback values ​​and engineering feedback values ​​using a fusion decision algorithm specifically includes: The spatial overlap between the plant growth agent and its neighboring agents is calculated to obtain a competition intensity index, and a dynamic weighting ratio is obtained by allocating weighting coefficients between the survival objective and the engineering objective based on the competition intensity index. The survival feedback value and the engineering feedback value are multiplied by the dynamic weighting ratio to generate a comprehensive growth benefit value. Search for all virtual growth paths within a preset angle range and calculate the corresponding predicted benefit value. Select the largest of the predicted benefit values ​​as the optimized path direction and generate the agent's growth action command accordingly.

5. The method for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to claim 1, characterized in that, The steps of calculating acoustic loss, analyzing biomass changes, and detecting cover fluctuations in the dynamic evolution sequence, and generating sequence acoustic performance indicators, ecological stability indicators, and landscape robustness indicators respectively, specifically include: The three-dimensional point cloud morphology of the community at different succession stages in the dynamic evolution sequence is retrieved, and the sound wave reflection and absorption simulation is performed in combination with the sound absorption coefficient in the native plant characteristic model to obtain the sound pressure level attenuation component of each succession stage. The acoustic performance index characterizing long-term noise reduction robustness is generated by accumulating all the sound pressure level attenuation components according to the time weighting function.

6. The method for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to claim 1, characterized in that, The process of generating the aforementioned sequence acoustic performance index also includes: Identify the canopy layering structure and community canopy closure parameters in the three-dimensional point cloud morphology of the community, and predict the call frequency and sound power of the invited organisms through a preset species attraction model to obtain the predicted biological sound spectrum. The predicted bio-sound spectrum is superimposed onto the residual noise spectrum after the sound pressure level attenuation component is processed, and the psychoacoustic masking value is calculated to obtain the sound comfort correction coefficient. The acoustic comfort correction coefficient is used to compensate for the gain of the sequence acoustic performance index, thereby generating a comprehensive soundscape index that takes into account psychoacoustic gain.

7. The method for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to claim 1, characterized in that, The steps for generating ecological stability indicators and landscape robustness indicators specifically include: The life state parameters of each plant growth agent in the dynamic evolution sequence are monitored, the proportion of surviving plants at the end of succession is counted to obtain the community stability score, and the ecological stability index is generated based on the community stability score. The dynamic coverage sequence is obtained by measuring the ratio of the community projected area to the total area of ​​the target region at each stage in the dynamic evolution sequence, and the variance of the dynamic coverage sequence is calculated to obtain the spatial continuity evaluation quantity. The spatial continuity evaluation quantity is inversely calculated and mapped to a standard score range to generate the landscape robustness index that characterizes visual morphological stability.

8. The method for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to claim 4, characterized in that, The step of iteratively optimizing the initial planting layout scheme specifically includes: The plant coordinates, spacing, and species ratios in the initial planting layout scheme are serialized and encoded to generate the initial population gene. Multiple rounds of simulated evolutionary operations were performed on the genes of the initial population. In each round, the non-dominated ranking level of the individual was calculated based on the sequence acoustic efficacy index, ecological stability index, and landscape robustness index to obtain the evolutionary selection probability. Based on the evolutionary selection probability, the preferred individuals are retained and gene crossover and mutation are performed until the change in the index is lower than the preset fluctuation threshold for stopping iteration, and the optimized planting layout scheme that conforms to the Pareto optimal solution set is output.

9. The method for optimizing the spatial layout of multi-dimensional soundscape ecological elements according to claim 3, characterized in that, After outputting the optimized planting layout plan, it also includes: The planting engineering parameters are obtained by extracting the coordinate points of all plants, species list, and suggested initial planting spacing in the optimized planting layout scheme. The planting engineering parameters are coordinate-registered with the digital environment base model, and combined with the prediction view generated by the dynamic evolution sequence, a refined planting guideline including a construction layout diagram, a seedling table, and a growth prediction report is generated.

10. An optimization system for the spatial layout of multi-dimensional soundscape ecological elements, applied to the optimization method for the spatial layout of multi-dimensional soundscape ecological elements as described in any one of claims 1-9, characterized in that, include: A multidimensional data construction module is used to generate the digital environment base model and construct the native plant feature model. The pressure field mapping module is used to analyze the soundscape evaluation points and generate the spatial sound effect contribution pressure field. The symbiotic simulation engine module is used to load the initial planting layout scheme, run the plant growth agent, and output the dynamic evolution sequence; The performance evaluation module is used to analyze the dynamic evolution sequence and generate the acoustic performance index, ecological stability index and landscape robustness index of the sequence; The multi-objective optimization module is used to iteratively optimize the planting layout based on the sequence acoustic performance index, ecological stability index and landscape robustness index, and output the optimized planting layout scheme.

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