Park green land optimization method based on plant community color quantization and emotion recovery influence

Abstract

The invention discloses a park green space optimization method based on plant community color quantification and emotion recovery influence, which comprises the following steps: collecting typical urban park plant community images through field investigation, and objectively quantifying plant colors by using image processing software; in combination with a psychological experiment normal form, after standardized pressure stimulation is applied to a subject in a laboratory environment, color combination plant community images with different proportions are presented; subjective psychological scale data and objective physiological electroencephalogram indexes are synchronously collected, the influence of different color composition proportions on the emotion recovery effect is obtained, and an optimal plant community configuration scheme is determined. The method fuses subjective and objective data, is high in quantifiability and operability, fills up the research blank of group scale plant community color quantization and an emotional response mechanism, can provide reference for plant community configuration design, is beneficial for realizing scientific planning of restorative landscapes such as park green land, and has a wide application prospect. And the design quality of the park green land plant landscape and the functionality of the park green land plant landscape are improved.

Description

Technical Field

[0001] This invention relates to the field of landscape design technology, and more specifically to a method for optimizing park green spaces based on the quantification of plant community color and the influence of emotional restoration, applicable to the scientific planning of restorative landscapes such as urban parks and community green spaces. Background Technology

[0002] Currently, with the rapid urbanization of modern society, the pace of urban life and the increasing pressure of social competition are constantly squeezing natural environmental space, giving rise to numerous urban problems. How to alleviate stress and promote emotional recovery has become a key global concern. A growing body of research has confirmed that contact with the natural environment can effectively relieve stress and has a positive effect on emotional recovery and physical and mental health. The colors of plants in parks and green spaces, as the most direct and experiential factor for people in the natural environment, play an important role in regulating emotions and promoting emotional recovery.

[0003] Against this backdrop, how to scientifically quantify the color characteristics of plant communities in urban parks and, using a combination of subjective and objective methods, deeply explore the differences in the impact of different plant community color combinations on the emotional recovery of people has become a hot topic and a challenge in constructing restorative landscapes in urban parks. With the advancement of modern technology and color psychology, scientifically utilizing plant community color combinations to construct restorative urban natural environments and exploring ways that benefit public well-being and the sustainable development of urban landscapes are key issues that urgently need to be addressed in current urban landscape design practice.

[0004] However, existing research focuses on qualitative descriptions or color analysis at a single scale (such as flower color or forest as a whole), lacking systematic quantitative research on the relationship between the proportion of colors (such as red, yellow, and green) and the emotional recovery effect at the plant community scale.

[0005] Furthermore, traditional emotion assessments primarily rely on subjective questionnaires (such as the PANAS scale), which suffer from significant individual bias and insufficient objectivity; while purely physiological indicators (such as electroencephalograms) are difficult to directly correlate with landscape design parameters. Therefore, there is an urgent need for a technical solution that integrates subjective and objective data, is quantifiable, highly operable, and can provide a scientific basis for optimizing the design of park green space plant landscapes, in order to improve the quality and functionality of park green space plant landscapes. Summary of the Invention

[0006] In view of this, the present invention provides a park green space optimization method based on the quantification of plant community color and the influence of emotional restoration, which can provide a reference for the design of plant community configuration, help to realize the scientific planning of restorative landscapes such as parks and green spaces, and improve the design quality and functionality of park green space plant landscapes.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for optimizing park green spaces based on the quantification of plant community color and its impact on mood restoration. This method includes the following steps: S1: Acquire landscape images of different plant communities in the park green space; S2: Based on the landscape image, perform color segmentation and quantization to construct a visual stimulus image set containing different color combinations and proportions; S3: Calculate the experimental sample effect size. In a laboratory environment, present the subjects with images from the set of visual stimuli and simultaneously collect their EEG signals and emotional data. S4: Preprocess the EEG signals to obtain clean and analyzable EEG signal data; and calculate the emotional change value of the test subject based on the emotional data; S5: Perform correlation and statistical analysis between the EEG signal data and the emotion change values, compare the differences in emotion recovery under different color combinations and proportions, determine the optimal plant community configuration scheme, and optimize the park green space design.

[0008] In a preferred embodiment, in step S2, the color combination includes a single green, a red-green combination, and a yellow-green combination.

[0009] In a preferred embodiment, in step S3, G*Power software is used to calculate the experimental sample effect size.

[0010] In a preferred embodiment, in step S3, images from the visual stimulus image set are presented using E-prime software.

[0011] In a preferred embodiment, in step S3, brainwave signals are acquired using a Neuroscan brainwave acquisition system; wherein: The Neuroscan EEG acquisition system includes: a Quick-Cap electrode cap for accurately acquiring scalp surface potential signals; a SynAmps2 amplifier for signal filtering and amplification; and Curry analysis software for storing, processing, and visualizing the acquired EEG data.

[0012] In a preferred embodiment, in step S3, the PANAS scale and the SD semantic scale are used to obtain the subject's emotional data, wherein: The PANAS scale includes multiple words representing positive emotions and multiple words representing negative emotions, while the SD semantic scale includes multiple pairs of adjectives with opposite meanings.

[0013] In a preferred embodiment, in step S3, the experimental process involves multiple rounds of repeated experimental steps, each round consisting of a baseline phase, a stress phase, and a post-test phase. During the post-test phase of each round, plant community images are randomly played using E-prime software.

[0014] In a preferred embodiment, step S4 involves preprocessing the EEG signal, including: file format conversion, signal segmentation, filtering, spatial decomposition, removal of eye movement artifacts, time alignment, and power spectrum conversion.

[0015] In a preferred embodiment, in step S4, based on the emotion data, positive and negative emotion values ​​are calculated using the Likert scale; and based on the positive and negative emotion values, the emotion change value of the test subject is calculated using IBM SPSSStatistics software.

[0016] Secondly, the present invention also provides a park green space optimization system based on the quantification of plant community color and the influence of emotional recovery. The system includes: an image acquisition module, a visual stimulus image set construction module, an EEG signal and emotion data acquisition module, and a data processing and analysis module. When applied, the above-mentioned park green space optimization method based on the quantification of plant community color and the influence of emotional recovery is executed to achieve park green space optimization.

[0017] As can be seen from the above technical solution, the present invention provides a method for optimizing park green spaces based on the quantification of plant community color and the influence of mood restoration. Compared with the prior art, the present invention has at least the following beneficial effects: 1. This invention provides a method for optimizing park green spaces based on the quantification of plant community color and the influence of emotional restoration. It can provide a reference for the design of plant community configuration, help to realize the scientific planning of restorative landscapes such as parks and green spaces, and improve the design quality and functionality of park green space plant landscapes.

[0018] 2. This invention improves the analysis of plant community color perspectives in restorative environments, providing a scientific reference for optimizing the effect of plant color on restorative environments in urban parks. This invention scientifically quantifies and analyzes the color of plant communities in urban parks, exploring its impact on emotional restoration through quantitative analysis. This provides empirical support for the theory of restorative environments and helps to construct a more scientific theoretical framework for restorative environment design.

[0019] 3. This invention expands the application scope of color psychology in landscape design, providing a new perspective for the cross-integration of color psychology with environmental psychology and landscape ecology. Meanwhile, current research on the physiological and psychological effects of plant community color lacks depth and breadth, and studies on the physiological and psychological impacts of different color proportions within plant communities are scarce. Therefore, integrating color psychology theory with park plant configuration to alleviate fatigue and enhance attention, thereby contributing to more positive emotions, can provide new ideas for the evaluation and design of plant community color landscapes. Furthermore, the conclusions drawn from different color combinations in plant community landscapes have practical significance in providing a scientific reference for the proportion of plant community color combinations in different functional zones of parks in the future.

[0020] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0024] Figure 1 This is a flowchart illustrating a park green space optimization method based on the quantification of plant community color and the influence of mood restoration, provided as an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the workflow architecture of the present invention provided for an embodiment of the present invention.

[0026] Figure 3 A schematic diagram illustrating the preparation of experimental instruments provided in the embodiments of the present invention.

[0027] Figure 4 This is a schematic diagram of the EEG experimental environment provided in an embodiment of the present invention.

[0028] Figure 5This is an experimental schematic diagram illustrating the effect of red-green plant communities on mood recovery, provided in an embodiment of the present invention.

[0029] Figure 6 This is an experimental schematic diagram illustrating the effect of a yellow-green plant community on mood recovery, provided in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0031] In the description of this invention, it should be noted that some processes described in this application specification and drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may be performed in any order or in parallel. Furthermore, various numbers are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0032] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0033] See Figure 1 and Figure 2 As shown, this embodiment of the invention provides a method for optimizing park green spaces based on the quantification of plant community color and its impact on mood restoration, mainly including the following steps: S1: Acquire landscape images of different plant communities in the park green space; In this invention, sample plots were selected in typical parks in Suzhou (such as Tiger Hill Wetland Park), and representative plant community photos were taken in spring and autumn (the colors mainly include green, red and yellow).

[0034] S2: Based on landscape images, perform color segmentation and quantization to construct a set of visual stimulus images containing different color combinations and proportions; Specifically, RGB / HSV values ​​can be extracted using image processing software, the pixel proportion of each primary color (green, red, yellow) in the image can be calculated, and multiple sets of ratio gradients can be constructed (such as green:red = 100:0, 75:25, 50:50, 25:75, 10:90, etc.).

[0035] S3: Calculate the experimental sample effect size. In a laboratory environment, present the subjects with images from the set of visual stimuli and simultaneously collect their EEG signals and emotional data.

[0036] S4: Preprocess the EEG signals to obtain clean and analyzable EEG signal data; and calculate the emotional change value of the test subject based on the emotion data.

[0037] S5: Correlate and statistically analyze EEG signal data with emotion change values, compare the differences in emotion recovery under different color combinations and proportions, determine the optimal plant community configuration scheme, and use it to optimize park green space design.

[0038] The technical means employed in the specific implementation of this invention mainly include: Literature review method: Literature review, through the systematic collection, identification, organization, and analysis of existing literature, forms a scientific understanding of a specific field or problem. In its initial stage, this invention, based on the influence of plant community color on emotional recovery, searched multiple literature databases for relevant keywords such as "plant color," "visual perception," "emotional recovery," and "color quantification." By systematically collecting and organizing relevant domestic and international literature in recent years, the aim was to gain a deeper understanding of the development trends and cutting-edge developments in related topics both domestically and internationally. This allowed for the summarization of theories related to plant community color and emotional recovery, drawing on the experience of previous research and identifying the advantages and disadvantages of current research on the effect of plant community color on emotional recovery, thus providing a solid theoretical foundation and reference for this invention.

[0039] Field research method: The field research method requires researchers to go to the site in person and systematically collect first-hand raw data on a specific research subject through various means such as observation, interviews, and seminars. By conducting a comprehensive and in-depth analysis of the research subject, the aim is to obtain accurate and timely information, thereby forming a comprehensive and in-depth scientific understanding of the subject. This invention involves conducting field investigations and photographing plant community images in several well-known and scenic urban parks in Suzhou. The color composition elements of the images are analyzed, statistically classified, and screened. Finally, based on the selected images, suitable plant community combinations are determined, and an experimental plan and procedure are developed.

[0040] Statistical analysis method: Statistical analysis is a systematic research method that reveals the development patterns and future trends of things by collecting, organizing, deeply analyzing, and interpreting data. This method uses mathematical and probability principles to establish mathematical models, systematically process and deeply analyze large amounts of data, and draw quantitative conclusions. The psychological and physiological data collected in the experiment were analyzed using SPSS Statistics 26.0. The experimental data underwent normality and homogeneity of variance tests. After confirming normality and homogeneity of variance, repeated measures ANOVA was used to analyze differences in emotional recovery in psychological and EEG (electroencephalography) data. The effects and differences in emotional recovery of participants were compared between plant communities with different proportions of the same color. Paired-samples t-tests were used to analyze the effects and differences in emotional recovery of participants between plant communities with different proportions of the same color. p A value < 0.05 is considered statistically significant.

[0041] The following example, using a city park during spring and autumn, demonstrates the principles and specific implementation of the method of this invention through a concrete experiment: 1. Recruitment of Experiment Personnel: The experimental sample effect size was calculated using G*Power 3.1 software, with the probability of Type I error (αerror prob) set to 0.05, the power (Power, i.e., 1-β error probability) to 0.8, and the effect size (f) to 0.25. The final calculated sample size was 24, therefore, each experimental group had at least 24 participants. This invention compares two experimental groups, recruiting 26 participants in each group, for a total of 52 university students aged 18-25, with undergraduate or graduate degrees and no restrictions on major. All participants were right-handed, had no color blindness, no history of neurological or psychiatric illness, and no history of brain injury. To avoid other psychological factors affecting EEG data collection, participants were required to ensure sufficient sleep the day before the experiment and abstain from consuming caffeine and other stimulating beverages to avoid potential stimulation of the nervous system by caffeine and other components. Fasting and stimulating exercise were also avoided before the experiment to minimize potential impact on the experimental data. In the final data processing stage, three participants were excluded due to abnormal data recordings caused by problems with the EEG equipment, and one participant was excluded due to incomplete questionnaire entries. Ultimately, there were 24 valid data points per group, totaling 48 data points. The average age was 21 years, with 23 males and 25 females, a male-to-female ratio of approximately 1:1.

[0042] 2. Preparation of experimental equipment: In the visual stimulus preparation stage, the visual stimuli were presented using E-prime software. E-prime, due to its timeliness, accuracy, and ease of use, has become a widely recognized and commonly used professional software for designing psychological experimental procedures. Furthermore, E-prime allows for the recording and transmission of experimental behaviors through keyboard and mouse operations. Figure 3 and Figure 4 As shown, the acquisition of EEG signals was accomplished using the Neuroscan EEG acquisition system, which mainly includes: a Quick-Cap electrode cap for precise acquisition of scalp surface potential signals; a SynAmps2 amplifier for signal filtering and amplification; and Curry analysis software for storing, processing, and visualizing the acquired EEG data. Because EEG signals are weak, they are easily interfered with in emotion-inducing experiments, requiring high-precision amplifiers to process the raw signals for clear detection. Therefore, amplifiers have become indispensable experimental instruments in the process of acquiring EEG signals. The 64-channel Quick-Cap Neo Net electrode cap used with the SynAmps2 / RT and Neuvo amplifiers is a high-density array configuration EEG acquisition device. It contains four integrated bipolar leads, arranged according to an extended 10 / 20 system design, and can simultaneously acquire multimodal physiological signals such as vertical electrooculography (VEOG), horizontal electrooculography (HEOG), electrocardiography (ECG), and electromyography (EMG). During the experiment, it needs to be directly connected to the SynAmps2 / RT and Neuvo headgear. In EEG data acquisition, to achieve good conductivity and low contact impedance, a conductive medium is typically used to fill the gap between the electrodes and the scalp. Conductive gel and saline are commonly used conductive media; however, compared to saline, conductive gel establishes a more stable connection between the electrodes and the scalp, reducing artifacts caused by movement and the skin surface. Therefore, this experiment chose conductive gel as the conductive medium between the subject's head and the EEG cap, and connected it during the experimental preparation phase.

[0043] 3. Experimental Procedure Design: See Figure 5 and Figure 6 As shown, two sets of experiments were conducted in this embodiment of the invention. The first set was an experiment on the effect of red-green plant communities on human emotional recovery; the second set was an experiment on the effect of yellow-green plant communities on human emotional recovery. Both experiments were conducted in a closed EEG laboratory, with the temperature maintained between 20-24°C and the humidity kept at around 60%. Each subject underwent the experiment under the same environmental conditions. During the experiment, the EEG laboratory was kept closed and quiet, and all lights were turned off to prevent the subjects from being disturbed by external noise and light.

[0044] In the two sets of experiments, the first set of experimental materials consisted of a single green plant community and three sets of red-green plant communities with different proportions, while the second set of experimental materials consisted of a single green plant community and three sets of yellow-green plant communities with different proportions. To prevent other interference factors, the experimental procedures for both sets of experiments were the same.

[0045] (1) Experimental Preparation Stage: Before the experiment, both groups of subjects were informed of the experimental procedures and requirements and signed informed consent forms. Before the experiment began, subjects were required to maintain a stable sitting posture and avoid shaking their heads or swallowing saliva to prevent affecting the acquisition of EEG signals. Subsequently, the experimenters fitted the subjects with appropriate EEG caps and applied EEG ointment between the cap and the scalp. Then, they checked the connection between each electrode of the EEG cap and the amplifier, ensuring that the impedance of each electrode point was below 5KΩ. After confirming that the EEG cap was connected correctly, the subjects were asked to calm down. Once ready, the E-prime and Curry programs were run, and the experimental recording began.

[0046] (2) Experimental Procedure: Each experiment consisted of four repeated rounds of experimental procedures. Each round consisted of a baseline phase, a stress phase, and a post-test phase. During the post-test phase of each round, four sets of plant community images were randomly played in sequence using E-prime to avoid the experimental order affecting subsequent data results. The baseline phase, stress phase, and post-test phase each lasted 3 minutes, and the participants completed the questionnaire for 2 minutes. Therefore, each participant needed 15 minutes per round, and the four rounds took a total of 60 minutes.

[0047] Baseline phase: After the formal experiment begins, participants are first asked to calm down for three minutes, and then fill out the PANAS scale for the baseline phase.

[0048] Stress Phase: Both experimental groups used mathematical calculations and rapid countdown sounds to induce high levels of stress in the participants. Four sets of mathematical problems were designed and used in each round of stress stimulation: "1022 minus 13", "765 minus 16", "1103 minus 17", and "979 minus 14". Participants were required to mentally calculate these mathematical problems during the three-minute stress phase and answer correctly within two seconds; otherwise, they had to recalculate and fill out the PANAS scale for the stress phase.

[0049] Post-test phase: During the three-minute post-test phase in both groups, participants viewed a randomly selected set of plant community color matching images, and EEG data were collected during this phase. Researchers left the laboratory during image stimulation, leaving only the participants in a quiet and enclosed EEG lab to prevent other factors from interfering with EEG data collection. The PANAS and SD semantic scales for the post-test phase were then completed.

[0050] (3) End of Experiment: After the four rounds of experiments for each group are completed, the experimenters will stop the program and check whether the data has been collected completely. The questionnaire data from the four rounds of experiments will be collected and integrated, and the EEG data will be checked and saved. Then, the subjects will be helped to remove the EEG caps and all equipment in the laboratory will be turned off. Finally, the EEG caps will be taken to the experimental preparation room for cleaning and drying.

[0051] After the two sets of experiments were completed, the data of 52 subjects were collected and further statistical analysis was performed.

[0052] 4. Data Processing and Analysis: ① Collection of psychological indicator data: Psychological index data were collected by gathering basic information from the participants through questionnaires, and then measured using psychological scales. In this experiment, the PANAS and SD semantic scales were used to measure psychological data.

[0053] The PANAS scale consists of 10 words representing positive emotions and 10 words representing negative emotions. It uses a Likert scale (1-5 points) to calculate positive emotions (PA score) and negative emotions (NA score). This scale has been widely used in psychological measurements in many emotional recovery experiments. A higher PA score and a lower NA score indicate better emotional recovery.

[0054] The SD Semantic Scale is considered a reliable and effective method for quantifying subjective emotions evoked by external stimuli. It can be used to quantify the emotional impressions evoked by different plant colors. This invention uses 14 pairs of adjectives with opposite meanings for participants to rate according to the degree of their emotional state. The Likert scale (1-5 points) is used to calculate the emotional value, where a positive adjective in a pair is worth 5 points (most positive), and a negative adjective is worth 1 point (most negative). Higher scores in each pair of adjectives indicate a stronger inclination towards positive descriptions, while lower scores indicate a stronger inclination towards negative descriptions. A higher total score across the 14 pairs of adjectives indicates a more positive emotion, and vice versa.

[0055] ② Physiological indicator data analysis: Measuring brain activity is an objective way to assess a person's physiological perception of a landscape or environment. Electroencephalogram (EEG) signals are categorized into five wave types based on frequency characteristics: delta waves (0.5-3Hz), theta waves (4-7Hz), alpha waves (8-13Hz), beta waves (14-30Hz), and gamma waves (>30Hz). Alpha, beta, delta, and theta waves are commonly used frequencies in landscape perception experiments. Alpha waves are typically associated with relaxation, alertness, and focus; this wavelength can be observed when the brain is alert and relaxed. Beta waves are the fastest and most active short-wavelength EEG waves, usually associated with tension, attention, and focus. Delta, theta, and gamma waves are less frequently detected when the brain is alert. Therefore, in the collection and analysis of physiological index data, the data is analyzed by collecting alpha wave data and beta / alpha values ​​when subjects viewed plant images of different colors. Increased alpha waves indicate greater relaxation, while decreased alpha waves indicate greater tension; increased beta waves indicate greater focus, while decreased beta waves indicate greater relaxation. The β / α value is used to describe the effect of stress on the nervous system. A higher β / α value indicates a higher level of stress or focus, while a lower β / α value indicates a lower level of stress or focus.

[0056] In this invention, the acquisition of EEG signals is accomplished using the Neuroscan EEG acquisition system, which mainly includes: Quick-Cap electrode caps, SynAmps2 amplifiers, and Curry analysis software. Other analysis and recording software includes E-prime (EEG experimental recording software), MATLAB R2022b (for analyzing EEG waves and creating brain topography), Excel, and IBM SPSS Statistics 26.0 (for EEG data correlation analysis). Because EEG signals are sensitive, they are easily affected by external environmental factors or related physiological signals during acquisition. Therefore, before analyzing the EEG signals, the raw EEG signals need to be preprocessed to remove interference signals caused by eye movement artifacts, noise, etc., to obtain clean and analyzable EEG signal data. This process includes the following: (1) The collected EEG signals were converted to a different file format using the eeglab plugin toolkit in MATLAB R2022b software, and then the raw EEG signal data were processed using the Fieldtrip plugin. (2) After importing the EEG data of a single subject, roughly locate the experimental process, such as dividing the EEG signal into 29000ms segments (100ms to 28000ms before stimulation); according to the settings of E-prime software, mark the playback position of plant community photos at different stages, and segment the EEG signal according to the marked points in the subsequent analysis. (3) Perform the experimental definition process according to the settings in (2). In order to ensure that the data runs normally, the time of the EEG signal segment should not be too short. Confirm an output variable for the defined experiment, and then save the step and data. (4) Perform pre-processing and re-referencing of EEG signals and select appropriate experimental and electrode channels. In the channel screening stage, all channels are filtered from 1 to 40 Hz, and then baseline correction from -1 to 0 Hz is performed. After removing linear trends, this step and the data are saved. (5) Run Independent Component Analysis (ICA) to spatially decompose the raw EEG data using the ICA algorithm to obtain a set of mutually independent signal components; then manually review and identify and label those components containing obvious motion artifacts, system spikes, or muscle activity artifacts; while retaining basic eye movement artifact components (such as mild blink artifacts), remove all labeled artifact components; finally, recombine the data of components that clearly correspond to blinking and saccades; finally, review the data again to check and manually remove channels and experimental segments with obvious residual artifacts, and save this step and the data. (6) Perform the condition mean process, lock the single experiments (Trials) belonging to the same experimental conditions according to the stimulus presentation time (Stimulus Onset), align them uniformly to the zero time reference point (t=0), and save this step and data; (7) Finally, perform frequency analysis, select 1-40Hz for power spectrum conversion, use Morlet wavelet to calculate TFRs, and save this step and data.

[0057] After analyzing all the subject data according to the above steps, the EEG data from the red-green and yellow-green plant communities were then subjected to group analysis. All obtained data were analyzed from the frequency distribution and the average value was calculated. Then, based on the analyzed data, available channels were selected, and EPR and topological maps of each plant community in the EEG experiment were drawn. TF images of the sample plots were also drawn. Finally, the data were statistically analyzed.

[0058] ③Statistics and Analysis: IBM SPSS Statistics was used to process the PA and NA values ​​of the PANAS scale. The formulas for calculating the emotional changes during the stress and recovery phases of the PANAS scale are as follows: Emotional change value during stress phase (△P1): △P1=P 压力 -P基线 ; Emotional change during recovery phase (ΔP2): ΔP2 = P 后测 -P 基线 .

[0059] The formulas for positive and negative emotion changes are as follows: Positive sentiment change value (△P) PA ): △P PA =(△P 1-PA -△P 2-PA ) / P 压力-PA , Negative emotion change value (△P) NA ): △P NA =(△P 1-NA -△P 2-NA ) / P 压力-NA .

[0060] In the formula, P represents the mean sentiment. 基线 P represents the baseline sentiment mean. 压力 P represents the average mood during the stress phase. 后测 ΔP represents the mean emotional values ​​during the recovery phase (post-test phase). A positive ΔP indicates that the mean emotional values ​​at the baseline stage are greater than those during the stress or recovery phases; conversely, a negative ΔP indicates that the mean emotional vocabulary after recovery is greater than the mean before recovery. PA The value of positive emotion change, △P NA This represents the change in negative emotions. △P 1-PA △P represents the change in positive emotions during the stress phase. 2-PA P represents the value of positive emotional change during the recovery phase. 压力-PA △P represents the mean of positive emotions during the stress phase. 1-NA ΔP represents the change in negative emotions during the stress phase. 2-NA P represents the change in negative emotions during the recovery phase. 压力-NA This represents the average negative emotions during periods of stress.

[0061] The psychological and physiological data collected in the experiment were analyzed using IBM SPSS Statistics 26.0. Normality and homogeneity of variance tests were performed on the experimental data. After confirming normality and homogeneity of variance, repeated measures ANOVA was used to analyze differences in emotional recovery in psychological scales and EEG data. The effects of different proportions of plant communities with the same color on the participants' emotional recovery and emotional differences were compared. Paired-samples t-tests were used to compare stress stages (P < 0.05). 压力 ) and post-test phase (P 后测The PA and NA scores were used to examine the differences in the participants' emotional changes during the experimental phase. Repeated measures ANOVA was then used to compare the differences in emotional recovery on the PANAS scale and the differences in 14 emotional words and total SD scores on the SD semantic scale, comparing the effects of different color combinations of plant communities on the participants' emotional recovery and emotional differences. p A value <0.05 is considered statistically significant.

[0062] Finally, data visualization was performed using Origin 2021, and the following conclusions were drawn: Compared to red-green combinations and single-green plant communities, yellow-green plant communities are more effective in promoting mood recovery, with the best recovery effect achieved when colored tree species account for 75%-90% of the visual environment. In the visual perception of a landscape, green can bring a sense of calm and relaxation, which is particularly important in the early stages of mood recovery and enhancement, while later, red and yellow dominant colors have a positive effect on mood. These conclusions have certain reference value for urban park planning and plant community design. In the future planning of urban park plant communities, flexible color matching of plant communities should be adopted according to the actual needs of the population and site conditions, comprehensively considering the restorative properties of plant colors and the functional uses of different sites.

[0063] As described in the above embodiments, those skilled in the art will understand that the present invention proposes a method for optimizing park green spaces based on the quantification of plant community color and the influence of mood restoration. Its innovation can be summarized in two main aspects: (1) Innovative perspective: Providing new ideas for color configuration of plant communities for mood restoration in urban parks: While existing research on the impact of the natural environment on human emotional recovery widely acknowledges its significant influence on physical and mental well-being, and many studies have confirmed the effects of different plant colors on this process, current research largely focuses on small-scale plant flower colors, individual plant colors, or large-scale forest colors. Research on color quantification and emotional recovery at the community level is relatively limited, and further consideration of the relationship between color composition and richness is needed. Furthermore, in community-scale studies, most color quantification studies compare plants of different colors, consistently presenting the impact of one or more color combinations on emotions. Although some research indicates that different green visibility ratios affect emotional recovery, studies specifically addressing different color combination ratios are scarce. Therefore, this invention innovatively focuses on the impact of different color combination ratios within plant communities on human emotional recovery, which is particularly important in the construction of restorative plant color landscapes. Based on this, this invention fills a gap in the field of plant community color research and obtains empirical data on the restorative characteristics of plant communities through color quantification, providing a solid scientific basis for future plant community color landscape design.

[0064] (2) Innovative approach: It expands the research methodology for quantifying the impact of plant color on human emotions. While research on the quantification of plant color is increasingly prevalent, a unified methodology has yet to emerge. Furthermore, previous studies on emotional recovery largely employed subjective evaluation methods, leading to inherent subjectivity in the results. In recent years, many perceptual studies have adopted a combined subjective and objective approach to provide emotional feedback. This invention quantifies the color of plant communities in urban parks using software measurement methods and combines this with a subjective-objective approach using psychological questionnaires and electroencephalogram (EEG) measurements. This allows for the assessment of the varying impacts of different color combinations of plant communities on emotional recovery in urban parks. This provides a new method for understanding the influence of plant community color quantification on human emotions and enriches research on the relationship between plant color quantification and emotional recovery, offering scientific guidance for future restorative plant color landscapes in urban parks.

[0065] Furthermore, this embodiment of the invention also provides a park green space optimization system based on the influence of plant community color quantification and emotional recovery. The system includes: an image acquisition module, a visual stimulus image set construction module, an EEG signal and emotion data acquisition module, and a data processing and analysis module. When applied, it executes the above-mentioned park green space optimization method based on the influence of plant community color quantification and emotional recovery to achieve park green space optimization.

[0066] The system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the aforementioned method embodiment, and will not be repeated here.

[0067] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, devices, or computer program products, etc. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0068] It should be noted that the word "comprising" does not exclude the presence of components or steps not listed in the claims. The words "a" or "an" preceding a component do not exclude the presence of a plurality of such components. This invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer.

[0069] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0070] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. 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.

Claims

1. A park green space optimization method based on plant community color quantification and emotional recovery influence, characterized in that, The method comprises the following steps: S1: acquiring landscape images of different plant communities in a park green space; S2: color segmentation and quantization based on the landscape images to construct a visual stimulus image set containing different color combinations and proportions; S3: calculating the experimental sample effect size, presenting the images in the visual stimulus image set to the subjects in a laboratory environment, and synchronously collecting their electroencephalogram signals and emotional data; S4: preprocessing the electroencephalogram signals to obtain clean and analyzable electroencephalogram signal data, and calculating based on the emotional data to obtain the emotional change value of the testee; S5: correlating and statistically analyzing the electroencephalogram signal data and the emotional change value, comparing the emotional recovery differences under different color combinations and proportions, and determining the optimal plant community configuration scheme.

2. The park green space optimization method based on plant community color quantification and emotional recovery influence according to claim 1, characterized in that, In S2, the color combinations include single green, red-green combination, and yellow-green combination.

3. The park green space optimization method based on plant community color quantification and emotional recovery influence according to claim 1, characterized in that, In S3, the G*Power software is used to calculate the experimental sample effect size.

4. The park green space optimization method based on plant community color quantification and emotional recovery influence according to claim 1, characterized in that, In S3, the E-prime software is used to present the images in the visual stimulus image set.

5. The park green space optimization method based on plant community color quantification and emotional recovery influence according to claim 1, characterized in that, In S3, the electroencephalogram signals are collected by the Neuroscan electroencephalogram acquisition system; wherein: The Neuroscan electroencephalogram acquisition system comprises a Quick-Cap electrode cap for accurately collecting scalp surface potential signals, a SynAmps2 amplifier for signal filtering and amplification processing, and a Curry analysis software for storing, processing, and visualizing the collected electroencephalogram data.

6. The park green space optimization method based on plant community color quantification and emotional recovery influence according to claim 1, characterized in that, In S3, the PANAS scale and the SD semantic scale are used to obtain the emotional data of the subjects, wherein: The PANAS scale includes a plurality of positive emotion words and a plurality of negative emotion words, and the SD semantic scale includes a plurality of pairs of opposite meaning adjectives.

7. The park green space optimization method based on plant community color quantification and emotional recovery influence according to claim 1, characterized in that, In S3, during the experiment, a plurality of repeated experimental steps are performed, each round consisting of a baseline stage, a stress stage, and a post-test stage, and in the post-test stage of each round, the plant community images are randomly played by the E-prime software.

8. The park green space optimization method based on plant community color quantification and emotional recovery influence according to claim 1, characterized in that, In S4, the preprocessing of the electroencephalogram signals includes file format conversion, signal segmentation, filtering, spatial decomposition, eye movement artifact removal, time alignment, and power spectrum conversion.

9. The park green space optimization method based on plant community color quantification and emotional recovery influence according to claim 1, characterized in that, In S4, based on the emotional data, the positive and negative emotion values are calculated by the Likert scale scoring method; and the emotional change value of the testee is obtained based on the positive and negative emotion values.

10. A park green space optimization system based on plant community color quantification and emotional recovery influence, characterized in that, The system comprises an image acquisition module, a visual stimulus image set construction module, an electroencephalogram signal and emotional data acquisition module, a data processing and analysis module, and when applied, performs a park green space optimization method based on plant community color quantization and emotional recovery influence according to any one of claims 1 to 9, realizing park green space optimization.

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