A trap structure optimization method and system based on visual smoke simulation

Abstract

This invention discloses a method and system for optimizing the structure of a pheromone trap based on visualized smoke simulation, belonging to the field of pheromone trap structure optimization technology. The method includes: constructing a controllable environmental space and arranging multiple traps with different structures; placing a tracer at the lure core of the trap to release smoke; acquiring smoke diffusion videos of each trap under different environmental conditions based on the target area; obtaining the horizontal diffusion distance, vertical descent distance, and updraft angle of the smoke diffusion as key diffusion parameters based on the smoke diffusion videos; selecting a target trap based on the key diffusion parameters of all traps under the same environmental conditions; and adjusting the structure of the target trap and combining it with the corresponding key diffusion parameters to obtain an optimized trap. The method visualizes and simulates the pheromone diffusion process, thereby accurately optimizing the key structural parameters of the trap.

Description

Technical Field

[0001] This invention relates to the field of trap structure optimization technology, and more specifically to a trap structure optimization method and system based on visual smoke simulation. Background Technology

[0002] Currently, the efficiency of sex pheromone traps depends on whether the pheromones they release can form a stable, directional "odor plume" in the field to guide pests to fly in that direction. The pheromone molecules themselves are invisible, and their diffusion is subject to complex influences from the trap's structure (such as shape and opening) and the environment (temperature, wind speed).

[0003] However, traditional optimization methods mainly rely on costly and time-consuming field "trial and error" experiments, which cannot intuitively observe and quantitatively analyze key diffusion parameters of pheromones around the trap, such as horizontal range, vertical settling, and rising angle, resulting in blind and inefficient design improvements.

[0004] Therefore, how to visualize and simulate pheromone diffusion, and then accurately optimize the key structural parameters of the trap, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method and system for optimizing the structure of a trap based on visual smoke simulation to overcome or at least partially solve the above problems. The method visualizes and simulates the pheromone diffusion process, thereby accurately optimizing the key structural parameters of the trap and overcoming the shortcomings of traditional design optimization, such as reliance on experience, long cycle and high cost.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a method for optimizing the structure of a trap based on visual smoke simulation, comprising: Construct an environmental space with controllable environmental conditions and deploy multiple traps with different structures; A tracer is placed at the lure core of the trap, and released to generate smoke; Based on the target area, acquire smoke diffusion videos of each of the traps under different environmental conditions; Based on the smoke diffusion video, the horizontal diffusion distance, vertical sinking distance, and updraft deflection angle of the smoke diffusion are obtained as key diffusion parameters. Based on the key diffusion parameters of all the traps under the same environmental conditions, the target trap is selected. Based on the structural adjustments made to the target decoy, and combined with the corresponding key diffusion parameters, an optimized decoy with improved structure is obtained.

[0008] In one embodiment, constructing an environmental space with controllable environmental conditions specifically includes: Construct an environmental chamber or laboratory with adjustable wind speed, temperature, and humidity as the environmental space; Multiple traps with different structures are placed in the environmental space; An adjustable-speed fan is installed on the side of the trap, and a wind speed detection device is used to calibrate the trap at the inlet and outlet positions to establish the required wind speed gradient. An electric heating device and a temperature measuring device are installed below the trap to control the temperature of the trap base area.

[0009] In one embodiment, releasing the smoke-generating substance specifically includes: A quantitative amount of analytical grade solution is dropped onto the release carrier, and the release carrier is fixedly placed at the lure core position of the trap for release, generating smoke to simulate the pheromone diffusion process; The method for obtaining the smoke diffusion video is as follows: Against a black light-absorbing cloth background, using a graduated ruler as a spatial reference, a camera is used to capture the dynamic diffusion of smoke from each of the traps under different combinations of temperature and wind speed, resulting in the smoke diffusion video.

[0010] In one embodiment, the key diffusion parameters are obtained as follows: Based on the smoke diffusion video, video segments within a preset time period after the smoke release contour reaches the maximum stable range are selected as keyframes. A pixel-to-actual-size conversion scale is established based on the known length of the graduated ruler in the keyframe as a reference. Based on the smoke state in the keyframe and the conversion scale, the horizontal diffusion distance, the vertical sinking distance, and the updraft deflection angle are obtained as the key diffusion parameters.

[0011] In one embodiment, the horizontal diffusion distance is the horizontal distance between the outermost edge of the smoke profile and the vertical projection of the decoy. The horizontal diffusion distance is obtained by calculating the absolute value of the horizontal coordinate of all smoke outline pixels with the projection point of the decoy as the first origin and dividing it by the conversion scale.

[0012] In one embodiment, the vertical sinking distance is the vertical sinking amount of the smoke body relative to the lure; The vertical sinking distance is obtained by obtaining the centroid ordinate of the binarized smoke region, subtracting it from the ordinate of the first origin, and then dividing by the conversion scale.

[0013] In one embodiment, the upward airflow deflection angle is the angle between the upward direction of the smoke side, with the maximum horizontal diffusion distance as the second origin and the inducing core as one side; The method to obtain it is as follows: In the keyframe, the decoy is marked as the release point, the smoke outline is identified, and the boundary point with the largest horizontal diffusion distance is determined as the vertex. A connection is obtained by linking the release point with the vertex; At the vertex, a tangent is drawn along the local direction of the smoke profile, so that it is tangent to the profile, thus obtaining the tangent line; The angle between the connecting line and the tangent is obtained as the upward airflow deflection angle.

[0014] In one embodiment, the method for obtaining the target trap is as follows: The key diffusion parameters of the traps with different structures were quantitatively compared under the same temperature, wind speed and equal amount of tracer conditions. The traps corresponding to the largest horizontal diffusion distance, the increase in vertical sinking distance greater than a first threshold, and the updraft deflection angle greater than a second threshold are selected as the target traps.

[0015] In one embodiment, the optimized trap acquisition method is as follows: By adjusting one or more structural parameters of the target trap, a corresponding adjusted trap is obtained; Based on the adjustment of the trap, the corresponding key diffusion parameters are obtained; Repeat the above process until the optimal key diffusion parameters corresponding to the maximized horizontal diffusion distance, the vertical sinking distance greater than the first set value, and the updraft deflection angle greater than the second set value are obtained, and the adjusted trap is used as the optimized trap.

[0016] In a second aspect, embodiments of the present invention provide a trap structure optimization system based on visual smoke simulation, used to execute a trap structure optimization method based on visual smoke simulation as described in any of the first aspects, comprising: an environment space construction module, a smoke simulation output module, a smoke video acquisition module, a key parameter acquisition module, a trap initial selection module, and a structure optimization output module; The environmental space construction module is used to construct an environmental space with controllable environmental conditions and to arrange multiple traps with different structures. The smoke simulation output module is used to place a tracer at the lure core position of the trap and release it to generate smoke. The smoke video acquisition module is used to acquire smoke diffusion videos of each of the traps under different environmental conditions based on the target area; The key parameter acquisition module is used to acquire the horizontal diffusion distance, vertical sinking distance and updraft deflection angle of the smoke diffusion as key diffusion parameters based on the smoke diffusion video. The trap selection module is used to select a target trap based on the key diffusion parameters of all the traps under the same environmental conditions. The structure optimization output module is used to adjust the structure of the target decoy based on the target decoy and, in combination with the corresponding key diffusion parameters, to obtain an optimized decoy with an optimized structure.

[0017] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a method and system for optimizing the structure of a trap based on visual smoke simulation, which has the following beneficial effects: 1. Visualization and Quantification: This invention transforms the invisible pheromone diffusion process into a visible, recordable, and precisely measurable physical process, providing unprecedented intuitive insights and quantitative basis for trap design.

[0018] 2. High efficiency and low cost: This invention is simulated in a controlled laboratory environment, which greatly shortens the optimization cycle and significantly reduces the time and material costs required for traditional large-scale field trials.

[0019] 3. Scientifically Guided Design: This invention clarifies three core evaluation indicators: "horizontal diffusion distance", "vertical sinking distance" and "rising airflow deflection angle", and establishes their direct correlation with the key structure of the trap (top opening, overall shape), enabling design optimization to move from experience to science.

[0020] 4. Environmental adaptability optimization: By simulating different combinations of temperature and wind speed, this invention can perform "customized" structural optimization for specific target field microclimates (such as high temperature and calm wind in summer), which significantly improves the environmental adaptability and stability of the trap.

[0021] 5. High applicability: The principle of the method of this invention is clear and applicable not only to rice stem borer traps, but also to the design and optimization of other pest traps that rely on wind-borne scents for attraction. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1This is a flowchart of a trap structure optimization method based on visual smoke simulation provided in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of odor simulation diffusion index measurement provided in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram showing the vertical sinking distance of different types of traps when the bottom temperature of the trap is 25°C, as provided in this embodiment of the invention.

[0026] Figure 4 This is a schematic diagram showing the horizontal diffusion distance of different types of traps when the bottom temperature of the trap is 25°C, as provided in this embodiment of the invention.

[0027] Figure 5 This is a schematic diagram showing the upward airflow deflection angle of different types of traps when the bottom temperature of the trap is 25°C, as provided in this embodiment of the invention.

[0028] Figure 6 This is a schematic diagram showing the vertical sinking distance of different types of traps when the bottom temperature of the trap is 40°C, as provided in this embodiment of the invention.

[0029] Figure 7 This is a schematic diagram showing the horizontal diffusion distance of different types of traps when the bottom temperature of the trap is 40°C, as provided in this embodiment of the invention.

[0030] Figure 8 This is a schematic diagram showing the upward airflow deflection angle of different types of traps when the bottom temperature of the trap is 40°C, as provided in this embodiment of the invention.

[0031] Figure 9 This is a schematic diagram showing the comparison of the number of moths attracted by different traps at different time periods in 2024, provided in an embodiment of the present invention.

[0032] Figure 10 This is a bar chart showing the relationship between the number of openings at the top of the trap and the number of moths attracted, as provided in this embodiment of the invention.

[0033] Figure 11 This is a dot plot showing the relationship between the number of openings at the top of the trap and the number of moths attracted, provided in an embodiment of the present invention.

[0034] Figure 12 This is a schematic diagram showing the comparison of the number of moths trapped by different types of traps at different time periods in 2025, provided in an embodiment of the present invention. Detailed Implementation

[0035] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1 like Figure 1 As shown, this embodiment of the invention discloses a method for optimizing the structure of a trap based on visual smoke simulation, including the following steps. For ease of description, these steps are numbered S1 to S6, and these numbers are not intended to limit the sequential relationship between the various steps of this invention: S1 constructs an environmental space with controllable environmental conditions and arranges multiple traps with different structures.

[0037] Furthermore, constructing an environmental space with controllable environmental conditions specifically includes: Construct environmental chambers or laboratories with adjustable wind speed, temperature, and humidity as environmental spaces; Multiple traps with different structures are placed in the environmental space; An adjustable-speed fan is installed on the side of the trap, and a wind speed detection device is used to calibrate the trap at the inlet and outlet positions to establish the required wind speed gradient. An electric heating device and a temperature measuring device are installed below the trap to control the temperature of the trap base area, for example, by setting it to two treatment levels: 40°C and 25°C.

[0038] Furthermore, in this embodiment, the wind speed detection device is an anemometer, model Testo 405-V1, and the established wind speed gradients are: 0.0m / s, 0.8m / s, 1.5m / s; In this embodiment, the electric heating device is an AUX NSC-200-13A1 radiator.

[0039] Furthermore, in this embodiment, the temperature gradient set in the ambient space includes at least one normal temperature condition (25℃) and one high temperature condition (35-40℃), and the wind speed gradient set includes at least one windless (0 m / s) and low wind speed (0.5-1.5 m / s) condition.

[0040] S2 places a tracer at the lure position of the trap, which releases to generate smoke.

[0041] Furthermore, the release of smoke includes: A quantitative amount of analytical grade solution is dropped onto the release carrier, which is then fixed at the lure core of the trap to generate smoke, which is used to simulate the pheromone diffusion process.

[0042] Furthermore, in this embodiment, 3 mL of analytical grade titanium tetrachloride (TiCl4) solution was added dropwise onto a standard medical cotton swab as a release carrier.

[0043] S3 acquires smoke diffusion videos of each trap under different environmental conditions based on the target area.

[0044] Furthermore, the method for obtaining the smoke diffusion video is as follows: Against a black light-absorbing cloth background, using a graduated ruler as a spatial reference, a camera was used to capture the dynamic diffusion of smoke from each trap under different combinations of temperature and wind speed, resulting in a video of the smoke diffusion.

[0045] S4 uses the smoke diffusion video to obtain the horizontal diffusion distance, vertical sinking distance, and updraft deflection angle of the smoke as key diffusion parameters.

[0046] Furthermore, the method for obtaining key diffusion parameters is as follows: Based on the smoke diffusion video, the video segment within a preset time period after the smoke release outline reaches the maximum stable range is selected as the key frame. A pixel-to-actual-size conversion scale is established based on a graduated ruler of known length in the keyframe as a reference. Based on the smoke state and conversion scale in the keyframes, the horizontal diffusion distance, vertical sinking distance, and updraft deflection angle are obtained as key diffusion parameters.

[0047] Furthermore, in this embodiment, the video segment within 30 seconds after the smoke is released and enters the stable diffusion stage, i.e., after the outline reaches the maximum stable range, is selected as the key frame based on the smoke diffusion video.

[0048] Furthermore, in this embodiment, image analysis software (such as Motic Plus Images 2.0) is used to quantize the video frames and extract the horizontal diffusion distance A, the vertical descent distance B, and the updraft deflection angle C.

[0049] Furthermore, the horizontal diffusion distance A is the horizontal distance between the outermost edge of the smoke profile and the vertical projection of the lure core; By taking the projection point of the decoy as the first origin, the absolute value of the horizontal coordinate of all smoke outline pixels is calculated and divided by the conversion scale to obtain the horizontal diffusion distance A.

[0050] Furthermore, the vertical sinking distance B is the vertical sinking amount of the smoke body relative to the lure. The vertical sinking distance B is obtained by subtracting the ordinate of the centroid of the binarized smoke region from the ordinate of the first origin and dividing by the conversion scale. A positive difference indicates sinking.

[0051] Furthermore, the updraft deflection angle C is the angle between the rising direction of the smoke side, with the maximum horizontal diffusion distance as the second origin and the lure as one side; The method to obtain it is as follows: Mark the decoy as release point O in the keyframe, identify the smoke outline and determine the boundary point with the largest horizontal diffusion distance as vertex P; Based on the connection between release point O and vertex P, the connection OP is obtained; At vertex P, draw a tangent line PT along the local direction of the smoke profile, so that it is tangent to the profile, thus obtaining the tangent line; Obtain the angle between the connecting line OP and the tangent line PT, and use it as the updraft deflection angle C.

[0052] Furthermore, the average value was taken multiple times for each keyframe, and the experiment was repeated multiple times under each combination of conditions. The final result was expressed as mean ± standard deviation.

[0053] Furthermore, in this embodiment, each keyframe is measured three times and the average value is taken; each temperature and wind speed combination is repeated five times to ensure data robustness.

[0054] S5 selects the target trap based on the key diffusion parameters of all traps under the same environmental conditions.

[0055] Furthermore, the method for obtaining the target decoy is as follows: Quantitatively compare the key diffusion parameters of traps with different structures under the same temperature, wind speed and amount of tracer; The decoys with the largest horizontal diffusion distance, an increase in vertical descent distance greater than the first threshold, and an updraft deflection angle greater than the second threshold are selected as target decoys.

[0056] Furthermore, a decoy structure that maximizes the horizontal diffusion distance A, increases the vertical sinking distance B, and forms an upward airflow deflection angle C that is conducive to horizontal diffusion is selected as the target decoy.

[0057] S6 adjusts the structure of the target decoy and combines it with the corresponding key diffusion parameters to obtain an optimized decoy with improved structure.

[0058] Furthermore, the method for obtaining the trap is optimized as follows: Adjust one or more structural parameters of the target decoy to obtain the corresponding adjusted decoy; Based on adjusting the trap, the corresponding key diffusion parameters are obtained; Repeat the above process until the optimal key diffusion parameters corresponding to the maximum horizontal diffusion distance, a vertical sinking distance greater than the first set value, and an updraft deflection angle greater than the second set value are obtained, and the adjusted trap is used as the optimized trap.

[0059] Furthermore, in this embodiment, the adjusted structural parameters include: the number and area of ​​top openings, the inclination of sidewalls, and the internal flow guiding structure; Structural optimizations include reducing or sealing the number of ventilation holes on the top of the trap, and / or adopting a trapezoidal cross-section structure that is narrower at the top and wider at the bottom to obtain a better smoke diffusion pattern under high temperature and low wind speed conditions.

[0060] Furthermore, in this embodiment, the upward airflow deflection angle is greater than 90°, and the larger the angle, the stronger the directional effect of the horizontal diffusion of the airflow.

[0061] Furthermore, the optimized trap has the best lateral diffusion area, and the optimized trap was finally verified in the field to verify its trapping effect.

[0062] Example 2 To verify the effectiveness of the method of the present invention, an example of a rice stem borer trap is provided.

[0063] In this embodiment, a traditional cylindrical trap (Type A) and an improved trapezoidal trap (Type B) are selected as comparative prototypes.

[0064] Inside the environmental chamber, conditions were set at 40℃ and a wind speed of 0.8 m / s. Cotton swabs soaked in 3 mL of TiCl4 were placed at the lure core of each trap, releasing smoke and recording the results. Image analysis showed that after sealing the 22 openings at the top of the type B trap, the horizontal smoke diffusion distance reached 67.0 ± 5.0 cm, which was 1.54 times that of the type A trap under the same treatment; simultaneously, the vertical smoke deposition distance was also superior.

[0065] Based on the simulation results above, it can be determined that "reducing the top opening" and "adopting a trapezoidal structure" are the key to the optimized design. Based on this principle, an optimized trap was made. In the field trial in July-August of the same year (high temperature and calm wind), the optimized trap captured 6.9-7.5 times more male rice stem borers than the traditional design, which fully verified the effectiveness of the method in guiding optimization.

[0066] The Influence of Aerosol Diffusion from Different Traps Airflow control system: An adjustable speed fan is installed on the side of the trap. An anemometer (Testo 405-V1, accuracy ±0.1m / s) is used to calibrate at the insect inlet of the trap to establish three wind speed gradients: 0.0m / s (control), 0.8m / s, and 1.5m / s. Temperature control system: An electric radiator (AUX NSC-200-13A1, 2000W) is placed below the trap and precisely controlled by a digital display temperature controller (accuracy ±0.5℃), with two temperature settings: 40℃ (high temperature) and 25℃ (normal temperature). Atomization observation system: Two types of traps are each equipped with two treatments: ① top opening sealed (the top hole is completely sealed with 3M transparent tape), ② no sealing (the top opening is left open).

[0067] 3 mL of titanium tetrachloride solution (analytical grade) was quantitatively added to a standard medical cotton swab and fixed at the decanter position. After the aerosol diffusion stabilized, the diffusion dynamics of each treatment combination (temperature × wind speed × sealing treatment) were recorded using a camera against a black light-absorbing cloth background, with a graduated ruler as a spatial reference. The video data were quantified using Motic Plus Images 2.0 image analysis software. Each treatment combination was repeated 5 times. The experiment was conducted in a constant temperature and humidity laboratory (temperature 25±1℃, relative humidity 60±5%).

[0068] Comparative test of traps In September 2024, the study was conducted in a late-season rice paddy in a certain city. After assembling the traps, bait cores were placed inside and the traps were marked. Type A and Type B traps were placed alternately in the rice paddy along the farm road. The inlet height of all traps was adjusted to 1.2 m, and the spacing between traps was 25 m. The number of rice stem borers in each trap was recorded daily at 24:00 and 6:00, for a total of 5 repetitions. The number of rice stem borers in the traps placed in the field and the number of openings at the top of the traps were also recorded.

[0069] The experiment was conducted in farmland in a city of a province from April 26 to June 6, 2025, during which the average monthly temperature was approximately 19–20℃ and the wind speed was generally greater than level 3. Subsequently, it was conducted in paddy fields in the same city from July 4 to September 25, 2025. During this period, the average temperature in July and August was approximately 31℃ and the wind speed was generally less than level 3. From late August to September, the average temperature was approximately 28℃ and the wind speed was generally less than level 3.

[0070] Three types of traps were used in the experiment: the old-style trap, the new integrated trap, and the new split trap. The lure used was consistent across all traps. At each experimental site, each type of trap was repeated five times, with treatments alternating. The inlet height of all traps was uniformly set at 0.8 meters, and the distance between adjacent traps was no less than 30 meters to minimize mutual interference. During the experiment, each trap was checked every morning to count the number of male rice stem borers captured, and the traps were emptied promptly.

[0071] Relationship between the number of top seals and the number of moths trapped The experiment was conducted in August 2024 in a single-season rice field in a city of a province. Type B traps were used, with the number of openings at the top randomly set. All traps were uniformly suspended 1.2 m above the rice plants, with an interval of at least 30 m. One month later, the number of openings at the top of the traps and the number of rice stem borers trapped were investigated.

[0072] Data Analysis Data analysis was performed using SPSS 17.0. One-way ANOVA was used to compare means among multiple groups, and Duncan's method was used for significance analysis; Student's t-test was used to compare means between two groups. In significance analysis, P < 0.05 was considered statistically significant. Pearson correlation coefficient was used in bivariate correlation analysis to analyze the correlation between the number of moths trapped and the number of openings at the top; the significance of the correlation was analyzed using a two-tailed t-test. Linear regression analysis was used for regression analysis.

[0073] result Odor diffusion like Figure 2 As shown, the measured indicators in the experiment include: ① Maximum horizontal diffusion distance A (cm), defined as the maximum horizontal distance between the edge of the mist and the center of the trap; ② Vertical sinking distance B (cm), which records the vertical displacement of the mist body relative to the sinking of the decoy core; ③ Upward airflow deflection angle C (°), which measures the deflection angle of the mist's upward trajectory with the vertical direction as the reference.

[0074] The effects of temperature and wind speed on the diffusion of odor molecules The temperature at the bottom of the trap is 25℃, and the wind speed is 0m / s. F =0.918, df =39, P =0.442), 0.8m / s ( F =1.404, df =39, P =0.257), 1.5m / s ( F =1.307, df =39, P =0.287), such as Figure 3 As shown, there was no significant difference in the sinking distance of titanium tetrachloride aerosol from different types of traps.

[0075] Different types of traps have no significant effect on the lateral distance of aerosol movement. F =1.088, df =39, P =0.366); when the wind speed is 0.8 m / s, the lateral movement distance of the mist from different types of traps varies significantly ( F =17.789, df =39, P <0.001), among which the B-type trap has the largest lateral movement distance after being capped, with an average of 67.0±5.0cm, which is 1.54 times that of the A-type capped trap.

[0076] like Figure 4 As shown, when the wind speed is 1.5 m / s, the lateral movement distance of the mist from different types of traps varies significantly. F =38.726, df =39, P <0.001), the maximum lateral movement distance of the unsealed B-type trap is 83.5±5.5cm, while the lateral movement distance of the sealed B-type trap is 1.32 times that of the sealed A-type trap.

[0077] like Figure 5 As shown, the updraft deflection angle is at a wind speed of 0 m / s ( F =0.245, df =39, P =0.864), 0.8m / s ( F =2.536, df =39, P =0.072), 1.5m / s ( F =1.704, df =39, P When the value was 0.183, there was no significant difference between different types of traps.

[0078] like Figure 6 As shown, when the bottom temperature of the trap is 40℃ and the wind speed is 0 m / s, the sinking distance of different types of traps varies significantly. F =4.409, df =39, P =0.010), among which the B-type trap had the highest sinking distance at the top, 29.1±3.2cm, which was 1.34 times that of the A-type trap; at a wind speed of 0.8m / s, there were significant differences in the sinking distances of different types of traps ( F =4.175, df =39, P =0.012), among which the B-type trap had the largest sinking distance of mist after being capped, which was 31.6±3.2cm, 1.32 times that of the A-type trap after being capped; when the wind speed was 1.5m / s, there were significant differences in the sinking distance between the different types of traps ( F =3.804, df =39, P =0.018).

[0079] like Figure 7 As shown, when the ambient temperature is 40℃ and the wind speed is set to 0m / s, different types of traps have no significant effect on the lateral distance of the mist movement. F =1.088, df =39, P=0.366); when the wind speed is 0.8 m / s, the lateral movement distance of the mist from different types of traps varies significantly ( F =17.789, df =39, P <0.001), among which the type B trap had the largest lateral movement distance after being capped, averaging 67.0±5.0 cm, which was 1.54 times that of the type A trap. When the wind speed was 1.5 m / s, there were significant differences in the lateral movement distance of the mist from different types of traps ( F =38.726, df =39, P <0.001), the maximum lateral movement distance of the unsealed Type B trap is 83.5±5.5cm, while the lateral movement distance of the sealed Type B trap is 1.32 times that of the sealed Type A trap.

[0080] like Figure 8 As shown, the deflection angle of the rising mist airflow varies significantly among different traps at a wind speed of 0 m / s. F =2.759, df =39, P =0.046), the maximum angle when the Type A trap is capped is 110.7°±4.0°. When the wind speed is 0.8m / s, there are significant differences in the rising angle of the mist among different traps. F =4.509, df =39, P =0.009), the maximum angle when the B-type trap is capped is 111.6°±4.5°; when the wind speed is 1.5m / s, there are significant differences in the rising angle of the mist among different traps. F =4.691, df =39, P =0.007), the maximum angle of the B-type trap is 123.0°±5.2°.

[0081] The structural design of the trap and the number of moths attracted like Figure 9 As shown, A: September 10-15, 2024, (third generation) Province A, City A; B: September 16-20, 2024, (third generation) Province A, City A; C: May 16-20, 2025, (overwintering generation) Province A, City B.

[0082] Experimental site in City A: From September 10th to September 15th, the nighttime temperatures were consistently high, and the moth-attracting rate of type A traps was significantly lower than that of type B. t =-3.148, df =8, P =0.014), but there was no significant difference between the two after the temperature dropped in the second half of the night ( t =0.194, df =8, P =0.851). Temperatures will drop from September 16th to September 19th, with the first half of the night's temperature similar to the second half. (The first half of the night...) t =-0.939, df =8, P =0.375) and in the latter half of the night (t=0.907, df=8, P=0.391), there was no significant difference in the number of moths attracted by type A and type B traps. At the test site in city b: From May 16th to 20th, the number of moths attracted by the three types of traps was significantly lower between 18:00 and 24:00 ( F =0.639, df =59, P =0.531) and 24:00-06:00 ( F =0.641, df =59, P There were no significant differences (e.g., 0.530).

[0083] like Figure 10 As shown, by statistically analyzing the number of rice stem borers in traps with different numbers of openings in the field, it was found that the number of moths trapped by traps with 1-8 openings was significantly higher than that trapped by traps with 9-19 openings at the top. t =2.405, df =85, P =0.018), the average number of moths attracted when the number of openings is less than 8 is 1.3 times that when the number of openings is greater than 8.

[0084] like Figure 11 As shown, a Pearson correlation analysis was performed on the number of openings and the number of moths trapped. The Pearson correlation coefficient was -0.283, indicating a weak correlation between the number of openings and the number of moths trapped. P =0.008). Linear regression analysis was performed on the number of holes and the number of moths trapped. The regression equation between the number of moths trapped and the number of holes was y = -1.2167x + 44.07, and the correlation coefficient R was 0.008. 2 =0.0802 (P < 0.0001).

[0085] like Figure 12 As shown, A: April 26 to June 6, 2025, City B, Province A; B: July 4 to August 21, 2025, (Third Generation) City A, Province A; C: August 22 to September 25, 2025, (Fourth Generation) City A, Province A.

[0086] At the test site in City B, there was no significant difference in the number of moths captured by the three types of traps. F =0.334, df =14, P=0.722), the old version of the trap captured 254.2±31.5 moths, the new integrated trap captured 253.0±32.1 moths, and the new split trap captured the highest number of moths, 291.4±47.5 moths.

[0087] At the test site in City A, there were significant differences in the number of moths attracted by the three types of traps during July and August. F = 20.182, df = 14, P <0.001). Among them, the old version of the trap had the lowest trapping rate, only 11.8 ± 3.9 moths; the new integrated trapping rate was 82.0 ± 11.0 moths, about 6.9 times that of the old version; while the new split trapping rate was the highest, reaching 88.0 ± 11.5 moths, about 7.5 times that of the old version.

[0088] However, from August 22nd to September 25th, there was no significant difference in the number of moths captured by the three types of traps at the test site in City A. F =0.862, df =14, P =0.447), the old version of the trap captured 22.2±9.3 moths, the new integrated trap captured up to 44.0±21.3 moths, and the new split trap captured 20.6±7.5 moths.

[0089] Example 3 Based on the same inventive concept, this invention also provides a trap structure optimization system based on visual smoke simulation, including: an environmental space construction module, a smoke simulation output module, a smoke video acquisition module, a key parameter acquisition module, a trap initial selection module, and a structure optimization output module; The environmental space construction module is used to construct an environmental space with controllable environmental conditions and to arrange multiple traps with different structures. The smoke simulation output module is used to release smoke by placing a tracer at the lure core position of the trap. The smoke video acquisition module is used to acquire smoke diffusion videos of each trap under different environmental conditions based on the target area; The key parameter acquisition module is used to obtain the horizontal diffusion distance, vertical sinking distance, and updraft deflection angle of the smoke diffusion based on the smoke diffusion video as key diffusion parameters. The initial selection module for the trap is used to select the target trap based on the key diffusion parameters of all traps under the same environmental conditions. The structure optimization output module is used to adjust the structure of the target decoy and, in conjunction with the corresponding key diffusion parameters, obtain the optimized decoy after structural optimization.

[0090] Furthermore, in this embodiment, the functional implementation methods of each functional module correspond one-to-one with the methods described above, and will not be repeated here.

[0091] Example 4 Based on the same inventive concept, the present invention also provides an electronic device, which includes a processor and a memory. The memory stores instructions, which are loaded and executed by the processor to implement a trap structure optimization method based on visual smoke simulation as described in Embodiment 1.

[0092] Based on the same inventive concept, the present invention also provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes a program stored in the memory, it can implement a trap structure optimization method based on visual smoke simulation, as shown in Example 1.

[0093] The electronic device may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can call logical instructions in the memory to execute a trap structure optimization method based on visual smoke simulation as described in Embodiment 1.

[0094] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present 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 of the various embodiments of the present 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.

[0095] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0096] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the structure of a trap based on visual smoke simulation, characterized in that, include: Construct an environmental space with controllable environmental conditions and deploy multiple traps with different structures; A tracer is placed at the lure core of the trap, and released to generate smoke; Based on the target area, acquire smoke diffusion videos of each of the traps under different environmental conditions; Based on the smoke diffusion video, the horizontal diffusion distance, vertical sinking distance, and updraft deflection angle of the smoke diffusion are obtained as key diffusion parameters. Based on the key diffusion parameters of all the traps under the same environmental conditions, the target trap is selected. Based on the structural adjustments made to the target decoy, and combined with the corresponding key diffusion parameters, an optimized decoy with improved structure is obtained.

2. The trap structure optimization method based on visual smoke simulation as described in claim 1, characterized in that, Constructing a controllable environmental space specifically includes: Construct an environmental chamber or laboratory with adjustable wind speed, temperature, and humidity as the environmental space; Multiple traps with different structures are placed in the environmental space; An adjustable-speed fan is installed on the side of the trap, and a wind speed detection device is used to calibrate the trap at the inlet and outlet positions to establish the required wind speed gradient. An electric heating device and a temperature measuring device are installed below the trap to control the temperature of the trap base area.

3. The method for optimizing the structure of a trap based on visual smoke simulation as described in claim 2, characterized in that, The release produces smoke, specifically including: A quantitative amount of analytical grade solution is dropped onto the release carrier, and the release carrier is fixedly placed at the lure core position of the trap for release, generating smoke to simulate the pheromone diffusion process; The method for obtaining the smoke diffusion video is as follows: Against a black light-absorbing cloth background, using a graduated ruler as a spatial reference, a camera is used to capture the dynamic diffusion of smoke from each of the traps under different combinations of temperature and wind speed, resulting in the smoke diffusion video.

4. The trap structure optimization method based on visual smoke simulation as described in claim 3, characterized in that, The key diffusion parameters are obtained as follows: Based on the smoke diffusion video, video segments within a preset time period after the smoke release contour reaches the maximum stable range are selected as keyframes. A pixel-to-actual-size conversion scale is established based on the known length of the graduated ruler in the keyframe as a reference. Based on the smoke state in the keyframe and the conversion scale, the horizontal diffusion distance, the vertical sinking distance, and the updraft deflection angle are obtained as the key diffusion parameters.

5. The trap structure optimization method based on visual smoke simulation as described in claim 4, characterized in that, The horizontal diffusion distance is the horizontal distance between the outermost edge of the smoke profile and the vertical projection of the lure core. The horizontal diffusion distance is obtained by calculating the absolute value of the horizontal coordinate of all smoke outline pixels with the projection point of the decoy as the first origin and dividing it by the conversion scale.

6. The trap structure optimization method based on visual smoke simulation as described in claim 5, characterized in that, The vertical sinking distance is the vertical sinking amount of the smoke body relative to the lure core; The vertical sinking distance is obtained by obtaining the centroid ordinate of the binarized smoke region, subtracting it from the ordinate of the first origin, and then dividing by the conversion scale.

7. The trap structure optimization method based on visual smoke simulation as described in claim 6, characterized in that, The upward airflow deflection angle is the angle between the upward direction of the smoke side, with the maximum horizontal diffusion distance as the second origin and the inducing core as one side; The method to obtain it is as follows: In the keyframe, the decoy is marked as the release point, the smoke outline is identified, and the boundary point with the largest horizontal diffusion distance is determined as the vertex. A connection is obtained by linking the release point with the vertex; At the vertex, a tangent is drawn along the local direction of the smoke profile, so that it is tangent to the profile, thus obtaining the tangent line; The angle between the connecting line and the tangent is obtained as the upward airflow deflection angle.

8. The trap structure optimization method based on visual smoke simulation as described in claim 7, characterized in that, The method for obtaining the target decoy is as follows: The key diffusion parameters of the traps with different structures were quantitatively compared under the same temperature, wind speed and equal amount of tracer conditions. The traps corresponding to the largest horizontal diffusion distance, the increase in vertical sinking distance greater than a first threshold, and the updraft deflection angle greater than a second threshold are selected as the target traps.

9. The method for optimizing the structure of a trap based on visual smoke simulation as described in claim 8, characterized in that, The method for obtaining the optimized trap is as follows: By adjusting one or more structural parameters of the target trap, a corresponding adjusted trap is obtained; Based on the adjustment of the trap, the corresponding key diffusion parameters are obtained; Repeat the above process until the optimal key diffusion parameters corresponding to the maximized horizontal diffusion distance, the vertical sinking distance greater than the first set value, and the updraft deflection angle greater than the second set value are obtained, and the adjusted trap is used as the optimized trap.

10. A trap structure optimization system based on visual smoke simulation, used to execute the trap structure optimization method based on visual smoke simulation as described in any one of claims 1-9, characterized in that, include: The module includes an environmental space construction module, a smoke simulation output module, a smoke video acquisition module, a key parameter acquisition module, a trap initial selection module, and a structure optimization output module. The environmental space construction module is used to construct an environmental space with controllable environmental conditions and to arrange multiple traps with different structures. The smoke simulation output module is used to place a tracer at the lure core position of the trap and release it to generate smoke. The smoke video acquisition module is used to acquire smoke diffusion videos of each of the traps under different environmental conditions based on the target area; The key parameter acquisition module is used to acquire the horizontal diffusion distance, vertical sinking distance and updraft deflection angle of the smoke diffusion as key diffusion parameters based on the smoke diffusion video. The trap selection module is used to select a target trap based on the key diffusion parameters of all the traps under the same environmental conditions. The structure optimization output module is used to adjust the structure of the target decoy based on the target decoy and, in combination with the corresponding key diffusion parameters, to obtain an optimized decoy with an optimized structure.

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