Urban suburban park symbiosis analysis system

Abstract

The invention relates to the technical field of park information processing, in particular to an urban suburban park symbiosis analysis system, which comprises an analogue simulation module, an area identification module, an image analysis module, a symbiosis analysis module and a symbiosis analysis module, the tourist gathering area can be accurately determined, the local analysis module performs difference processing on the target maintenance image and the standard vegetation image of the gathering area to obtain a vegetation damage value, then the reference maintenance duration is determined, and the area maintenance module performs fine maintenance on the vegetation of the gathering area according to the reference maintenance duration. Therefore, the park management department can master the activity dynamic of tourists in time, early warning is carried out on the ecological damage risk possibly occurring in the tourist concentration area in advance, and meanwhile the pertinence and effectiveness of maintenance work are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of park information processing, and particularly to a symbiotic analysis system for urban suburban parks. Background Art

[0002] With the acceleration of the urbanization process, as an important part of the urban ecosystem, the balance between ecological protection and tourist viewing experience in urban suburban parks has become increasingly important.

[0003] However, in the actual operation process, it is impossible to comprehensively and in real time grasp the activity trajectories and gathering areas of tourists in the park. This makes it difficult for park management departments to predict the damage that the concentrated activities of tourists may cause to ecological resources such as vegetation. Remedial measures are often taken only after the vegetation is severely damaged, which greatly affects the stability and beauty of the park ecosystem. On the other hand, the maintenance work in the park lacks pertinence, and unified maintenance standards and cycles are mostly adopted, which not only causes waste of maintenance resources but also cannot meet the differentiated maintenance needs of vegetation in different areas due to different tourist activity intensities. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems in the background art, and a symbiotic analysis system for urban suburban parks is proposed.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions: A symbiotic analysis system for urban suburban parks, comprising: A simulation module, configured to obtain real-time monitoring images and perform simulation in the park simulation model with the monitoring image positions and the positions of mass points; An area recognition module, configured to collect the mass point distribution images in the park simulation model; An image analysis module, configured to divide the mass point distribution images into multiple unit distribution images, then identify the number of mass points in the unit distribution images to determine the concentrated images; Identify the target analysis areas corresponding to the concentrated images, obtain all the unit distribution images in the target analysis areas, calculate the set coefficients of the target analysis areas according to the number of scattered points in the unit distribution images, and determine the gathering areas by the set coefficients; A local analysis module, configured to collect the target maintenance images of the gathering areas, perform difference processing on the target maintenance images and the standard vegetation images to obtain the vegetation damage values, and determine the benchmark maintenance duration of the gathering areas based on the vegetation damage values; An area maintenance module, configured to perform fine maintenance on the vegetation in the gathering areas according to the benchmark maintenance duration of the gathering areas.

[0006] As a further solution of the present invention, the method of simulation includes: According to the design information of the target park, a park simulation model is set, wherein the design information refers to the overall design drawing of the target park; Acquire real-time monitoring images, identify locations in the monitoring images, and perform location matching in the park simulation model to determine the location area corresponding to the monitoring images in the park simulation model; When the location area is determined, the existing organisms in the monitoring screen are identified. Organisms refer to life forms that may cause damage to the vegetation of the target park, including tourists or animals. The identified organisms are then set as particles and marked in the park simulation model. At the same time, based on the location of the organisms in the target park, each particle has a corresponding position in the park simulation model.

[0007] As a further solution of the present invention, there are multiple monitoring points in the target park, each monitoring point has a fixed monitoring area, and the monitoring equipment at each monitoring point has a unique IP address; When the monitoring device at the monitoring point transmits the real-time monitoring picture to the simulation module, the simulation module identifies the IP address of the data and determines the transmission location of the monitoring picture based on the IP address; According to the monitoring point corresponding to the transmission position, the monitoring picture is directly matched with the monitoring area of the monitoring point, and then the location area corresponding to the monitoring picture in the park simulation model is determined.

[0008] As a further solution of the present invention, a method for collecting a particle distribution image includes: Based on the design layout of the target park, functional areas are set for the target park, wherein the functional areas include trail areas and vegetation areas. Furthermore, the trail area refers to an area paved with materials such as plastic and permeable bricks for people to take walks and other leisure activities, and the vegetation area refers to an area paved with herbaceous plants; A unit time is set, and based on the unit time, the distribution state of the particle vegetation area in the park simulation model is collected in real time to obtain a particle distribution image.

[0009] As a further solution of the present invention, a method for determining a concentrated image includes: S1: Set a unit area region and simultaneously obtain a particle distribution image. Take the unit area region as the basic region, and then perform image partitioning acquisition on the particle distribution image according to a fixed acquisition time and the unit area region, and then obtain a unit distribution image; S2: Identify the number of particles in each unit distribution image, and compare the number of particles with the threshold number X1. If the number of particles is less than or equal to the threshold number X1, the corresponding unit distribution image is marked as a normal image. Conversely, if the number of particles is greater than the threshold number X1, the corresponding unit distribution image is marked as a concentrated image.

[0010] As a further solution of the present invention, the method for obtaining the aggregation area includes: When a concentrated image is detected, obtain the area position corresponding to the concentrated image, mark this position as the target analysis area, and obtain all unit distribution images within the target analysis area within the effective time; Arrange the unit distribution images of the target analysis area in position in chronological order, and at the same time, according to the acquisition time of the unit distribution images, form an image set for the unit distribution images corresponding to the same natural day; Again in chronological order, select the image set corresponding to the first natural day and mark it as the target set. Taking the target set as an example, identify the number of mass points on each unit distribution image in the target set, delete the images with the number of mass points being 0 on the unit distribution images in the target set, and at the same time mark the number of mass points on the remaining unit distribution images as the regional scatter point number Li, where i represents different unit distribution images; After that, perform mean processing on the regional scatter point number Li and mark the mean processing result as the regional aggregation value of the target analysis area; Process all the unit distribution images in the target set according to the above method to obtain the regional aggregation value Qj for each natural day; Use the formula to obtain the set coefficient Hx of the target analysis area, where Qmax represents the maximum value of the regional aggregation value, Qmin represents the minimum value of the regional aggregation value, Qa represents the mean value of Qj in the regional aggregation, b represents the number of natural days with concentrated images in the target analysis area, and J represents the total number of natural days within the effective time; Compare the set coefficient Hx of the target analysis area with the loss threshold X2. If Hx < X2, mark the target analysis area as a normal operation area; conversely, if Hx ≥ X2, mark the target analysis area as an aggregation area.

[0011] As a further solution of the present invention, the effective time means: taking the time when the vegetation maintenance of the target analysis area ended most recently and the target analysis area was reopened again as the starting time, setting the current time as the ending time, and the effective time is the time range duration between the starting time and the ending time.

[0012] As a further solution of the present invention, the method for obtaining the benchmark maintenance duration includes: When performing difference processing on the target maintenance image and the standard vegetation image, the difference processing includes vegetation coverage difference processing and vegetation color difference processing, and then multiply the obtained vegetation coverage difference and color difference by the corresponding weight coefficients and , and finally obtain the vegetation damage value. Further, vegetation coverage difference × + Color difference value × = Vegetation damage value, ; Multiply the vegetation damage value by the vegetation growth coefficient to obtain the benchmark maintenance duration of the aggregation area, and the vegetation growth coefficient is a threshold value.

[0013] As a further solution of the present invention, it further includes a monitoring and acquisition module for monitoring the target park to obtain real-time monitoring images, and then the monitoring and acquisition module transmits the real-time monitoring images to the simulation module.

[0014] Compared with the existing technologies, the advantages of the present invention are as follows: Through real-time monitoring images, the present invention performs simulation on the positions of the monitoring images and the positions of the mass points, and combines the concentrated images and the set coefficient of the target analysis area to accurately determine the aggregation area of tourists, which enables the park management department to timely grasp the activity dynamics of tourists and give early warnings of the possible ecological damage risks in the tourist concentration area. At the same time, by performing difference processing on the target maintenance image and the standard vegetation image of the aggregation area to obtain the vegetation damage value, and then determining the benchmark maintenance duration, the area maintenance module performs fine maintenance on the vegetation in the aggregation area according to this. Compared with the traditional unified standard maintenance method, the pertinence and effectiveness of the maintenance work are greatly improved; The present invention also accurately grasps the relationship between tourist activities and vegetation maintenance, minimizes the negative impact of tourist activities on the park ecological environment while ensuring the tourist experience, realizes the harmonious coexistence of tourists and park ecological resources, and is conducive to improving the overall quality of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic structural diagram of the system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0017] Refer to Figure 1 , an urban-rural fringe park symbiosis analysis system, including a monitoring and acquisition module, a simulation module, a region recognition module, an image analysis module, a local analysis module, and an area maintenance module.

[0018] The monitoring and acquisition module is used to monitor the target park to obtain real-time monitoring images. Here, the target park is an urban suburban park in a designated area. In this embodiment, the monitoring device is set as a video monitoring device, such as a camera. Further, when monitoring the target park, the public area of the target park is fully covered by the monitoring device. Then, the monitoring and acquisition module transmits the real-time monitoring images to the simulation module; The simulation module is used to set up a park simulation model according to the design information of the target park. Here, the design information refers to the overall design drawing of the target park; Obtain real-time monitoring images, identify the positions in the monitoring images, and perform position pairing in the park simulation model to determine the position area in the park simulation model corresponding to the monitoring images; After the position area is determined, identify the organisms existing in the monitoring images. Here, the organisms in this embodiment refer to living bodies that can damage the vegetation of the target park, including tourists or animals, etc. Then, set the identified organisms as mass points and mark them in the park simulation model. At the same time, based on the positions of the organisms in the target park, each mass point has a corresponding position in the park simulation model; It should be further noted that there are multiple monitoring points in the target park. Here, each monitoring point has a fixed monitoring area, and each monitoring device of the monitoring point has a unique IP address. When the monitoring device of the monitoring point transmits the real-time monitoring images to the simulation module, the simulation module identifies the IP address of the data and determines the transmission position of the monitoring images based on the IP address. Then, according to the monitoring point corresponding to the transmission position, directly correspond the monitoring images to the monitoring area of the monitoring point, and further determine the position area in the park simulation model corresponding to the monitoring images; Then, a two-way communication connection is set between the simulation module and the area recognition module; The area recognition module is used to obtain the park simulation model and, at the same time, set up functional areas for the target park based on the design layout of the target park. Here, the functional areas include a walking path area and a vegetation area. Further, the walking path area refers to the area paved with materials such as plastic and permeable bricks for people to take walks and other leisure activities, such as fitness walking paths and leisure squares, etc. The vegetation area refers to the area paved with herbaceous plants, such as large lawns, etc.; Then, set the unit time, and based on the unit time, collect the distribution state of the mass points in the vegetation area of the park simulation model in real time to obtain a mass point distribution image. Here, the specific value of the unit time is set by those skilled in the art according to big data experience. In this embodiment, the unit time is set to 15 minutes; Then, the area recognition module transmits the mass point distribution image to the image analysis module; The image analysis module is used to obtain the particle distribution image, and uses image processing methods to analyze the particle distribution image to determine the aggregation area of the target park. The specific method for determining the aggregation area includes: S1: Set the unit area region, and at the same time obtain the particle distribution image. Taking the unit area region as the basic region, then perform image partition acquisition on the particle distribution image according to the fixed acquisition time and the unit area region, and then obtain the unit distribution image. Among them, the specific value of the fixed acquisition time is set by those skilled in the art according to big data experience. In this embodiment, the fixed acquisition time is set to 10 minutes; S2: Identify the number of particles in each unit distribution image, and compare the number of particles with the threshold number X1. If the number of particles is less than or equal to the threshold number X1, then mark the corresponding unit distribution image as a normal image. On the contrary, if the number of particles is greater than the threshold number X1, then mark the corresponding unit distribution image as a concentrated image. Among them, the specific value of the threshold number X1 is taken by those skilled in the art according to big data experience; When a concentrated image is detected, obtain the regional position corresponding to the concentrated image, mark this position as the target analysis area, and obtain all unit distribution images within the effective time of the target analysis area. Among them, taking the time when the target analysis area was last opened after vegetation maintenance as the start time, and setting the current time as the end time, the effective time is the time interval duration between the start time and the end time; S3: Arrange the unit distribution images of the target analysis area in position in chronological order, and at the same time, according to the acquisition time of the unit distribution images, form an image set for the unit distribution images corresponding to the same natural day; Again in chronological order, select the image set corresponding to the first natural day and mark it as the target set. Taking the target set as an example, identify the number of particles on each unit distribution image in the target set, delete the images with the number of particles equal to 0 on the unit distribution images in the target set, and at the same time mark the number of particles on the remaining unit distribution images as the regional scatter number Li, where i represents different unit distribution images; After that, perform mean processing on the regional scatter number Li, and mark the mean processing result as the regional aggregation value of the target analysis area; Process all the unit distribution images in the target set according to the above method to obtain the regional aggregation value Qj of each natural day; S4: Use the formula Obtain the set coefficient Hx of the target analysis area, where Qmax represents the maximum value of the area aggregation value, Qmin represents the minimum value of the area aggregation value, Qa represents the average value of Qj in the area aggregation, b represents the number of natural days with concentrated images in the target analysis area, and J represents the total number of natural days within the effective time; Compare the set coefficient Hx of the target analysis area with the loss threshold X2. If Hx < X2, mark the target analysis area as a normal operation area; conversely, if Hx ≥ X2, mark the target analysis area as an aggregation area. The specific value of the loss threshold X2 is obtained by those skilled in the art through big data operations; After that, the image analysis module transmits the aggregation area to the local analysis module; The local analysis module is used to obtain the aggregation area and perform image analysis on the vegetation in the aggregation area to determine the reference maintenance duration of the aggregation area. The specific method for determining the reference maintenance duration includes: When an aggregation area is detected, collect the real-time image of the aggregation area, mark it as the target maintenance image, perform difference processing on the target maintenance image and the standard vegetation image to obtain the vegetation damage value; Among them, when performing difference processing on the target maintenance image and the standard vegetation image, the difference processing includes vegetation coverage difference processing and vegetation color difference processing. Then, multiply the obtained vegetation coverage difference and color difference by the corresponding weight coefficients and , and finally obtain the vegetation damage value. Further, vegetation coverage difference × + color difference × = vegetation damage value, , and and The specific values of are obtained by those skilled in the art through big data operations; After that, multiply the vegetation damage value by the vegetation growth coefficient to obtain the reference maintenance duration of the aggregation area. The vegetation growth coefficient is a threshold, and the specific value is obtained by those skilled in the art based on the growth data of the corresponding vegetation; After that, the local analysis module transmits the reference maintenance duration of the aggregation area to the area maintenance module; The area maintenance module is used to receive the reference maintenance duration of the aggregation area and perform fine maintenance on the vegetation in the aggregation area according to the reference maintenance duration of the aggregation area, providing a suitable picnic and rest place for tourists on the one hand and maintaining the basic shape of the lawn on the other hand.

[0019] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. An urban-rural fringe park symbiotic analysis system, characterized in that, Including: A simulation module, which is used to obtain real-time monitoring images and perform simulation in the park simulation model for the positions of the monitoring images and the mass points; An area recognition module, which is used to collect the mass point distribution images in the park simulation model; An image analysis module, which is used to divide the mass point distribution image into multiple unit distribution images, then identify the number of mass points in the unit distribution images, and determine the concentrated image; Identify the target analysis area corresponding to the concentrated image, obtain all the unit distribution images in the target analysis area, calculate the set coefficient of the target analysis area according to the number of scattered points in the unit distribution images, and determine the aggregation area from the set coefficient; A local analysis module, which is used to collect the target maintenance images of the aggregation area, perform difference processing on the target maintenance images and the standard vegetation images to obtain the vegetation damage value, and determine the reference maintenance duration of the aggregation area based on the vegetation damage value; An area maintenance module, which is used to perform fine maintenance on the vegetation in the aggregation area according to the reference maintenance duration of the aggregation area.

2. The symbiotic analysis system for urban suburban parks according to claim 1, characterized in that The method of simulation includes: Set the park simulation model according to the design information of the target park, where the design information refers to the overall design drawing of the target park; Obtain real-time monitoring images, identify the positions in the monitoring images, and perform position pairing in the park simulation model to determine the position area corresponding to the monitoring images in the park simulation model; After the position area is determined, identify the organisms existing in the monitoring images. Here, the organisms refer to the living bodies that will damage the vegetation in the target park, including tourists or animals, etc. Then set the identified organisms as mass points and mark them in the park simulation model. At the same time, based on the positions of the organisms in the target park, each mass point has a corresponding position in the park simulation model.

3. The symbiotic analysis system for urban suburban parks according to claim 2, wherein There are multiple monitoring points in the target park. Each monitoring point has a fixed monitoring area, and the monitoring devices at each monitoring point have unique IP addresses; When the monitoring device at the monitoring point transmits the real-time monitoring image to the simulation module, the simulation module identifies the IP address of the data and determines the transmission position of the monitoring image based on the IP address; According to the monitoring point corresponding to the transmission position, directly correspond the monitoring image to the monitoring area of the monitoring point, and then determine the position area corresponding to the monitoring image in the park simulation model.

4. The symbiotic analysis system for urban suburban parks according to claim 1, characterized in that, The method of collecting the mass point distribution images includes: Based on the design layout of the target park, set functional areas for the target park. Among them, the functional areas include a footpath area and a vegetation area. Further, the footpath area refers to the area paved with materials such as plastic and permeable bricks for people's leisure activities such as walking, and the vegetation area refers to the area paved with herbaceous plants; Set the unit time, and based on the unit time, collect the distribution state of the mass points in the vegetation area of the park simulation model in real time to obtain the mass point distribution image.

5. The symbiotic analysis system for an urban suburban park according to claim 1, wherein The method of determining the concentrated image includes: S1: Set the unit area region, and at the same time obtain the mass point distribution image. Taking the unit area region as the basic region, then perform image partition collection on the mass point distribution image according to the fixed collection time and the unit area region, and thus obtain the unit distribution image; S2: Identify the number of mass points in each unit distribution image, and compare the number of mass points with the threshold number X1. If the number of mass points is less than or equal to the threshold number X1, mark the corresponding unit distribution image as a normal image; otherwise, if the number of mass points is greater than the threshold number X1, mark the corresponding unit distribution image as a concentrated image.

6. The symbiotic analysis system for urban suburban parks according to claim 4, characterized in that, The method for obtaining the aggregation area includes: When a concentrated image is detected, obtain the regional position corresponding to the concentrated image, mark this position as the target analysis area, and obtain all unit distribution images of the target analysis area within the effective time; Arrange the unit distribution images of the target analysis area in position according to the time sequence. At the same time, according to the acquisition time of the unit distribution images, form an image set for the unit distribution images corresponding to the same natural day; Again, according to the time sequence, select the image set corresponding to the first natural day and mark it as the target set. Taking the target set as an example, identify the number of mass points on each unit distribution image in the target set, delete the images with the number of mass points equal to 0 on the unit distribution images in the target set, and at the same time mark the number of mass points on the remaining unit distribution images as the regional scatter point number Li, where i represents different unit distribution images; After that, perform mean processing on the regional scatter point number Li, and mark the result of the mean processing as the regional aggregation value of the target analysis area; Process all the unit distribution images in the target set according to the above method to obtain the regional aggregation value Qj of each natural day; Using the formula to obtain the set coefficient Hx of the target analysis area, where Qmax represents the maximum value of the regional aggregation value, Qmin represents the minimum value of the regional aggregation value, Qa represents the mean value of Qj in the regional aggregation, b represents the number of natural days with concentrated images in the target analysis area, and J represents the total number of natural days within the effective time; Compare the set coefficient Hx of the target analysis area with the loss threshold X2. If Hx < X2, mark the target analysis area as a normal operation area; otherwise, if Hx ≥ X2, mark the target analysis area as an aggregation area.

7. The symbiotic analysis system for urban suburban parks according to claim 6, wherein, The effective time refers to: taking the time when the vegetation maintenance of the target analysis area ends for the last time and then the target analysis area is reopened as the start time, and setting the current time as the end time. The effective time is the interval duration between the start time and the end time.

8. A symbiotic analysis system for urban suburban parks according to claim 1, characterized in that The method for obtaining the benchmark maintenance duration includes: When performing difference processing on the target maintenance image and the standard vegetation image, the difference processing includes vegetation coverage difference processing and vegetation color difference processing, and then the obtained vegetation coverage difference and color difference are multiplied by the corresponding weight coefficients respectively and , and finally the vegetation damage value is obtained. Further, vegetation coverage difference × + color difference × = vegetation damage value, ; Multiply the vegetation damage value by the vegetation growth coefficient to obtain the benchmark maintenance duration of the aggregation area, and the vegetation growth coefficient is a threshold.

9. The symbiotic analysis system for urban suburban parks according to claim 1, wherein It also includes a monitoring and acquisition module for monitoring the target park to obtain real-time monitoring images, and then the monitoring and acquisition module transmits the real-time monitoring images to the simulation module.

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