A bird-repellent system for photovoltaic modules based on big data and autonomous deep learning

Abstract

The present invention relates to the technical field of bird-repelling systems, and more specifically, to a bird-repelling system based on big data and autonomous deep learning for photovoltaic modules. Bird collection modules and bird-repelling units are distributed around the photovoltaic modules. The bird collection modules collect bird species and bird numbers close to the photovoltaic modules, and the bird-repelling units execute bird-repelling strategies accordingly based on the bird species collected by the bird collection modules. The feedback module provides feedback to the bird-repelling units based on changes in bird species and bird numbers before and after the execution of the bird-repelling strategies, thereby controlling the corresponding execution of the bird-repelling strategies by the bird-repelling units. The autonomous learning module records the known bird species and bird numbers to be driven away, and the bird-repelling strategies executed by the bird-repelling units are bound to the bird species to be driven away, and a data model is continuously constructed and improved to achieve adaptive bird expulsion, thereby reducing the cleaning difficulty and operation and maintenance costs of the photovoltaic modules.

Description

Technical Field

[0001] The present invention relates to the technical field of bird-repelling systems, and more specifically, to a bird-repelling system for photovoltaic modules based on big data and autonomous deep learning. Background Art

[0002] Photovoltaic power generation is a highly efficient technology that utilizes the photovoltaic effect at semiconductor interfaces to directly convert sunlight into electricity. The system consists of three key components: solar panels, controllers, and inverters. Solar panels, also known as photovoltaic modules, are typically large-area modules formed by connecting and packaging multiple solar cells in series. They efficiently convert sunlight into electricity.

[0003] Since photovoltaic panels are exposed to the outdoor environment for a long time, they are easily clogged with dust, leaves, and other debris, which directly affects the intensity of light they receive and reduces the efficiency of light energy conversion. Therefore, regular cleaning and maintenance of photovoltaic panels is particularly important.

[0004] Furthermore, birds may leave droppings when flying over, which not only further obstructs light but is also extremely difficult to clean due to its composition and stickiness. If bird droppings remain on PV panels for a long time, maintenance becomes more difficult and may damage the panels.

[0005] To ensure the smoothness and power generation efficiency of photovoltaic modules, they are usually cleaned regularly in dusty areas or areas with frequent bird activities, but this undoubtedly further increases the cost of operation and maintenance. Summary of the Invention

[0006] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a bird-repellent system for photovoltaic modules based on big data and autonomous deep learning, which has the advantages of targeted expulsion of bird flocks, reducing the difficulty of cleaning photovoltaic modules and reducing operation and maintenance costs.

[0007] The above technical objectives of the present invention are achieved through the following technical solutions: A bird repellent system for photovoltaic modules based on big data and autonomous deep learning, comprising:

[0008] Bird collection module, collects bird species and bird numbers close to photovoltaic panels;

[0009] The bird-repelling unit executes the corresponding bird-repelling strategy according to the bird species collected by the bird collection module;

[0010] Feedback module, which provides feedback to the bird-repelling unit based on the changes in bird species and bird numbers before and after the implementation of the bird-repelling strategy;

[0011] The autonomous learning module collects the bird species and bird numbers collected by the bird collection module and the non-expelled bird species and non-expelled bird numbers collected by the feedback module after the bird-repelling unit is started. The bird-repelling strategy executed by the bird-repelling unit is bound to the expelled bird species.

[0012] Preferably, the bird collection module includes a camera module and an infrared sensing component. The infrared sensing component senses the direction of the bird flock. After determining the direction of the bird flock, the camera module cooperates to capture and photograph the bird flock.

[0013] Based on multiple camera photos captured by the camera module, the identification target in the camera photo is randomly framed, and the color, head, and body of the identification target are used as identification feature elements. Based on the comparison between the standard database and the identification feature elements, the bird species in the bird flock are identified.

[0014] Preferably, a bird feature scoring model is established based on the color, head, and body identification feature elements of the identification target as weights:

[0015] T=(C*W1)+(H*W2)+(B*W3);

[0016] Where C: color score, W1: color weight, H: head score; W2: head weight; B: body score; W3: body weight; T: bird feature score;

[0017] Based on the bird feature score, the identified bird species are sorted by bird priority, and the bird repelling unit issues bird repelling strategies in sequence for the identified bird species according to the bird priority.

[0018] Preferably, the bird collection module collects and compares the camera photos before and after the operation of the bird repelling unit, and based on the comparison between the bird species and the number of birds in the bird flock before and after the operation of the bird repelling unit, further feeds back to the bird collection module through the feedback module, and the bird collection module further analyzes the collected identification targets to identify the bird species in the bird flock. Based on the determination of the bird species in the bird flock, the bird repelling unit executes the repelling strategy for the bird flock, and the operation is cyclical until the bird flock falls off the sensing of the bird collection module.

[0019] Preferably, the bird-repelling strategies implemented by the bird-repelling unit include sound wave repelling, visual repelling and odor repelling;

[0020] The sonic repelling method plays sound waves of a specific frequency to interfere with or induce the birds to stay away.

[0021] The visual driving device is used to scare the flock of birds by arranging flashing lights or moving visual devices;

[0022] The odor driving device sprays chemical odors in the direction of the bird flock to drive the bird flock away.

[0023] Preferably, the bird repellent strategies of sound wave repelling, visual repelling and odor repelling are sorted according to the priority interference level, and the corresponding bird repellent strategy is selected according to the distance of the bird flock's movement direction close to the photovoltaic component collected by the bird collection module, first by sound wave repelling, then by adding visual repelling, and finally by adding odor repelling.

[0024] Preferably, the autonomous learning module conducts a comprehensive evaluation based on the changes in the number of birds before and after a single operation of the bird-repelling unit, and records the correspondence between bird species and bird-repelling strategies, and establishes a bird-repelling strategy corresponding to the same bird species next time based on the comprehensive evaluation data.

[0025] Preferably, an evaluation threshold of the comprehensive evaluation is set in the autonomous learning module. When the calculated value based on the comprehensive evaluation is greater than or equal to the evaluation threshold, the autonomous learning module will feedback to the bird-repelling unit to preferentially execute the same corresponding bird-repelling strategy for the same bird species. When the calculated value based on the model of the comprehensive evaluation is less than the evaluation threshold, it will be recorded and saved.

[0026] Preferably, the autonomous learning module includes an established original large database, in which each bird species is matched with a corresponding bird-repelling strategy, and a basic threshold of bird-repelling effect is set for each bird-repelling strategy. If the comprehensive evaluation result is less than the basic threshold, it is judged that the bird species identified by the bird collection module may be wrong, until the corresponding bird species is found, and the recognition accuracy of the bird collection module in identifying the bird species is reversely improved.

[0027] In summary, the present invention has the beneficial effects: bird collection modules and bird repellent units are distributed around the photovoltaic components, the bird collection modules are used to collect the species and number of birds close to the photovoltaic components, the bird repellent unit performs the bird repellent strategy accordingly based on the bird species collected by the bird collection module, the feedback module provides feedback to the bird repellent unit based on the changes in bird species and number of birds before and after the execution of the bird repellent strategy, thereby controlling the corresponding execution of the bird repellent strategy by the bird repellent unit, based on the comparison of the bird species and number of birds before and after the bird repellent unit executes the bird repellent strategy collected by the bird collection module, the self-learning module records the known species and number of expelled birds, the bird repellent strategy executed by the bird repellent unit is bound to the species of expelled birds, and the data model is continuously constructed and improved to achieve adaptive bird expulsion, thereby reducing the cleaning difficulty and operation and maintenance costs of the photovoltaic components. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a simplified connection diagram of an embodiment of the present invention;

[0029] Figure 2 Schematic diagram of the division of the bird-repelling strategy according to an embodiment of the present invention.

[0030] Figure numerals: 1. Bird collection module; 11. Bird repelling unit; 12. Feedback module; 13. Autonomous learning module; 14. Original large database; 2. Sound wave repelling; 21. Visual repelling; 22. Odor repelling; 3. Photovoltaic module. DETAILED DESCRIPTION

[0031] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0032] It should be noted that when a component is referred to as being “fixed to” or “disposed on” another component, it can be directly on the other component or indirectly on the other component. When a component is referred to as being “connected to” another component, it can be directly or indirectly connected to the other component.

[0033] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0034] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0035] A photovoltaic module 3-use bird repellent system based on big data and autonomous deep learning, see Figure 1 ,include:

[0036] Bird collection module 1, collects bird species and bird numbers close to photovoltaic module 3;

[0037] The bird-repelling unit 11 executes a corresponding bird-repelling strategy according to the bird species collected by the bird collection module 1;

[0038] The feedback module 12 provides feedback to the bird-repelling unit 11 based on the changes in bird species and bird numbers before and after the bird-repelling strategy is executed;

[0039] The autonomous learning module 13 collects the bird species and bird numbers collected by the bird collection module 1 and the non-expelled bird species and non-expelled bird numbers collected by the feedback module 12 after starting the bird-repelling unit 11. The expelled bird species and the expelled bird numbers are known, and the bird-repelling strategy executed by the bird-repelling unit 11 is bound to the expelled bird species.

[0040] In this embodiment, a bird collection module 1 and a bird repelling unit 11 are distributed around the photovoltaic module 3. The bird collection module 1 collects the species and number of birds near the photovoltaic module 3, and the bird repelling unit 11 executes the bird repelling strategy accordingly according to the bird species collected by the bird collection module 1. The feedback module 12 provides feedback to the bird repelling unit 11 according to the changes in the bird species and number of birds before and after the execution of the bird repelling strategy, thereby controlling the correspondence of the bird repelling unit 11 to execute the bird repelling strategy. Based on the comparison of the bird species and number of birds collected by the bird collection module 1 before and after the bird repelling unit 11 executes the bird repelling strategy, the autonomous learning module 13 records the known species and number of birds to be driven away, and the bird repelling strategy executed by the bird repelling unit 11 is bound to the species of birds to be driven away, and the data model is continuously constructed and improved to achieve adaptive bird expulsion, thereby reducing the cleaning difficulty and operation and maintenance cost of the photovoltaic module 3.

[0041] The bird collection module 1 includes a camera module, an infrared sensing component, a radar and other sensors. The infrared sensing component or the radar senses the direction of the bird flock. After determining the direction of the bird flock, the camera module cooperates to capture and film the bird flock.

[0042] Based on multiple camera photos captured by the camera module, the identification target in the camera photo is randomly framed, and the color, head, body, wings, etc. of the identification target are used as identification feature elements. Based on the comparison between the standard database and the identification feature elements, the bird species in the bird flock are identified.

[0043] Due to the large number of bird species and the fact that bird flocks are always on the move, errors often occur when identifying bird species in a flock. Different bird species require different bird-repelling strategies. To this end, a bird feature scoring model is established, using the color, head, and body of the target as weights:

[0044] T=(C*W1)+(H*W2)+(B*W3);

[0045] Where C: color score, W1: color weight, H: head score; W2: head weight; B: body score; W3: body weight; T: bird feature score;

[0046] Based on the bird feature score, the identified bird species are sorted by bird priority, and the bird repelling unit 11 executes a bird repelling strategy for the identified bird species according to the bird priority.

[0047] The proportion of weights is obtained through a large amount of data and experiments, so I will not elaborate on them one by one here.

[0048] When the weights are known, the bird species collected by the recognition module are identified, and the bird feature score of the identification target corresponding to the various bird species is calculated according to the familiarity scores of the various birds in the color, head and body of the identification target. The corresponding bird species are sorted by bird priority. According to the bird priority sorting, the bird-repelling unit 11 issues bird-repelling strategies for the identified bird species in turn.

[0049] Each time the bird-repelling unit 11 executes a bird-repelling strategy, the bird collection module 1 collects and compares the camera photos before and after the operation of the bird-repelling unit 11. Based on the comparison between the bird species and the number of birds in the bird flock before and after the operation of the bird-repelling unit 11, the feedback is further fed back to the bird collection module 1 through the feedback module 12. The bird collection module 1 further analyzes the collected identification targets to identify the bird species in the bird flock. Based on the determination of the bird species in the bird flock, the bird-repelling unit 11 executes the driving strategy on the bird flock. This cycle is repeated until the bird flock falls out of the sensing range of the bird collection module 1.

[0050] In addition, regarding the bird-repelling strategy executed by the bird-repelling unit 11, the degree and manner of disturbing the birds in the flock are different due to the different species of the birds.

[0051] The bird repelling strategies of this embodiment include sound wave repelling 2, visual repelling 21 and odor repelling 22.

[0052] See also Figure 2 , wherein the sound wave driving 2 plays sound waves of a specific frequency to interfere with or induce the birds to stay away;

[0053] The visual driving 21 scare the birds by arranging flashing lights or moving visual devices;

[0054] The scent repellent 22 sprays chemical scents in the direction of the bird flock to repel the bird flock.

[0055] Since visual repelling 21 is affected by the propagation distance and propagation area, and odor repelling 22 is affected by the external environment due to its duration, and odor repelling 22 also requires the replenishment of chemical reagents, this application combines the advantages and disadvantages of the three bird repelling strategies and combines sound wave repelling 2, visual repelling 21 and odor repelling 22 for application.

[0056] Specifically, the bird repelling strategies of sound wave repelling 2, visual repelling 21 and odor repelling 22 are sorted by priority interference level, and the distance of the bird flock's movement direction collected by the bird collection module 1 close to the photovoltaic component 3 is selected, and the sound wave repelling 2 is used first, and then the visual repelling 21 is added, and finally the odor repelling 22 is added.

[0057] Bird repelling strategies also include sound repelling at specific frequencies2; visual repelling using different light colors21; and odor repelling using different scents22.

[0058] The bird collection module 1 collects the movement direction of the bird flock and the distance from the photovoltaic module 3, and adopts different bird repelling strategies in sequence. The first is sound wave repelling 2, which has a large diffusion area and is accurate. Secondly, visual repelling 21 is used to interfere, and finally smell repelling 22 is used to further enhance the repelling effect.

[0059] Using the bird repelling strategies of acoustic repelling 2, visual repelling 21 and odor repelling 22 to prioritize interference levels can avoid wasting resources while maximizing the expulsion and interference of bird species.

[0060] In different areas, three different bird-repelling strategies, namely, sound repelling 2, visual repelling 21 and odor repelling 22, are established to avoid the mutual influence between different bird-repelling strategies and the resulting random flight of birds.

[0061] Secondly, the autonomous learning module 13 described in this application conducts a comprehensive evaluation based on the changes in the number of birds before and after a single operation of the bird-repelling unit 11, and records the correspondence between bird species and bird-repelling strategies, and establishes a bird-repelling strategy corresponding to the same bird species next time based on the comprehensive evaluation data.

[0062] In actual operation, after implementing the bird-repelling strategy, a comprehensive evaluation is conducted based on the changes in the number of birds before and after, to determine which sound wave corresponds to which bird species with the most obvious expulsion effect. The corresponding relationship between the bird species and the sound wave is bound and recorded, and can be used as a bird-repelling strategy for the same bird next time.

[0063] Since different bird-repelling strategies have different effects on the expulsion of the same bird species, the autonomous learning module 13 can use the different bird-repelling strategies that can be executed by the bird-repelling unit 11 to find the best bird-repelling strategy for the same bird species.

[0064] Based on this, by setting the evaluation threshold of the comprehensive evaluation in the autonomous learning module 13, when the calculated value based on the comprehensive evaluation is greater than or equal to the evaluation threshold, the autonomous learning module 13 will feedback to the bird-repelling unit 11 to preferentially execute the same corresponding bird-repelling strategy for the same bird species; when the calculated value based on the comprehensive evaluation model is less than the evaluation threshold, it will be recorded and saved.

[0065] In addition, an original large database 14 is established in the autonomous learning module 13. In the original large database 14, each bird species is matched with a corresponding bird-repelling strategy, and a basic threshold of the bird-repelling effect is set for each bird-repelling strategy. If the comprehensive evaluation result is less than the basic threshold, it is judged that the bird species identified by the bird collection module 1 may be wrong, until the corresponding bird species is found, and the recognition accuracy of the bird collection module 1 in identifying the bird species is reversely improved.

[0066] The bird-repelling effect here refers to the change in the number of birds before and after the bird-repelling unit 11 is executed. If the comprehensive evaluation result is less than the basic threshold, it is determined that the bird species identified by the bird collection module 1 may be incorrect. Based on the bird species obtained by the bird collection module 1 in the identified bird flock, the bird priority ranking is carried out according to the identified bird species, and the bird-repelling strategy for the next level of bird species is applied accordingly until the bird flock is completely repelled, thereby improving the recognition accuracy of the bird species identified by the bird collection module 1.

[0067] The above embodiments are merely explanations of the present invention and are not limitations of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the embodiments as needed. However, as long as they are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. A bird repellent system for photovoltaic modules based on big data and autonomous deep learning, characterized by: include: Bird collection module, collects photos of bird species and numbers near photovoltaic panels for identification; The bird-repelling unit executes the corresponding bird-repelling strategy according to the bird species collected by the bird collection module; Feedback module, which provides feedback to the bird-repelling unit based on the changes in bird species and bird numbers before and after the implementation of the bird-repelling strategy; The autonomous learning module collects the bird species and bird numbers collected by the bird collection module and the bird species and bird numbers not driven away collected by the feedback module after the bird driving unit is activated. The bird driving strategy executed by the bird driving unit is bound to the driven bird species. The bird collection module includes a camera module and an infrared sensing component. The infrared sensing component senses the direction of the bird flock. After determining the direction of the bird flock, the camera module cooperates to capture and film the bird flock. Randomly framing an identification target in a plurality of photographs captured by a camera module, using the color, head, and body of the identification target as identification feature elements, and identifying the species of birds in the flock based on a comparison between a standard database and the identification feature elements; A bird feature scoring model is established based on the recognition feature elements of the target's color, head, and body as weights: T=(C*W1)+(H*W2)+(B*W3); in C: color score, W1: color weight, H: head score; W2: head weight; B: Body score; W3: body weight; T: bird feature scoring score; Based on the bird feature score, the identified bird species are ranked by bird priority, and the bird repelling unit issues bird repelling strategies for the identified bird species in sequence according to the bird priority ranking; The bird collection module collects and compares the camera photos before and after the operation of the bird repelling unit. Based on the comparison between the bird species and the number of birds in the bird flock before and after the operation of the bird repelling unit, the comparison is further fed back to the bird collection module through the feedback module. The bird collection module further analyzes the collected identification targets to identify the bird species in the bird flock. Based on the determination of the bird species in the bird flock, the bird repelling unit executes the repelling strategy on the bird flock, and this cycle is repeated until the bird flock falls off the sensing of the bird collection module; The bird-repelling strategies implemented by the bird-repelling unit include acoustic repelling, visual repelling and odor repelling; The bird repellent strategies of acoustic repellent, visual repellent and odor repellent are sorted by priority interference level. According to the movement direction of the bird flock collected by the bird collection module and the distance close to the photovoltaic module, the corresponding bird repellent strategy is selected, which is firstly driven by acoustic repellent, then by adding visual repellent, and finally by adding odor repellent; The autonomous learning module performs a comprehensive evaluation based on the changes in the number of birds before and after a single operation of the bird-repelling unit, and records the corresponding relationship between bird species and bird-repelling strategies, and establishes a bird-repelling strategy corresponding to the same bird species next time based on the comprehensive evaluation data.

2. The bird repellent system for photovoltaic modules based on big data and autonomous deep learning according to claim 1, characterized in that: The sonic repelling method plays sound waves of a specific frequency to interfere with or induce the birds to stay away. The visual driving device is used to scare the flock of birds by arranging flashing lights or moving visual devices; The odor driving device sprays chemical odors in the direction of the bird flock to drive the bird flock away.

3. The bird repellent system based on big data and autonomous deep learning for photovoltaic modules according to claim 1 is characterized in that: An evaluation threshold for comprehensive evaluation is set in the autonomous learning module. When the calculated value based on the comprehensive evaluation is greater than or equal to the evaluation threshold, the autonomous learning module will feedback to the bird-repelling unit to preferentially execute the same corresponding bird-repelling strategy for the same bird species. When the calculated value based on the model of comprehensive evaluation is less than the evaluation threshold, it will be recorded and saved.

4. The bird repellent system for photovoltaic modules based on big data and autonomous deep learning according to claim 3 is characterized by: The autonomous learning module includes an established original large database, in which each bird species is matched with a corresponding bird-repelling strategy, and a basic threshold of bird-repelling effect is set for each bird-repelling strategy. If the comprehensive evaluation result is less than the basic threshold, it is judged that the bird species identified by the bird collection module may be wrong, until the corresponding bird species is found, and the recognition accuracy of the bird collection module in identifying the bird species is reversely improved.

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