Fire prevention monitoring method, device, terminal and storage medium based on weed detection

Abstract

The present application is applicable to the field of information monitoring technology, and provides a fire prevention monitoring method, device, terminal and storage medium based on weed detection, wherein the method includes: obtaining a target weed image of a sampling area; based on the target weed image, identifying a target grass stem in a grass area, and marking the grass area and the target grass stem with pixels to obtain weed pixels and target grass stem pixels; based on the arrangement data of the weed pixels and the target grass stem pixels associated with each pixel column in the target weed image, calculating the weed growth amount corresponding to each pixel column; based on the weed growth amount corresponding to each pixel column, obtaining the weed flammability corresponding to the sampling area. This solution improves the operation and maintenance efficiency of photovoltaic power stations and reduces costs.

Description

Technical Field

[0001] The present application belongs to the field of information monitoring technology, and in particular, relates to a fire prevention monitoring method, device, terminal and storage medium based on weed detection. Background Art

[0002] As a common way to utilize solar energy resources, solar photovoltaic power generation system has developed rapidly in recent years and is widely used in the field of clean energy supply. However, photovoltaic power stations occupy a large area and are usually built in remote areas. The weeds under the photovoltaic panels not only affect the power generation effect, but also pose a great fire hazard.

[0003] In order to improve power generation efficiency, eliminate fire hazards, and effectively protect power assets, photovoltaic power stations will regularly carry out weeding to prevent weed accumulation from causing fires and photovoltaic panels. However, since photovoltaic power stations are mostly located in remote areas, the growth of weeds cannot be controlled in time, and frequent weeding will waste manpower and material resources. However, if weeding is not done, the fire hazards cannot be eliminated in time, resulting in high weeding operation and maintenance costs and low efficiency of photovoltaic power stations. Summary of the invention

[0004] The embodiments of the present application provide a fire monitoring method, device, terminal and storage medium based on weed detection to solve the problem in the prior art that the growth of weeds in remote areas cannot be timely controlled, resulting in high weed control and maintenance costs and low efficiency in photovoltaic power station operations.

[0005] A first aspect of an embodiment of the present application provides a fire prevention monitoring method based on weed detection, comprising:

[0006] Acquire an image of target weeds in the sampling area;

[0007] Based on the target weed image, target grass stems in a grass area are identified, and pixel points of the grass area and the target grass stems are marked to obtain weed pixel points and target grass stem pixel points;

[0008] Calculating the weed growth amount corresponding to each pixel column based on the arrangement data of the weed pixel points and the target grass stem pixel points associated with each pixel column in the target weed image;

[0009] The flammability of weeds corresponding to the sampling area is obtained based on the weed growth amount corresponding to each pixel column.

[0010] A second aspect of an embodiment of the present application provides a fire prevention monitoring device based on weed detection, comprising:

[0011] An image acquisition module, used for acquiring an image of target weeds in a sampling area;

[0012] A recognition module, for recognizing target grass stems in a grass area based on the target weed image, and marking pixels of the grass area and the target grass stems to obtain weed pixels and target grass stem pixels;

[0013] a calculation module, configured to calculate the weed growth amount corresponding to each pixel column based on the arrangement data of the weed pixel points and the target grass stem pixel points associated with each pixel column in the target weed image;

[0014] The flammability acquisition module is used to acquire the flammability of weeds corresponding to the sampling area based on the growth amount of weeds corresponding to each pixel column.

[0015] A third aspect of an embodiment of the present application provides a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0016] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0017] A fifth aspect of the present application provides a computer program product, which, when executed on a terminal, enables the terminal to execute the steps of the method described in the first aspect.

[0018] As can be seen from the above, in the embodiment of the present application, the target grass stems in the grass area are identified based on the target weed image of the sampling area, and the grass area and the target grass stems are pixel-marked to obtain weed pixels and target grass stem pixels, and the weed growth amount corresponding to each pixel column is calculated based on the arrangement data of the weed pixels and the target grass stem pixels associated with each pixel column in the target weed image, so as to further obtain the weed flammability corresponding to the sampling area. This process achieves the acquisition of weed distribution information and grass stem distribution information on the basis of image acquisition and image weed pixel extraction and analysis, calculates the weed growth amount and measures the weed flammability, so as to timely control the weed growth situation and obtain the fire hazard information, ensure that an effective and reasonable weeding plan can be formulated, reduce the manpower and material resources caused by frequent weeding, and reduce fire hazards, thereby improving the operation and maintenance efficiency of photovoltaic power stations and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a process of a fire prevention monitoring method based on weed detection provided in an embodiment of the present application. Figure 1 ;

[0021] Figure 2 is a schematic diagram of a target weed image provided in an embodiment of the present application;

[0022] Figure 3 This is a schematic diagram of extracting grass stem length provided in an embodiment of the present application;

[0023] Figure 4 This is a process of a fire prevention monitoring method based on weed detection provided in an embodiment of the present application. Figure 2 ;

[0024] Figure 5 is an example diagram of the image acquisition system architecture provided by an embodiment of the present application;

[0025] Figure 6 is a structural diagram of an image acquisition device provided in an embodiment of the present application;

[0026] Figure 7 is a structural diagram of a fire prevention monitoring device based on weed detection provided in an embodiment of the present application;

[0027] Figure 8 It is a structural diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0029] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0030] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0031] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0032] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0033] In a specific implementation, the terminal described in the embodiments of the present application includes, but is not limited to, other portable devices such as mobile phones, laptop computers, or tablet computers with touch-sensitive surfaces (e.g., touch screen displays and / or touch pads). It should also be understood that in some embodiments, the device is not a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., touch screen displays and / or touch pads).

[0034] In the following discussion, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse and / or joystick.

[0035] The terminal supports various applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disk burning application, a spreadsheet application, a game application, a telephone application, a video conferencing application, an email application, an instant messaging application, a workout support application, a photo management application, a digital camera application, a digital camcorder application, a web browsing application, a digital music player application, and / or a digital video player application.

[0036] Various applications that can be executed on the terminal can use at least one common physical user interface device such as a touch-sensitive surface. One or more functions of the touch-sensitive surface and corresponding information displayed on the terminal can be adjusted and / or changed between applications and / or within corresponding applications. In this way, the common physical architecture of the terminal (e.g., the touch-sensitive surface) can support various applications with user interfaces that are intuitive and transparent to the user.

[0037] It should be understood that the size of the serial numbers of the steps in this embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0038] Based on image acquisition and image analysis and processing, the present application provides a fire prevention monitoring solution based on weed detection, which realizes obtaining weed distribution information and grass stem distribution information on the basis of image acquisition and image weed pixel extraction and analysis, calculating the weed growth amount and measuring the weed flammability, so as to timely control the weed growth situation and obtain fire hazard information, ensure that an effective and reasonable weeding plan can be formulated, reduce the manpower and material resources caused by frequent weeding, reduce fire hazards, improve the operation and maintenance efficiency of photovoltaic power stations and reduce costs.

[0039] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0040] See also Figure 1 , Figure 1 This is a process of a fire prevention monitoring method based on weed detection provided in an embodiment of the present application. Figure 1 .like Figure 1 As shown, a fire prevention monitoring method based on weed detection comprises the following steps:

[0041] Step 101: Acquire a target weed image in a sampling area.

[0042] The sampling area is an area where weed detection is required, and the target weed image is a captured image obtained based on the sampling area.

[0043] Step 102 : based on the target weed image, identify the target grass stems in the grass area, and mark the pixels of the grass area and the target grass stems to obtain weed pixels and target grass stem pixels.

[0044] The grass area is the grass area in the target weed image. In different situations, the acquired target weed image may be entirely grass areas, entirely grass-free areas, or contain both grass areas and grass-free areas. In this step, when analyzing the target weed image, it is necessary to perform the analysis based on the grass area in the image.

[0045] Among them, it is necessary to identify grass stems in the grass area. The target grass stem is a part of the grass area.

[0046] When marking the pixels of the grass area and the target grass stem, it is specifically determined whether the pixels in the target weed image are pixels constituting weeds and further whether the pixels constituting weeds are pixels constituting the target grass stem. Therefore, when marking the pixels of the grass area and the target grass stem, the pixels marked as pixels of the target grass stem must be marked as weed pixels.

[0047] In the process of identifying the target grass stems in the grass area, as an optional implementation, the target grass stems in the grass area are identified based on the target weed image, including:

[0048] A grass area is extracted from the target weed image, texture extraction is performed on the grass area, and target grass stems growing along a set direction are obtained based on the extracted texture recognition.

[0049] Among them, the trained neural network model can be used to filter out the grass and grass-free areas from the target weed image, using vector A ij To express:

[0050]

[0051] Furthermore, the extraction of the target grass stems can be achieved by introducing a two-dimensional Gabor filter to extract the texture features of the grass area at different scales and directions, obtaining the approximately vertical grass stems in the image as the target grass stems and marking the pixels. Among them, the target grass stems growing along the set direction are specifically the grass stems generated along the vertical direction. Since the grass does not necessarily grow completely vertically in the vertical direction, the target grass stems growing along the set direction are the grass stems within a certain tilt angle range of the texture processing. The filter can be set to determine the direction of the approximately vertical grass stem texture as θ∈[80°,100°], and the maximum response of the Gabor change detection in this direction can be used to identify the grass stems with texture connectivity, and the binary vector V ij To express:

[0052]

[0053] In this process, when a pixel has both grass area and texture connectivity in the set direction, the pixel will be marked as a target grass stem pixel and represented by a binary vector X ij express:

[0054]

[0055] Furthermore, in order to reduce the interference of environmental noise in the image, when the Xi j = 0, that is, the pixel is not in the grass area or does not have texture connectivity in the set direction. For this pixel, if the two adjacent pixels on the left and right of the pixel have been marked as target grass stem pixels (i.e., X i-1j =1 and X i+1j =1), then it is determined that the pixel point also belongs to the target grass stem pixel point, that is:

[0056]

[0057] Step 103 : calculating the weed growth amount corresponding to each pixel column based on the arrangement data of the weed pixel points and the target grass stem pixel points associated with each pixel column in the target weed image.

[0058] The target weed image contains several pixel columns. Affected by the distribution positions of weed pixels and target acquisition pixels in the target weed image, the number of associated weed pixels and target grass stem pixels in each pixel column is different. The value may be 0 or there may be many.

[0059] In one embodiment, the weed growth amount corresponding to each pixel column is calculated based on the arrangement data of the weed pixel points and the target grass stem pixel points associated with each pixel column in the target weed image, including:

[0060] Based on the number of pixels continuously marked as target grass stem pixels in each pixel column of the target weed image, the grass stem length of the target grass stem in the corresponding pixel column is obtained;

[0061] Calculating a weed density weighted value based on the distribution ratio of weed pixel points in a set pixel area where each pixel column in the target weed image is located;

[0062] Based on the grass stem length and the weighted value of the weed density, the weed growth amount corresponding to each pixel column is calculated.

[0063] Combination Figure 2 , Figure 3 As shown, the target weed image is an image of size W*H. If a pixel satisfies X ij =1, where 1≤i≤H. For each pixel column, the jth column is divided into two columns according to the continuous X ij = 1, we can get the number of pixels that are continuously marked as target grass stem pixels, that is, the pixel length C of the grass stem. j :

[0064]

[0065] in is the number of pixels in the qth group in the jth column that are continuously marked as target grass stem pixels. A pixel column may contain multiple groups of pixels that are continuously marked as target grass stem pixels. Q represents the number of pixel groups extracted in the jth pixel column that are continuously marked as target grass stem pixels. j represents the column, and Q≤H. Since multiple truncated grass stem lengths may be extracted from a pixel column, the number of pixels in each group that are continuously marked as target grass stem pixels forms the grass stem length value of the corresponding truncated grass stem.

[0066] When obtaining the grass stem length of the target grass stem in the corresponding pixel column based on the number of pixels continuously marked as target grass stem pixels, in order to reduce the influence of noise and error on the result, the longest grass stem length can be used as the grass stem length of the target grass stem in the current pixel column in the target weed image, that is, Alternatively, a weighted average value is calculated by taking the lengths of many truncated grass stems extracted from the pixel column (i.e., the number of pixel points in each group of multiple groups of pixel points continuously marked as target grass stem pixel points) as the grass stem length of the target grass stem in the current pixel column in the target weed image.

[0067] In the process of obtaining the weed density weighted value based on the distribution ratio of weed pixel points in the set pixel area where each pixel column in the target weed image is located, the set pixel area is specifically an area including the pixel column itself and the pixel area range adjacent to the pixel column, and the width of the adjacent pixel area range (that is, the number of pixel columns it includes) can be set as needed.

[0068] like Figure 2 As shown, let R j is the set pixel area where the jth column is located, R j The width of the region is W j , the weed density weighted value d of the j-th pixel column j It can be expressed as:

[0069] Among them, N j For R j A within area ij =1 for the grassy area, W j A customizable constant, the default value is 5.

[0070] Finally, the product of the grass stem length and the weed density weight value may be used as the weed growth amount corresponding to each pixel column.

[0071] That is, the density-weighted weed growth in the jth column can be expressed as

[0072] The above process uses density-weighted calculation method, and does not need to know the actual height of the weeds. The growth status of the weeds can be reflected only by analyzing the pixel height of the weed stems in the weed image.

[0073] Step 104: based on the weed growth amount corresponding to each pixel column, obtain the flammability of the weeds corresponding to the sampling area.

[0074] The weed flammability is used to indicate the fire risk level of weeds in the current sampling area.

[0075] Specifically, the weed flammability is positively correlated with the weed growth amount. Alternatively, the weed flammability corresponding to the sampling area can be obtained through a mapping relationship between the weed growth amount and the weed flammability. For example, a weed flammability can correspond to a weed growth amount interval range. After calculating the weed growth amount in the current sampling area, it is determined in which interval the weed growth amount is, and the weed flammability of the current sampling area is determined to be the weed flammability corresponding to the area range.

[0076] As an optional implementation, the method of obtaining the flammability of weeds corresponding to the sampling area based on the weed growth amount corresponding to each pixel column includes:

[0077] The weed growth amount of all pixel columns is averaged; the average value is multiplied by the flammability coefficient corresponding to the sampling area to obtain the flammability of the weeds in the sampling area.

[0078] That is, the weed flammability Fe in the sampling area is calculated:

[0079]

[0080] Among them, a n is the flammability coefficient, which can be a value determined based on the historical growth of weeds in the current sampling area, the weed growth season, or the camera installation method, installation distance, etc. This value can be selected as a preset value and is positively correlated with the growth of weeds. The more vigorous the weed growth in the current sampling area, the larger the value.

[0081] The above processing process can automatically determine the flammability of weeds, so that operation and maintenance personnel can know the grass growth and flammability information of the photovoltaic power station without on-site observation, and arrange and carry out weeding work in a timely and reasonable manner.

[0082] In the embodiment of the present application, the target grass stems in the grass area are identified based on the target weed image of the sampling area, and the grass area and the target grass stems are pixel-marked to obtain weed pixels and target grass stem pixels. Based on the arrangement data of the weed pixels and target grass stem pixels associated with each pixel column in the target weed image, the weed growth amount corresponding to each pixel column is calculated to further obtain the weed flammability corresponding to the sampling area. This process achieves the acquisition of weed distribution information and grass stem distribution information on the basis of image acquisition and image weed pixel extraction and analysis, calculates the weed growth amount and measures the weed flammability, so as to timely control the weed growth situation and obtain the fire hazard information, ensure that an effective and reasonable weeding plan can be formulated, reduce the manpower and material resources caused by frequent weeding, reduce fire hazards, and improve the operation and maintenance efficiency of photovoltaic power stations and reduce costs.

[0083] The examples of the present application also provide different implementations of the fire prevention monitoring method based on weed detection.

[0084] See also Figure 4 , Figure 4 This is a process of a fire prevention monitoring method based on weed detection provided in an embodiment of the present application. Figure 2 .like Figure 4 As shown, a fire prevention monitoring method based on weed detection comprises the following steps:

[0085] Step 401 : collecting weed images in a monitoring area at a preset location point to obtain an image to be processed.

[0086] Specifically, combined Figure 5 and Figure 6 As shown, the preset position point may be located around the photovoltaic array, and an image acquisition device is arranged around the photovoltaic array to observe and collect weed images. The image acquisition device includes a visible light sensor unit, a power supply unit, and a wireless communication unit. The external power supply of the device can be realized by connecting to the DC module of the photovoltaic array. The visible light sensor unit collects image data and outputs the information through the wireless communication unit.

[0087] The weed growth conditions of each photovoltaic array are different, so the deployment method, position and number of image acquisition devices arranged in each array are different, but the angle between the center of the camera and the weed ground cannot be greater than 45°. Finally, the image of each array is transmitted back to the data storage and processing system and the corresponding processing process is performed through the fire monitoring method provided in this application.

[0088] As an optional implementation, the weed image is collected in the monitoring area at a preset position point to obtain an image to be processed, including:

[0089] Collect weed images in the monitoring area at a preset location to obtain an initial weed image;

[0090] Based on the initial weed image, combined with the collected meteorological data, a valid image is screened out from the initial weed image as an image to be processed.

[0091] In this process, if Figure 6 As shown, in the process of executing the corresponding processing by the fire monitoring method provided by this application, it is also necessary to combine with the meteorological monitoring system for processing to avoid collecting data in extreme weather and affecting the use of analysis results.

[0092] In order to reduce the impact of weather and environment on images, we set the image collection time between 8:00 and 16:00 in clear weather to improve the image collection effect. We can also compare multiple images and eliminate images with large differences to avoid capturing dynamic objects or being affected by large obstructions that affect the final result.

[0093] This process introduces meteorological data to avoid the impact of extreme weather and low light conditions on the analysis results, and selects pictures that best reflect the weed areas based on multiple pictures to ensure data reliability.

[0094] Step 402 : based on different sampling areas included in the monitoring area, the collected image to be processed is divided into image segments to obtain a target weed image corresponding to each sampling area.

[0095] For a collected photo, the photo can be divided into different image parts according to n sampling areas, and each image part is formed into a target weed image corresponding to the sampling area.

[0096] When performing subsequent image processing, the target weed image corresponding to each sampling area is used as a processing object to perform corresponding operations.

[0097] Step 403 : based on the target weed image, identify the target grass stems in the grass area, and mark the pixels of the grass area and the target grass stems to obtain weed pixels and target grass stem pixels.

[0098] The implementation process of this step is the same as the implementation process of step 102 in the aforementioned embodiment, and will not be repeated here.

[0099] Step 404 : calculating the weed growth amount corresponding to each pixel column based on the arrangement data of the weed pixel points and the target grass stem pixel points associated with each pixel column in the target weed image.

[0100] The implementation process of this step is the same as the implementation process of step 103 in the aforementioned embodiment, and will not be repeated here.

[0101] Step 405 , based on the weed growth amount corresponding to each pixel column, the flammability of the weeds corresponding to the sampling area is obtained.

[0102] The implementation process of this step is the same as the implementation process of step 104 in the aforementioned embodiment, and will not be repeated here.

[0103] Step 406, averaging the weed flammability values ​​corresponding to all sampling areas to obtain the target weed flammability value of the monitoring area.

[0104] After obtaining the weed flammability corresponding to each sampling area, the weed flammability corresponding to the entire monitoring area can be processed.

[0105] Step 407: Obtain the fire hazard level of the monitoring area based on the flammability of the target weeds.

[0106] After obtaining the weed flammability, the corresponding fire hazard level can be determined based on the weed flammability value, and corresponding warning information can be issued at the same time. For example, different weed flammability values ​​correspond to three fire hazard levels: high, medium, and low, and corresponding warning information or warning sounds are issued.

[0107] The above processing process can automatically determine the flammability of weeds and inform the operation and maintenance personnel of the fire hazard level information. The operation and maintenance personnel can know the grass growth conditions and possible fire hazard information of the photovoltaic power station without having to observe on-site, and arrange and carry out weeding work in a timely and reasonable manner, providing a reference for the weeding plan, so that the weeding efficiency and power generation efficiency reach an economical maximum point.

[0108] In the embodiment of the present application, the target grass stems in the grass area are identified based on the target weed image of the sampling area, and the grass area and the target grass stems are pixel-marked to obtain weed pixels and target grass stem pixels. Based on the arrangement data of the weed pixels and target grass stem pixels associated with each pixel column in the target weed image, the weed growth amount corresponding to each pixel column is calculated to further obtain the weed flammability corresponding to the sampling area. This process achieves the acquisition of weed distribution information and grass stem distribution information on the basis of image acquisition and image weed pixel extraction and analysis, calculates the weed growth amount and measures the weed flammability, so as to timely control the weed growth situation and obtain the fire hazard information, ensure that an effective and reasonable weeding plan can be formulated, reduce the manpower and material resources caused by frequent weeding, reduce fire hazards, and improve the operation and maintenance efficiency of photovoltaic power stations and reduce costs.

[0109] See also Figure 7 , Figure 7 : is a structural diagram of a fire monitoring device based on weed detection provided in an embodiment of the present application. For the sake of convenience of explanation, only the parts related to the embodiment of the present application are shown.

[0110] The fire prevention monitoring device 700 based on weed detection includes:

[0111] An image acquisition module 701 is used to acquire an image of target weeds in a sampling area;

[0112] The recognition module 702 is used to recognize target grass stems in a grass area based on the target weed image, and mark the pixels of the grass area and the target grass stems to obtain weed pixels and target grass stem pixels;

[0113] A calculation module 703 is used to calculate the weed growth amount corresponding to each pixel column based on the arrangement data of the weed pixel points and the target grass stem pixel points associated with each pixel column in the target weed image;

[0114] The flammability acquisition module 704 is used to acquire the flammability of weeds corresponding to the sampling area based on the weed growth amount corresponding to each pixel column.

[0115] The identification module is specifically used for:

[0116] Extracting a grass area from the target weed image;

[0117] Texture extraction is performed on the grass area, and the target grass stems growing along a set direction are obtained based on the extracted texture recognition.

[0118] The computing module is specifically used for:

[0119] Based on the number of pixels continuously marked as the target grass stem pixels in each pixel column of the target weed image, obtaining the grass stem length of the target grass stem in the corresponding pixel column;

[0120] Obtaining a weed density weighted value based on a distribution ratio of the weed pixel points in a set pixel area where each pixel column in the target weed image is located;

[0121] The weed growth amount corresponding to each pixel column is calculated based on the grass stem length and the weed density weighted value.

[0122] Among them, the flammability acquisition module is specifically used for:

[0123] averaging the weed growth amounts of all the pixel columns;

[0124] The average value is multiplied by the flammability coefficient corresponding to the sampling area to obtain the flammability of weeds in the sampling area.

[0125] The image acquisition module is specifically used for:

[0126] Collect weed images in the monitoring area at a preset location to obtain an image to be processed;

[0127] Based on different sampling areas included in the monitoring area, the collected image to be processed is divided to obtain a target weed image corresponding to each sampling area.

[0128] The device also includes:

[0129] The grade assessment module is used to average the weed flammability corresponding to all the sampling areas to obtain the target weed flammability of the monitoring area; based on the target weed flammability, obtain the fire hazard grade of the monitoring area.

[0130] The image acquisition module is more specifically used for:

[0131] Collect weed images in the monitoring area at a preset location to obtain an initial weed image;

[0132] Based on the initial weed image and in combination with the collected meteorological data, a valid image is screened out from the initial weed image as the image to be processed.

[0133] The fire prevention monitoring device based on weed detection provided in the embodiment of the present application can implement each process of the embodiment of the fire prevention monitoring method based on weed detection described above, and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0134] Figure 8 8 is a structural diagram of a terminal provided in an embodiment of the present application. As shown in the figure, the terminal 8 of this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown in the figure), a memory 81, and a computer program 82 stored in the memory 81 and executable on the at least one processor 80, wherein the processor 80 implements the steps of any of the above-mentioned method embodiments when executing the computer program 82.

[0135] The terminal 8 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 8 may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will appreciate that Figure 8 It is only an example of terminal 8 and does not constitute a limitation on terminal 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0136] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0137] The memory 81 may be an internal storage unit of the terminal 8, such as a hard disk or memory of the terminal 8. The memory 81 may also be an external storage device of the terminal 8, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 8. Further, the memory 81 may also include both an internal storage unit of the terminal 8 and an external storage device. The memory 81 is used to store the computer program and other programs and data required by the terminal. The memory 81 may also be used to temporarily store data that has been output or is to be output.

[0138] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0139] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0145] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be implemented through a computer program product. When the computer program product runs on a terminal, the terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0146] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A fire prevention monitoring method based on weed detection, characterized in that: include: Acquire an image of target weeds in the sampling area; Based on the target weed image, target grass stems in a grass area are identified, and pixel points of the grass area and the target grass stems are marked to obtain weed pixel points and target grass stem pixel points; Based on the number of pixels continuously marked as the target grass stem pixels in each pixel column of the target weed image, obtaining the grass stem length of the target grass stem in the corresponding pixel column; Obtaining a weed density weighted value based on a distribution ratio of the weed pixel points in a set pixel area where each pixel column in the target weed image is located; Based on the grass stem length and the weed density weighted value, calculating the weed growth amount corresponding to each pixel column; The flammability of weeds corresponding to the sampling area is obtained based on the weed growth amount corresponding to each pixel column.

2. The fire prevention monitoring method according to claim 1, characterized in that: The step of identifying target grass stems in a grass area based on the target weed image comprises: Extracting a grass area from the target weed image; Texture extraction is performed on the grass area, and the target grass stems growing along a set direction are obtained based on the extracted texture recognition.

3. The fire monitoring method according to claim 1, characterized in that: The obtaining the weed flammability corresponding to the sampling area based on the weed growth amount corresponding to each pixel column includes: averaging the weed growth amounts of all the pixel columns; The average value is multiplied by the flammability coefficient corresponding to the sampling area to obtain the flammability of weeds in the sampling area.

4. The fire prevention monitoring method according to claim 1, characterized in that: The step of obtaining the target weed image of the sampling area includes: Collect weed images in the monitoring area at a preset location to obtain an image to be processed; Based on different sampling areas included in the monitoring area, the collected image to be processed is divided to obtain a target weed image corresponding to each sampling area.

5. The fire prevention monitoring method according to claim 4, characterized in that: After obtaining the weed flammability corresponding to the sampling area based on the weed growth amount corresponding to each pixel column, the method further includes: Calculating an average of the weed flammability corresponding to all the sampling areas to obtain the target weed flammability of the monitoring area; Based on the flammability of the target weeds, the fire hazard level of the monitored area is obtained.

6. The fire prevention monitoring method according to claim 4, characterized in that: The step of collecting weed images in the monitoring area at a preset location point to obtain an image to be processed includes: Collect weed images in the monitoring area at a preset location to obtain an initial weed image; Based on the initial weed image and in combination with the collected meteorological data, a valid image is screened out from the initial weed image as the image to be processed.

7. A fire prevention monitoring device based on weed detection, characterized in that: include: An image acquisition module, used for acquiring an image of target weeds in a sampling area; A recognition module, for recognizing target grass stems in a grass area based on the target weed image, and marking pixels of the grass area and the target grass stems to obtain weed pixels and target grass stem pixels; a calculation module, configured to calculate the weed growth amount corresponding to each pixel column based on the arrangement data of the weed pixel points and the target grass stem pixel points associated with each pixel column in the target weed image; A flammability acquisition module, configured to acquire the flammability of weeds corresponding to the sampling area based on the growth amount of weeds corresponding to each pixel column; Wherein, the computing module is specifically used for: Based on the number of pixels continuously marked as the target grass stem pixels in each pixel column of the target weed image, obtaining the grass stem length of the target grass stem in the corresponding pixel column; Obtaining a weed density weighted value based on a distribution ratio of the weed pixel points in a set pixel area where each pixel column in the target weed image is located; The weed growth amount corresponding to each pixel column is calculated based on the grass stem length and the weed density weighted value.

8. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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