Agricultural facility monitoring method and system based on big data

Abstract

The present invention is applicable to the computer field and provides an agricultural facility monitoring method and system based on big data, the method comprising: obtaining inspection information of a planting area, the inspection information comprising edge inspection images; judging whether branches and leaves in a non-bottom part of a plant in a first sub-area are deformed based on the inspection information, the planting sub-area comprising several first sub-areas; if it is determined that branches and leaves in a non-bottom part of a plant in the first sub-area are deformed, locating the corresponding first sub-area; obtaining bottom identification information of the corresponding first sub-area, judging whether a print change occurs in the corresponding first sub-area based on the bottom identification information; if it is determined that a print change occurs in the corresponding first sub-area, judging that a foreign object has entered the corresponding first sub-area. The beneficial effect of the present invention is that it can identify areas that are difficult to be found when crops are damaged, thereby providing a reference for the maintenance of agricultural facilities.

Description

Technical Field

[0001] The present invention belongs to the field of computers, and in particular relates to a method and system for monitoring agricultural facilities based on big data. Background Art

[0002] Agriculture is very important, and smart agriculture is the future development direction of agriculture. Agricultural facilities refer to various facilities with public service functions that ensure that agricultural production and circulation can proceed smoothly under suitable conditions. Agricultural facilities mainly include planting facilities and breeding facilities.

[0003] In existing technologies, smart agriculture is an important application of Internet of Things technology in the field of modern agriculture. It mainly includes remote control functions, monitoring functions, and real-time image and video monitoring functions. For example, through the remote control system, users can remotely view facility environmental data and equipment operation status, and can also analyze data for convenient and flexible management; it can also realize automatic control of the operation of heating, cooling and other equipment according to the preset planting conditions, meet the strict environmental conditions requirements for crop planting, and reduce production costs.

[0004] Currently, agricultural facilities may be damaged by external vandals, especially in planting areas. When the protective facilities in the planting areas are destroyed, there is a possibility that the equipment and crops in the planting areas will be damaged. Considering the coverage of video surveillance and the area of ​​the planting areas, if the external vandals are not directly monitored, the damage to the crops will be difficult to detect. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide an agricultural facility monitoring method and system based on big data, aiming to solve the problems raised in the above background technology.

[0006] The embodiment of the present invention is implemented as follows: on the one hand, a method for monitoring agricultural facilities based on big data, the method comprising the following steps:

[0007] Acquiring inspection information of the planting area, wherein the inspection information includes edge inspection images;

[0008] Determining whether branches and leaves in a non-bottom portion of a plant in a first sub-area are deformed based on the inspection information, wherein the planting sub-area includes a plurality of first sub-areas;

[0009] If it is determined that branches and leaves in a non-bottom portion of the plant in the first sub-region are deformed, the corresponding first sub-region is located;

[0010] Acquiring bottom identification information of the corresponding first sub-region, and determining whether a print change occurs in the corresponding first sub-region according to the bottom identification information;

[0011] If it is determined that a footprint change occurs in the corresponding first sub-area, it is determined that a foreign object has entered the corresponding first sub-area, and an alarm message is reported to prompt that branch and leaf deformation and footprint changes caused by the entry of foreign objects have occurred in the corresponding first sub-area.

[0012] As a further embodiment of the present invention, the method further comprises:

[0013] issuing an inspection instruction to the first mobile inspection device, wherein the inspection instruction includes inspection parameters, the inspection parameters including an inspection movement speed and a movement route, and the movement route covers at least a plurality of first sub-areas;

[0014] According to the inspection parameters, the first mobile inspection device is instructed to move according to the inspection parameters in the inspection instruction to obtain inspection images, wherein the inspection images include edge movement inspection images.

[0015] As a further embodiment of the present invention, the method further comprises:

[0016] Instructing the second mobile inspection device to receive the inspection priority instruction reported by the fixed-point inspection device, wherein at least one fixed-point inspection device is provided in each first area, and when the fixed-point inspection device identifies abnormal sound information, generating an inspection priority instruction and reporting the inspection priority instruction to the second mobile inspection device, wherein the inspection priority instruction carries an identifier of the area where the abnormal sound information is located;

[0017] The second mobile inspection device is instructed to move to at least one first sub-area where the area identifier is located according to the area identifier in the inspection priority instruction, so as to obtain an edge mobile inspection image.

[0018] As a further aspect of the present invention, the method further comprises:

[0019] When the number of inspection priority instructions is greater than or equal to 3 within the set time period, it is determined whether only a certain first sub-area is located within the set distance of the current first area;

[0020] If yes, the inspection priority of the first sub-area is raised to the highest priority;

[0021] Otherwise, the original inspection priority is maintained, wherein the original inspection priority is determined according to the generation time of the inspection priority instruction. The earlier the generation time, the higher the corresponding inspection priority.

[0022] As a further embodiment of the present invention, the method further comprises:

[0023] detecting whether the bending of the branches of the plant reaches a first set deformation, and if so, determining that the first condition is satisfied;

[0024] detecting whether the displacement of the fruit leaves in the plant reaches a second set deformation, and if so, determining that the second condition is satisfied;

[0025] When at least one of the first condition and the second condition is satisfied, it is determined that the non-bottom portion of the plant in the first sub-region has branch and leaf deformation.

[0026] As a further solution of the present invention, the determining whether a print change occurs in the corresponding first sub-region according to the bottom identification information specifically includes:

[0027] extracting the surface printing mark and / or sinking trace in the bottom identification information;

[0028] detecting whether the surface printing mark in the bottom identification information reaches a first set area, and if so, determining that the third condition is satisfied;

[0029] detecting whether the sinking trace in the bottom identification information that is not less than the preset covering area reaches a set depth, and if so, determining that the fourth condition is satisfied;

[0030] When at least one of the third condition and the fourth condition is satisfied, it is determined that a footprint change occurs in the corresponding first sub-region.

[0031] As a further embodiment of the present invention, the method further comprises:

[0032] When it is determined that a footprint change occurs in the corresponding first sub-region, all points where the footprint change occurs are captured;

[0033] Arrange all points in the order of their appearance time segments to generate arrangement results;

[0034] Determining whether there are continuous points in the arrangement result, where the continuous points are used to indicate that a distance difference between at least two adjacent points in the arrangement result is within a threshold distance;

[0035] If continuous points appear in the arrangement result, identifying whether the direction of the continuous points tends to a certain section of the protection zone in the planting area, wherein the protection zone includes a side protection zone or a top protection zone;

[0036] If the direction of the continuous points tends to a certain section of the protection zone in the planting area, the corresponding section of the protection zone is located, damage inspection indication information is generated according to the corresponding section of the protection zone, and the damage inspection indication information is reported.

[0037] As a further embodiment of the present invention, in another aspect, a big data-based agricultural facility monitoring system is provided, the system comprising:

[0038] An inspection information acquisition module is used to acquire inspection information of the planting area, wherein the inspection information includes edge inspection images;

[0039] A deformation judgment module is used to judge whether branches and leaves of plants in a first sub-area other than the bottom part thereof are deformed according to the inspection information, wherein the planting sub-area includes a plurality of first sub-areas;

[0040] The region positioning module is configured to: if it is determined that branches and leaves of plants other than the bottom portion of the plants within the first sub-region are deformed, locate the corresponding first sub-region;

[0041] A footprint acquisition and judgment module is configured to: acquire bottom identification information of the corresponding first sub-region, and judge whether a footprint change occurs in the corresponding first sub-region based on the bottom identification information;

[0042] The foreign object entry warning module is used to: if it is determined that a footprint change occurs in the corresponding first sub-area, then it is determined that a foreign object has entered the corresponding first sub-area, and an alarm information is reported to prompt that branch and leaf deformation and footprint changes caused by foreign objects have occurred in the corresponding first sub-area.

[0043] An embodiment of the present invention provides a big data-based agricultural facility monitoring method and system, which obtains inspection information of a planting area, wherein the inspection information includes an edge inspection image; determines whether branches and leaves in non-bottom parts of plants in a first sub-area are deformed based on the inspection information, and the planting sub-area includes several first sub-areas; if it is determined that branches and leaves in non-bottom parts of plants in the first sub-area are deformed, the corresponding first sub-area is located; bottom identification information of the corresponding first sub-area is obtained, and whether a footprint change occurs in the corresponding first sub-area based on the bottom identification information; if it is determined that a footprint change occurs in the corresponding first sub-area, it is determined that a foreign object has entered the corresponding first sub-area, and an alarm message is reported to prompt that branches and leaves are deformed and footprint changes caused by foreign objects have occurred in the corresponding first sub-area. Even in some cases where monitoring is difficult to cover and foreign objects are not directly discovered, areas that are difficult to be discovered when crops are damaged can be identified, providing reference and suggestions for the maintenance of agricultural facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a main flow chart of an agricultural facility monitoring method based on big data.

[0045] Figure 2 The invention relates to a flow chart for instructing a second mobile inspection device to move and directly obtain edge mobile inspection images in a big data-based agricultural facility monitoring method.

[0046] Figure 3 The present invention is a flowchart for improving inspection priority according to the number of inspection priority instructions in a big data-based agricultural facility monitoring method.

[0047] Figure 4 The invention relates to a flow chart for determining the occurrence of branch and leaf deformation in non-bottom parts of plants in a first sub-area in a big data-based agricultural facility monitoring method.

[0048] Figure 5 The invention relates to a flow chart for determining the occurrence of imprint changes in a corresponding first sub-region in a method for monitoring agricultural facilities based on big data.

[0049] Figure 6 The invention relates to a flow chart for reporting damage inspection indication information in a big data-based agricultural facility monitoring method.

[0050] Figure 7 It is the main structure diagram of an agricultural facility monitoring system based on big data. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be 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.

[0052] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0053] The present invention provides a method and system for monitoring agricultural facilities based on big data, which solves the technical problems in the background technology.

[0054] like Figure 1 FIG. 1 is a main flow chart of a method for monitoring agricultural facilities based on big data according to an embodiment of the present invention. The method for monitoring agricultural facilities based on big data includes:

[0055] Step S10: Obtain inspection information of the planting area, the inspection information including edge inspection images; some economic crops are generally planted in the planting area; especially for some greenhouse areas, such as plastic greenhouses, glass greenhouses, film greenhouses, etc., or planting areas formed by fences and barriers; the edges of these planting areas are very likely to be damaged by foreign entrants, such as some wild beasts, and foreign animals, such as poultry, etc., and these foreign entrants may not be discovered; they may enter through abnormal channels, such as destroying edge protection, or they may enter through normal entrances, etc. Therefore, the inspection information may also include non-edge inspection images; edge inspection images are end point recognition images;

[0056] Step S11: Determine, based on the inspection information, whether any branch or leaf deformation occurs in a non-bottom portion of the plant within the first sub-region, where the planting sub-region includes a plurality of first sub-regions. The non-bottom portion generally includes the ground surface and the area above where the plant is located. Branch or leaf deformation may manifest as a significant displacement of the branch, such as a deviation in one direction. This may be caused by an external force, but whether this external force necessarily comes from an intruding object requires further determination, as wind or accidental factors may also cause this.

[0057] Step S12: If it is determined that the non-bottom portion of the plant in the first sub-region has branch and leaf deformation, locate the corresponding first sub-region. The purpose of this step is to locate the first sub-region to facilitate the implementation of the next step.

[0058] Step S13: Obtaining the bottom identification information of the corresponding first sub-region, and determining whether a footprint change occurs in the corresponding first sub-region based on the bottom identification information. This step is provided to further determine whether the abnormality in the first sub-region is indeed likely caused by an intrusion. The bottom identification information generally includes the surface information of the sub-region. If a footprint change occurs in the first sub-region, such as a clear footprint or a concave mark, it indicates that it is very likely caused by an intrusion.

[0059] Step S14: If a footprint change is determined to have occurred in the corresponding first sub-region, then a foreign object is determined to have entered the corresponding first sub-region, and an alarm is reported to indicate that deformation of branches and leaves and footprint changes caused by the foreign object have occurred in the corresponding first sub-region. Since both the footprint change and the branch and leaf deformation correspond to the same first sub-region, and the correlation between the two meets the destructive impact of foreign objects entering the planting area, an alarm is reported to indicate that a foreign object may have entered the corresponding first sub-region.

[0060] When this embodiment is applied, it obtains inspection information of the planting area, and the inspection information includes edge inspection images; determines whether branches and leaves in the non-bottom part of the plant in the first sub-area are deformed based on the inspection information, and the planting sub-area includes several first sub-areas; if it is determined that branches and leaves in the non-bottom part of the plant in the first sub-area are deformed, the corresponding first sub-area is located; the bottom identification information of the corresponding first sub-area is obtained, and whether a print change occurs in the corresponding first sub-area based on the bottom identification information; if it is determined that a print change occurs in the corresponding first sub-area, it is determined that a foreign object has entered the corresponding first sub-area, and an alarm information is reported to prompt that branches and leaves are deformed and print changes caused by foreign objects have occurred in the corresponding first sub-area. Even in some cases where monitoring is difficult to cover and foreign objects are not directly discovered, areas that are difficult to be discovered when crops are damaged can be identified, providing reference and suggestions for the maintenance of agricultural facilities.

[0061] As a preferred embodiment of the present invention, the method further includes:

[0062] Step S20: issuing an inspection instruction to the first mobile inspection device, wherein the inspection instruction includes inspection parameters, wherein the inspection parameters include an inspection movement speed and a movement route, wherein the movement route covers at least a plurality of first sub-areas; the first inspection device may be an inspection device mounted on a slide robot, or an inspection robot, etc.; the inspection movement in the inspection parameters may be determined based on the area of ​​the planting area, and the movement route may be determined based on a practical route that is convenient for movement, such as the location of a furrow;

[0063] Step S21: According to the inspection parameters, instruct the first mobile inspection device to move according to the inspection parameters in the inspection instruction to obtain inspection images, wherein the inspection images include edge movement inspection images.

[0064] It is understandable that, by determining the inspection parameters, when the first mobile inspection device moves according to the inspection parameters in the inspection instruction, it can directly capture images within the range covered by the inspection device to obtain inspection image information.

[0065] Furthermore, an embodiment of obtaining inspection images is proposed. The inspection equipment in this embodiment and the previous embodiment can be the same inspection equipment, that is, this embodiment can be used as an optimization of the previous embodiment; Figure 2 As shown, as a preferred embodiment of the present invention, the method further includes:

[0066] Step S30: instruct the second mobile inspection device to receive the inspection priority instruction reported by the fixed-point inspection device, wherein at least one fixed-point inspection device is provided in each first area, and when the fixed-point inspection device identifies abnormal sound information, it generates an inspection priority instruction and reports the inspection priority instruction to the second mobile inspection device, wherein the inspection priority instruction carries the area identifier where the abnormal sound information is located; for example, the mobile inspection device may be a slide rail inspection device, that is, a slide rail is provided at the top of the planting area or near the top, and the inspection device is installed on the slide rail; for another example, the mobile inspection device may be an inspection robot; the identification of abnormal sound information here mainly includes Extract the acoustic feature information from the sound and compare it with possible acoustic feature information. When the similarity between the two does not overlap, it indicates that other foreign objects may have entered the planting area, such as wild beasts or foreign animals. Possible acoustic feature information mainly includes people, wind sounds, and patrolling animals when necessary. Identifying the area where the abnormal sound information is located is conducive to improving the accuracy of the first sub-area, indicating that the area where the intrusion incident may occur is not blindly selected. When the abnormal sound information here is recognized by multiple fixed-point inspection devices, it is necessary to select the one with the loudest sound or several larger sounds as the basis for abnormal sound information identification.

[0067] Step S31: Instruct the second mobile inspection device to move to at least one first sub-area where the area identifier is located according to the area identifier in the inspection priority instruction to obtain edge mobile inspection images. Generally speaking, the at least one first sub-area where the area identifier is located only corresponds to one area identifier. However, considering the range of sound diffusion, there are generally several adjacent first sub-areas close to the edge. For example, the area identifier is S-1-4, where S stands for Side, 1 stands for counting from the position of sub-area 1, and 5 stands for counting from the position of sub-area 1, the fifth sub-area. The first sub-area determined at this time can be S-1-3, S-1-4, S-1-5, etc.

[0068] Through the implementation of this embodiment, the accuracy of edge mobile inspection image acquisition can be improved, which is conducive to reducing the subsequent recognition workload and can improve the accuracy of abnormality recognition.

[0069] like Figure 3 As shown, as a preferred embodiment of the present invention, the method further includes:

[0070] Step S40: When the number of inspection priority instructions is greater than or equal to 3 within the set time period, execute the next step;

[0071] It is understandable that when there are only two inspection priority instructions, inspections are generally performed in the order of time;

[0072] Step S41: determining whether only a certain first sub-area is within the set distance of the current first area; the value of the set distance mainly depends on the inspection speed. If the speed is low, then the set distance can be considered to be set; or the set distance can be set to be small;

[0073] Step S42: If yes, the inspection priority of the first sub-area is raised to the highest priority;

[0074] Step S43: Otherwise, the original inspection priority is maintained, wherein the original inspection priority is determined according to the generation time of the inspection priority instruction. The earlier the generation time, the higher the corresponding inspection priority.

[0075] It should be understood that when there are three or more inspection instructions, since there are more first sub-areas involved, in order to save routes and make the journey shorter when the inspection equipment is limited, the distance between at least two first sub-areas is taken into account and used as the standard for changing the priority. The reason for doing this is to avoid returning in a short time or shortening the return route, and at the same time not spending too much time inspecting a first sub-area.

[0076] like Figure 4 As shown, as a preferred embodiment of the present invention, the method further includes:

[0077] Step S50: Detect whether the bending of the branches in the plant reaches the first set deformation. If so, it is determined that the first condition is met; the first set deformation may be caused by external forces, such as wind, such as the thrust generated by the passing of foreign destructive objects, etc.; these external forces may cause the branches to bend; for example, for the main trunk or branch of a tomato, its deformation degree reaches 20 degrees away from the center (under normal circumstances, the main trunk of a tomato is equipped with a bracket).

[0078] Step S51: Detecting whether the displacement of the fruit leaves in the plant reaches a second set deformation. If so, determining that the second condition is satisfied. The second set deformation may be caused by the same reason as the first set deformation, except that the force is applied at different locations.

[0079] Step S52: When at least one of the first condition and the second condition is satisfied, it is determined that the non-bottom portion of the plant in the first sub-region has branch and leaf deformation. Only one of the first condition and the second condition needs to be satisfied, and at most both conditions can be satisfied.

[0080] When this embodiment is applied, it detects whether the bending of the branches in the plant reaches the first set deformation. If so, it determines that the first condition is met. It detects whether the displacement of the fruit leaves in the plant reaches the second set deformation. If so, it determines that the second condition is met. When at least one of the first condition and the second condition is met, it is determined that the non-bottom part of the plant in the first sub-area has branch and leaf deformation, and it can judge the possible deformation of branches, fruit leaves, etc. to identify the corresponding first sub-area, providing a basis for further combining the bottom identification information to identify whether foreign destructive objects have really entered the first sub-area.

[0081] like Figure 5 As shown in FIG. 1 , as a preferred embodiment of the present invention, the step of determining whether a print change occurs in the corresponding first sub-region according to the bottom identification information specifically includes:

[0082] Step S131: extracting surface marks and / or sinking traces from the bottom identification information; it is shown that the surface marks mainly refer to marks left on the surface inside the area, which are mainly formed by footprints; the sinking marks are also mainly formed by footprints. The main reason for the difference between the two is the difference in the surface of the planting area, such as relatively soft soil or direct film covering; the latter is more likely to form surface marks;

[0083] Step S132: Detecting whether the surface print in the bottom identification information reaches a first set area. If so, determining that the third condition is satisfied. Different set areas may be assigned to different types of foreign objects. For example, for a rabbit that intrudes, the area of ​​a single foot print is 5-8 square centimeters. The main purpose of setting this condition is to avoid prints caused by non-external factors, such as prints from falling dust.

[0084] Step S133: Detecting whether the sinking trace in the bottom identification information that is not less than the preset coverage area has reached a set depth. If so, it is determined that the fourth condition is satisfied. The main reason for the sinking trace is that the surface cannot bear the weight of the external vandalism. For example, the sinking trace in relatively soft soil reaches 2 cm.

[0085] Step S134: When at least one of the third condition and the fourth condition is satisfied, it is determined that a footprint change occurs in the corresponding first sub-region.

[0086] It can be understood that by extracting the surface marks and / or sinking traces in the bottom identification information, it is detected whether the surface marks in the bottom identification information reach the first set area, and when at least one of the surface marks and / or sinking traces changes and meets the corresponding conditions, it indicates that foreign destructive objects are very likely to have entered the planting area and formed traces on the surface.

[0087] like Figure 6 As shown, as a preferred embodiment of the present invention, the method further includes:

[0088] Step S60: When it is determined that a footprint change occurs in the corresponding first sub-area, all points where the footprint change occurs are captured; the points where the footprint change occurs are mainly the points where the surface imprint and / or the sinking trace are located. Considering that the soil layer of the planting ground in the first sub-area or the planting area may be different at different locations, the surface imprint and the sinking trace can be combined. However, when the two are combined, the difference in coverage area between the two needs to be small, otherwise the combination loses its meaning;

[0089] Step S61: Arrange all points in the order of their appearance time segments to generate an arrangement result. Arrange points in the order of their appearance time segments so that points within similar time segments are grouped into one arrangement result as much as possible, indicating that the points within this time segment are most likely left by a certain foreign destructive object during one trip.

[0090] Step S62: Determine whether there are consecutive points in the arrangement result. The consecutive points are used to indicate that the distance difference between at least two adjacent points in the arrangement result is within a threshold distance. Continuing to determine the consecutive points, that is, further confirming whether the adjacent points are from a certain trip. For the same trip, the distance between consecutive points should be limited and should be regularly and evenly distributed.

[0091] Step S63: If continuous points appear in the arrangement result, identifying whether the direction of the continuous points tends to a certain section of the protection zone in the planting area, wherein the protection zone includes a side protection zone or a top protection zone;

[0092] The direction of the continuous points here is whether it is a certain section of the regional protection area, which means that the head end or the end of the continuous points points to a certain section of the protection area. For example, the protection area is distributed in 20 sections along the length direction, and the end of the continuous points points to the 10th section, which is the interrupted part;

[0093] Step S64: If the direction of the consecutive points approaches a certain section of the protection zone within the planting area, the corresponding section of the protection zone is located, damage inspection indication information is generated based on the corresponding section of the protection zone, and the damage inspection indication information is reported. If the direction of the consecutive points approaches a certain section of the protection zone within the planting area, it indicates that foreign vandalism is likely to have entered or exited from the section corresponding to the point, but may not have been discovered yet. In this case, personnel should be prompted to inspect the section to check whether the edge protection facilities have been damaged.

[0094] When this embodiment is applied, by capturing all points where the imprint changes, all points are arranged in the order of the time segments of their appearance to generate an arrangement result, and it is determined whether continuous points appear in the arrangement result. The continuous points are used to characterize that the distance difference between at least two adjacent points in the arrangement result is within the threshold distance. If continuous points appear in the arrangement result, it is identified whether the direction of the continuous points tends to a certain section of the protection zone in the planting area. If the direction of the continuous points tends to a certain section of the protection zone in the planting area, the corresponding section of the protection zone is located, and damage inspection indication information is generated according to the corresponding section of the protection zone. The damage inspection indication information is reported, which can locate the position where foreign vandals may damage the edge protection, and provide a reliable reference for maintenance.

[0095] like Figure 7 As shown, as another preferred embodiment of the present invention, on the other hand, a big data-based agricultural facility monitoring system comprises:

[0096] An inspection information acquisition module 100 is used to acquire inspection information of a planting area, wherein the inspection information includes edge inspection images;

[0097] The deformation judgment module 200 is used to judge whether the non-bottom part of the plant in the first sub-area has branch and leaf deformation according to the inspection information, and the planting sub-area includes a plurality of first sub-areas;

[0098] The region positioning module 300 is configured to: if it is determined that branches and leaves of plants other than the bottom portion of the plants within the first sub-region are deformed, locate the corresponding first sub-region;

[0099] The footprint acquisition and judgment module 400 is used to: acquire bottom identification information of the corresponding first sub-region, and judge whether a footprint change occurs in the corresponding first sub-region according to the bottom identification information;

[0100] The foreign object entry warning module 500 is used to: if it is determined that a footprint change occurs in the corresponding first sub-area, then it is determined that a foreign object has entered the corresponding first sub-area, and an alarm information is reported to prompt that branch and leaf deformation and footprint changes caused by foreign objects have occurred in the corresponding first sub-area.

[0101] In the above embodiment of the present invention, a method for monitoring agricultural facilities based on big data is provided, and based on the method for monitoring agricultural facilities based on big data, a system for monitoring agricultural facilities based on big data is provided, which obtains inspection information of a planting area, the inspection information including edge inspection images; determines whether branches and leaves in the non-bottom part of the plant in the first sub-area are deformed based on the inspection information, and the planting sub-area includes several first sub-areas; if it is determined that branches and leaves in the non-bottom part of the plant in the first sub-area are deformed, the corresponding first sub-area is located; bottom identification information of the corresponding first sub-area is obtained, and whether a footprint change occurs in the corresponding first sub-area based on the bottom identification information; if it is determined that a footprint change occurs in the corresponding first sub-area, it is determined that a foreign object has entered the corresponding first sub-area, and an alarm information is reported to prompt that branches and leaves deformation and footprint changes caused by foreign objects have occurred in the corresponding first sub-area. Even in some cases where monitoring is difficult to cover and foreign objects are not directly discovered, areas that are difficult to be discovered when crops are damaged can be identified, providing reference and suggestions for the maintenance of agricultural facilities.

[0102] In order to enable the above-mentioned method and system to be loaded and run smoothly, in addition to the various modules mentioned above, the system may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

[0103] The processor may be a central processing unit (CPU), 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. The general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the system, connecting various parts using various interfaces and lines.

[0104] The above-mentioned memory can be used to store computer and system programs and / or modules. The above-mentioned processor implements the above-mentioned various functions by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as an information collection template display function, a product information release function, etc.). The data storage area can store data created based on the use of the berth status display system (such as product information collection templates corresponding to different product types, product information that different product providers need to release, etc.). In addition, the memory can include high-speed random access memory and can also include non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0105] It should be understood that although the various steps in the flow charts of the various embodiments of the present invention are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the various embodiments may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0106] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring agricultural facilities based on big data, characterized in that: The method comprises: Acquiring inspection information of the planting area, wherein the inspection information includes edge inspection images; determining whether branches and leaves of plants in a first sub-area are deformed at a non-bottom portion thereof based on the inspection information, wherein the planting area includes a plurality of first sub-areas; If it is determined that branches and leaves in a non-bottom portion of the plant in the first sub-region are deformed, the corresponding first sub-region is located; Acquiring bottom identification information of the corresponding first sub-region, and determining whether a print change occurs in the corresponding first sub-region according to the bottom identification information; If it is determined that a footprint change occurs in the corresponding first sub-area, it is determined that a foreign object has entered the corresponding first sub-area, and an alarm message is reported to prompt that branch and leaf deformation and footprint changes caused by the entry of foreign objects have occurred in the corresponding first sub-area.

2. The agricultural facility monitoring method based on big data according to claim 1, characterized in that: The method further comprises: issuing an inspection instruction to the first mobile inspection device, wherein the inspection instruction includes inspection parameters, the inspection parameters including an inspection movement speed and a movement route, and the movement route covers at least a plurality of first sub-areas; According to the inspection parameters, the first mobile inspection device is instructed to move according to the inspection parameters in the inspection instruction to obtain inspection images, wherein the inspection images include edge movement inspection images.

3. The agricultural facility monitoring method based on big data according to claim 1, characterized in that: The method further comprises: Instructing the second mobile inspection device to receive the inspection priority instruction reported by the fixed-point inspection device, wherein at least one fixed-point inspection device is provided in each first area, and when the fixed-point inspection device identifies abnormal sound information, generating an inspection priority instruction and reporting the inspection priority instruction to the second mobile inspection device, wherein the inspection priority instruction carries an identifier of the area where the abnormal sound information is located; The second mobile inspection device is instructed to move to at least one first sub-area where the area identifier is located according to the area identifier in the inspection priority instruction, so as to obtain an edge mobile inspection image.

4. The agricultural facility monitoring method based on big data according to claim 3, characterized in that: The method further comprises: When the number of inspection priority instructions is greater than or equal to 3 within the set time period, it is determined whether only a certain first sub-area is located within the set distance of the current first area; If yes, the inspection priority of a first sub-area is raised to the highest priority; Otherwise, the original inspection priority is maintained, wherein the original inspection priority is determined according to the generation time of the inspection priority instruction. The earlier the generation time, the higher the corresponding inspection priority.

5. The agricultural facility monitoring method based on big data according to claim 1, characterized in that: The method further comprises: detecting whether the bending of the branches of the plant reaches a first set deformation, and if so, determining that the first condition is satisfied; detecting whether the displacement of the fruit leaves in the plant reaches a second set deformation, and if so, determining that the second condition is satisfied; When at least one of the first condition and the second condition is satisfied, it is determined that the non-bottom portion of the plant in the first sub-region has branch and leaf deformation.

6. The agricultural facility monitoring method based on big data according to claim 1, characterized in that: The specific report of determining whether a print change occurs in the corresponding first sub-area according to the bottom identification information includes: extracting the surface printing mark and / or sinking trace in the bottom identification information; detecting whether the surface printing mark in the bottom identification information reaches a first set area, and if so, determining that the third condition is satisfied; detecting whether the sinking trace in the bottom identification information that is not less than the preset covering area reaches a set depth, and if so, determining that the fourth condition is satisfied; When at least one of the third condition and the fourth condition is satisfied, it is determined that a footprint change occurs in the corresponding first sub-region.

7. The agricultural facility monitoring method based on big data according to any one of claims 1 to 6, characterized in that: The method further comprises: When it is determined that a footprint change occurs in the corresponding first sub-region, all points where the footprint change occurs are captured; Arrange all points in the order of their appearance time segments to generate arrangement results; Determining whether there are continuous points in the arrangement result, wherein the continuous points are used to indicate that a distance difference between at least two adjacent points in the arrangement result is within a threshold distance; If continuous points appear in the arrangement result, identifying whether the direction of the continuous points tends to a certain section of the protection zone in the planting area, wherein the protection zone includes a side protection zone or a top protection zone; If the direction of the continuous points tends to a certain section of the protection zone in the planting area, the corresponding section of the protection zone is located, damage inspection indication information is generated according to the corresponding section of the protection zone, and the damage inspection indication information is reported.

8. An agricultural facility monitoring system based on big data, characterized in that: The system comprises: An inspection information acquisition module is used to acquire inspection information of the planting area, wherein the inspection information includes edge inspection images; A deformation judgment module is used to judge whether branches and leaves in non-bottom parts of plants in a first sub-area are deformed according to the inspection information, wherein the planting area includes a plurality of first sub-areas; The region positioning module is configured to: if it is determined that branches and leaves of plants other than the bottom portion of the plants within the first sub-region are deformed, locate the corresponding first sub-region; A footprint acquisition and judgment module is configured to: acquire bottom identification information of the corresponding first sub-region, and judge whether a footprint change occurs in the corresponding first sub-region based on the bottom identification information; The foreign object entry warning module is used to: if it is determined that a footprint change occurs in the corresponding first sub-area, then it is determined that a foreign object has entered the corresponding first sub-area, and an alarm information is reported to prompt that branch and leaf deformation and footprint changes caused by foreign objects have occurred in the corresponding first sub-area.

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