Harvester, harvester intelligent control method and system based on Internet of Things, storage medium and electronic equipment

Abstract

The invention discloses a harvester, a harvester intelligent control method and system based on the Internet of Things, a storage medium and electronic equipment, the harvester comprises a harvester main body, the upper end of the harvester main body is provided with a material storage trolley, and the material storage trolley and the harvester main body are connected through two sets of connecting assemblies; and an inclined plate is rotationally installed at the tail end of the harvester body and rotates through a driving mechanism, and when one end of the inclined plate rotates to make contact with the ground, the storage trolley can transport the grains stored in the storage trolley. The storage trolley capable of flexibly operating is installed on the harvester body, harvested grains are stored, the storage trolley is separated from the harvester when the inclined plate makes contact with the ground, the step of additional shutdown and discharging of an existing harvester is optimized through the modular design, the harvesting efficiency is improved, and the harvesting cost is reduced. The storage trolley is provided with an automatic hooking and unlocking device, and the connecting and separating process between the trolley and the harvester body is simplified.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural machinery control, and particularly to a harvester, an intelligent control method and system for a harvester based on the Internet of Things, a storage medium, and an electronic device. Background Art

[0002] At present, as an important part of agricultural mechanization, harvesters have evolved from the early single cutting function to a multi-functional combined machine integrating cutting, transportation, threshing, and weed removal, greatly improving agricultural production efficiency. The prior art adopts an agricultural harvesting assistance system, which realizes the effective separation and recycling of straw through components such as a crushing box and a filter belt conveyor, avoiding environmental pollution problems caused by burning straw. At the same time, the existing methods also adopt auxiliary wheels and magnet fixing devices to enhance the stability and adaptability of the harvester during operation, ensuring stable harvesting work under different terrain conditions. These technological advancements reflect the continuous optimization of harvester mechanical design, aiming to meet the requirements of modern agricultural production for high efficiency and environmental protection.

[0003] In the prior art, the functionality and applicability of harvesters have been enhanced through improvements in mechanical structures, but there are still deficiencies in intelligent management and flexibility. Most existing harvesters rely on their inherent mechanical characteristics to improve production efficiency and fail to make full use of Internet of Things technology for deeper intelligent control. Specifically, current harvesters lack the ability of real-time monitoring and intelligent regulation and cannot dynamically adjust working parameters such as cutting height and threshing force according to crop types, which directly affects the quality and efficiency of harvesting operations. In addition, due to the lack of an efficient path planning function, harvesters cannot drive along the optimal route during field operations, resulting in waste of time and fuel resources. For large-scale farms, traditional harvesters cannot provide refined operation task management and cannot meet the diverse and personalized needs of agricultural production.

[0004] In summary, in the prior art, the Internet of Things system cannot be used to improve harvesting efficiency and achieve automated crop harvesting. Summary of the Invention

[0005] The present invention provides a harvester, an intelligent control method and system for a harvester based on the Internet of Things, a storage medium, and an electronic device, which use the Internet of Things system to improve harvesting efficiency and achieve automated crop harvesting.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a harvester, including a harvester main body, on the upper end of which a storage trolley is installed. The storage trolley is connected to the harvester main body through two sets of connection components, and a sloping plate is rotatably installed at the tail end of the harvester main body. The sloping plate is rotated by a driving mechanism. When one end of the sloping plate rotates to contact the ground, the storage trolley can transport the stored grain inside.

[0007] Preferably, in an alternative embodiment, according to the connection component including an outer plate connected to one side of the storage trolley, a hook is rotatably arranged inside the outer plate, a positioning groove is opened on the upper side of the hook, a cylinder is installed on the outer plate, and the piston end of the cylinder is fixedly connected to a positioning plate. The positioning plate penetrates through a through hole opened on the upper side of the outer plate and is inserted and matched with the positioning groove.

[0008] Preferably, in an alternative embodiment, according to the driving mechanism including a first rotating rod rotatably connected to the tail end of the harvester main body, the first rotating rod is fixedly connected to one end of the sloping plate, a first bevel gear is fixedly connected to the first rotating rod, a second rotating rod is rotatably arranged on one side of the harvester main body, and second bevel gears are connected to both the upper and lower ends of the second rotating rod. The second bevel gear located at the upper end meshes with the first bevel gear, a lead screw is rotatably arranged at the bottom of the harvester main body, and a third bevel gear is connected to one end of the lead screw. The third bevel gear meshes with the second bevel gear located at the lower end.

[0009] Preferably, in an alternative embodiment, according to the lead screw, a support plate is threadedly connected thereto, and the upper end of the support plate slides in a limit groove opened at the bottom of the harvester main body.

[0010] Preferably, in an alternative embodiment, according to the storage trolley, a conical hopper is arranged inside, the discharge port of the conical hopper corresponds to the feed port of a feeding auger installed at the bottom of the storage trolley, and a feed port is opened at the upper end of the storage trolley.

[0011] In a second aspect, the present invention also provides an intelligent control method for a harvester based on the Internet of Things, including:

[0012] Obtaining the environmental information and crop information of the operation area;

[0013] According to the environmental information and the crop information, constructing an operation area model and performing a harvest priority calculation operation to obtain an operation area model and a crop harvest priority;

[0014] According to the operation area model and the crop harvest priority, performing a harvest path planning operation to obtain a crop harvest path;

[0015] According to the operation area model and the crop harvest path, performing a crop model construction operation to obtain a crop model;

[0016] According to the crop model, perform an operation for calculating working parameters to obtain the working parameters;

[0017] According to the working parameters, the crop harvesting path, and the operation area model, perform a crop harvesting operation to achieve harvester control.

[0018] Preferably, in an alternative embodiment, obtain the environmental information and crop information of the operation area, including:

[0019] Through the positioning module, obtain the position coordinate information of the operation area;

[0020] Through the harvesting management system, obtain the crop information of the operation area;

[0021] According to the position coordinate information, perform an operation for obtaining terrain information to obtain the terrain information of the operation area;

[0022] According to the position coordinate information and the terrain information, perform an operation for processing environmental information to obtain the environmental information of the operation area.

[0023] Preferably, in an alternative embodiment, according to the environmental information and the crop information, construct an operation area model and perform an operation for calculating the harvesting priority to obtain the operation area model and the crop harvesting priority, including:

[0024] According to the environmental information and the crop information, perform an operation for constructing an operation area model to obtain the operation area model;

[0025] According to the operation area model, perform an operation for generating operation tasks to obtain the operation tasks;

[0026] According to the operation tasks and the crop information, perform an operation for calculating the harvesting priority to obtain the crop harvesting priority;

[0027] The calculation formula for the harvesting priority calculation is as follows:

[0028] P1 = C × (M × T × A1)

[0029] Wherein, P1 represents the crop harvesting priority, C represents the crop type priority coefficient, M represents the crop maturity index, T represents the crop timeliness index, and A1 represents the crop area ratio.

[0030] Preferably, in an alternative embodiment, according to the operation area model and the crop harvesting priority, perform an operation for planning the harvesting path to obtain the crop harvesting path, including:

[0031] According to the operation area model and the crop harvesting priority, perform an operation for calculating the harvesting area priority to obtain the harvesting area priority;

[0032] According to the harvesting area priority and the operation area model, perform a harvesting path planning operation to obtain a crop harvesting path;

[0033] The calculation formula for the harvesting area priority is as follows:

[0034]

[0035] where P2 represents the harvesting area priority, A2 represents the crop area, k att represents the attraction coefficient, k rep represents the repulsion coefficient, d obs represents the obstacle distance, d goal represents the target point distance, σ represents the standard deviation, and B represents the balance coefficient.

[0036] Preferably, in an alternative embodiment, according to the operation area model and the crop harvesting path, perform a crop model construction operation to obtain a crop model, including:

[0037] According to the operation area model and the crop harvesting path, perform an image acquisition operation to obtain first crop image information;

[0038] According to the crop image information, perform an image processing operation to obtain second crop image information;

[0039] According to the second crop image information, perform a crop model construction operation to obtain a crop model.

[0040] Preferably, in an alternative embodiment, according to the crop model, perform a working parameter calculation operation to obtain working parameters, including:

[0041] According to the crop model, obtain preset working parameters through a harvesting management system to obtain working parameters;

[0042] According to the working parameters, obtain harvested crop information and harvester operation information through a real-time acquisition module;

[0043] According to the harvested crop information and the harvester operation information, perform a working parameter calculation operation to obtain working parameters.

[0044] Preferably, in an alternative embodiment, according to the working parameters, the crop harvesting path, and the operation area model, perform a crop harvesting operation to achieve harvester control, including:

[0045] According to the crop harvesting path and the operation area model, use an automatic navigation system to drive the harvester to the target harvesting area;

[0046] According to the working parameters, perform a working parameter output operation to obtain the operating parameters of the harvester;

[0047] According to the operating parameters of the harvester, perform a crop harvesting operation to achieve harvester control.

[0048] In a third aspect, the present invention provides an intelligent control system for a harvester based on the Internet of Things, including:

[0049] A data acquisition module for acquiring the environmental information and crop information of the operation area;

[0050] A data preprocessing module for constructing an operation area model and performing a harvesting priority calculation operation according to the environmental information and the crop information to obtain the operation area model and the crop harvesting priority;

[0051] A path planning module for performing a harvesting path planning operation according to the operation area model and the crop harvesting priority to obtain a crop harvesting path;

[0052] A crop model generation module for performing a crop model construction operation according to the operation area model and the crop harvesting path to obtain a crop model;

[0053] A parameter calculation module for performing a working parameter calculation operation according to the crop model to obtain working parameters;

[0054] An execution module for performing a crop harvesting operation according to the working parameters, the crop harvesting path, and the operation area model to achieve harvester control.

[0055] In a fourth aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements an intelligent control method for a harvester based on the Internet of Things as described in any one of the above.

[0056] In a fifth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute an intelligent control method for a harvester based on the Internet of Things as described in any one of the above.

[0057] Compared with the prior art, the present invention has the following beneficial effects: By constructing a harvester, an intelligent control method, system, storage medium and electronic device for the harvester based on the Internet of Things, automated crop harvesting is achieved. First, the technical solution obtains the position coordinate information, crop information and terrain information of the operation area through the positioning module and the harvesting management system, and performs environmental information processing operations, so as to accurately grasp the actual situation of the operation area. On this basis, according to the collected environmental information and crop information, a detailed operation area model is constructed and the harvesting priority calculation operation is carried out. Not only various factors such as crop type, maturity, timeliness and area ratio are considered, but also the crop harvesting priority is quantified by a specific formula P1 = C×(M×T×A1), ensuring the scientificity and rationality of the operation task allocation. Further, based on the constructed operation area model and the calculated crop harvesting priority, the harvesting path planning operation is carried out, and the harvesting area priority calculation formula and the path planning algorithm are adopted to determine the optimal crop harvesting path. This process takes into account factors such as harvesting area priority, crop area, and obstacle distance, enabling the harvester to travel along an efficient route in the field, avoiding unnecessary movement time and fuel consumption. In addition, the present invention also constructs a crop model through image acquisition and processing operations. After obtaining a detailed crop model, by real-time collecting the information of the harvested crops and the operation information of the harvester, the working parameters such as cutting height and threshing force are dynamically adjusted to meet the requirements of different crops. The harvester accurately drives into the target area and performs crop harvesting operations according to these precise working parameters and the pre-planned harvesting path, realizing the intelligent control of the harvester. This intelligent control system for the harvester can not only improve the operation efficiency and service quality, but also reduce the degree of manual intervention and achieve the refined management of agricultural production.

[0058] In summary, in view of the deficiencies of the existing harvesters in terms of intelligent management and harvesting efficiency, the present invention proposes an intelligent control method and system based on the Internet of Things, aiming to achieve automated crop harvesting. The present invention accurately obtains the position, crop and terrain information of the operation area by integrating the positioning module and the harvesting management system, and constructs a detailed operation area model. By using the harvesting priority calculation formula to comprehensively consider crop type, maturity, timeliness and area ratio, the scientificity of task allocation is ensured. The path planning algorithm is adopted to determine the optimal harvesting path according to the operation area model and the priority, enabling the harvester to travel along the most economical route. At the same time, a crop model is constructed through image processing, and the crop and harvester operation information is collected in real time to dynamically adjust the working parameters such as cutting height and threshing force, realizing intelligent control and improving the operation efficiency and service quality. This intelligent control system not only improves the operation efficiency and service quality, reduces the degree of manual intervention, but also realizes the refined management of agricultural production.

[0059] Compared with the prior art, the present invention has the following beneficial effects: By installing a flexible storage trolley on the harvester main body to store the harvested grain, and enabling the storage trolley to be separated from the harvester when the inclined plate contacts the ground. Through this modular design, the steps of additional downtime for discharging of the existing harvester are optimized, improving the harvesting efficiency. The storage trolley is equipped with an automatic hook hanging and unlocking device, which not only simplifies the connection and separation process between the trolley and the harvester main body, but also ensures that the entire system can operate smoothly without human intervention, further enhancing the automation level and operation flexibility of the system. Description of the Drawings

[0060] Figure 1 is a schematic flowchart of an intelligent control method for a harvester based on the Internet of Things provided by the second embodiment of the present invention;

[0061] Figure 2 is a schematic structural diagram of an intelligent control system for a harvester based on the Internet of Things provided by the third embodiment of the present invention;

[0062] Figure 3 is a schematic overall structure diagram provided by the first embodiment of the present invention;

[0063] Figure 4 is a schematic side view of the overall structure provided by the first embodiment of the present invention;

[0064] Figure 5 is a schematic structural diagram of the storage trolley provided by the first embodiment of the present invention;

[0065] Figure 6 is a schematic structural diagram of the connection component provided by the first embodiment of the present invention;

[0066] Figure 7 is a split view of the connection component structure provided by the first embodiment of the present invention;

[0067] Figure 8 is a schematic structural diagram of the driving mechanism provided by the first embodiment of the present invention;

[0068] Figure 9 is provided by the first embodiment of the present invention Figure 8 Schematic diagram of the structure at location A.

[0069] Reference numerals: 1, harvester main body; 2, storage trolley; 21, book material auger; 3, conical hopper; 4, connection component; 41, outer plate; 42, hook; 43, cylinder; 44, positioning plate; 45, through hole; 46, positioning groove; 5, inclined plate; 6, rotating rod 1; 7, bevel gear 1; 8, rotating rod 2; 9, bevel gear 2; 10, bevel gear 3; 11, support plate; 12, limiting groove; 13, lead screw. Detailed Embodiments

[0070] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0071] Refer to Figures 3 to 9 As shown, the embodiments of the present invention provide a harvester, including:

[0072] The harvester main body 1. The harvester main body 1 uses crawler tracks, which can provide more stable support. Especially when driving at high speed or performing complex operations, it improves the operation safety and stability of the equipment. The front end of the harvester main body 1 is equipped with a cutting table. According to crop types such as wheat and corn, the cutting table may be configured with different cutting devices. The cut crops are quickly conveyed backward to the next processing stage inside the machine through the screw conveyor or conveyor belt on the cutting table. The crops entering the machine first pass through the feeding roller or conveying chain, and these devices are responsible for evenly feeding the crops into the threshing device. Some larger straws and other light impurities are removed by using the principles of gravity and air separation. The crops are fed into a threshing system composed of a high-speed rotating threshing drum and a concave sieve fixed below it. The threshing drum is equipped with racks or other forms of threshing elements. When the crops pass through the gap between the drum and the concave sieve, the grains are knocked off from the ear axis, while the straw continues to move forward. The knocked-off grains together with fine debris fall into the cleaning system below. This usually includes a powerful fan and a series of vibrating sieves with different pore sizes. The airflow generated by the fan helps to blow away lighter impurities such as dust and broken straws, and the vibrating sieves further screen out clean grains according to the particle size. The clean grains after cleaning finally fall into the storage trolley 2 at the tail of the harvester main body 1 for temporary storage.

[0073] The upper end of the storage trolley 2 is provided with a feeding port, which corresponds to the discharging port of the harvester main body 1, so that the cleaned grain enters the storage trolley 2. And a conical hopper 3 is arranged inside the storage trolley 2, and the discharging port of the conical hopper 3 corresponds to the feeding port of the feeding auger 21 installed at the bottom of the storage trolley 2. The feeding auger 21 can discharge the grain in the storage trolley 2 for bagging.

[0074] In order to achieve the quick connection and separation between the storage trolley 2 and the harvester main body 1, two sets of connection components 4 are adopted in this design. Each set of connection components 4 includes key components such as an outer plate 41, a hook 42, a cylinder 43, a positioning plate 44, etc. That is, each connection component 4 contains an outer plate 41, which is respectively fixed on one side of the storage trolley 2 and the harvester main body 1. An inner-rotating hook 42 is arranged inside each outer plate 41. A positioning groove 46 is opened on the upper side of the hook 42 for subsequent locking operations. A cylinder 43 is installed on the outer plate 41, and its piston end is fixedly connected to the positioning plate 44. When it is necessary to connect the storage trolley 2 and the harvester main body 1, the two hooks 42 approach each other and hook together. At this time, due to the mutual acting force, the two hooks 42 will slide inward. The cylinder 43 is used to push the positioning plate 44 downward, so that it passes through the through hole 45 opened on the upper side of the outer plate 41 and is inserted and matched with the positioning groove 46 on the hook 42. This action locks the hook 42 to prevent its movement and ensures the firm connection between the storage trolley 2 and the harvester main body 1.

[0075] When it is necessary to separate the storage trolley 2 and the harvester main body 1, only need to control the cylinder 43 to contract, so that the positioning plate 44 withdraws from the positioning groove 46, releasing the lock on the hook 42. Subsequently, the hook 42 is no longer restricted and can rotate or slide freely, thus allowing the storage trolley 2 and the harvester main body 1 to be quickly separated. By utilizing the hooking function of the hook 42 and combining with the positioning plate 44 driven by the cylinder 43 for locking, the firm and convenient connection and separation between the storage trolley 2 and the harvester main body 1 are realized. It not only improves the working efficiency but also meets the requirements of unmanned operation, making the whole system more flexible and efficient.

[0076] In order to ensure that the storage trolley 2 can move up and down the harvester main body 1 smoothly and achieve quick separation and loading, a rotatable inclined plate 5 is installed at the tail end of the harvester main body 1. The inclined plate 5 is rotated through a set of precise driving mechanisms. When one end of it rotates to contact the ground, it provides a stable getting-off passage for the storage trolley 2, thus reducing the time wasted by the traditional harvester main body 1 due to additional downtime for discharging materials and significantly improving the overall operation efficiency.

[0077] Specifically, the inclined plate 5 is rotatably connected to the tail end of the harvester main body 1 through the first rotating rod 6. One end of the first rotating rod 6 is fixedly connected to the inclined plate 5, and the other end is provided with a first bevel gear 7. On one side of the harvester main body 1, a second rotating rod 8 is rotatably arranged, and bevel gears 9 are respectively connected to its upper and lower ends. The bevel gear 9 located at the upper end meshes with the first bevel gear 7 to form the first-stage transmission. A lead screw 13 is also rotatably arranged at the bottom of the harvester main body 1. One end of it is connected to a third bevel gear 10, and the third bevel gear 10 meshes with the bevel gear 9 located at the lower end to form the second-stage transmission. When it is necessary for the storage trolley 2 to drop from the harvester main body 1, the lead screw 13 is rotated by using an electric motor. The lead screw 13 drives the third bevel gear 10 to rotate, and then successively rotates the bevel gear 9 and the first bevel gear 7. This continuous gear transmission ultimately causes the first rotating rod 6 to rotate, making the inclined plate 5 rotate downward until it contacts the ground, forming a stable getting-off passage for the storage trolley 2 to move safely.

[0078] When the inclined plate 5 does not rotate, it is perpendicular to the ground and plays a role in blocking the storage trolley 2 to prevent it from accidentally slipping or moving. By cleverly using the bevel gear transmission system, the automatic rotation of the inclined plate 5 is realized, providing convenience for the loading and unloading of the storage trolley 2. It not only optimizes the operation process of the harvester, reduces the downtime, but also enhances the automation level of the system, meeting the requirements of modern agriculture for efficient and intelligent equipment. In addition, the design of the inclined plate 5 also takes safety into consideration, ensuring that the storage trolley 2 will not move accidentally in the non-use state.

[0079] To further enhance the stability of the inclined plate 5 and ensure that the storage trolley 2 can move up and down on the harvester main body 1 safely and smoothly, a support plate 11 is threadedly connected to the lead screw 13. When the lead screw 13 rotates, due to the effect of the thread, the support plate 11 will move up and down along the lead screw 13. The upper end of the support plate 11 is slidably installed in the limit groove 12 opened at the bottom of the harvester main body 1. The limit groove 12 not only provides a guiding function for the support plate 11, but also restricts it to slide only along the predetermined path, ensuring the accuracy and stability of the movement. When it is necessary to lower the inclined plate 5 to form a getting-off passage for the storage trolley 2, the electric motor drives the lead screw 13 to rotate. As the lead screw 13 rotates, the support plate 11 gradually slides out of the limit groove 12 and finally extends to the lower side of the inclined plate 5 to firmly support it.

[0080] Referring to Figure 1 , the second embodiment of the present invention provides an intelligent control method for a harvester based on the Internet of Things, including the following steps:

[0081] S11, obtaining the environmental information and crop information of the operation area;

[0082] S12. Based on the environmental information and the crop information, construct an operation area model and perform an operation for calculating the harvesting priority to obtain the operation area model and the crop harvesting priority;

[0083] S13. Based on the operation area model and the crop harvesting priority, perform an operation for planning the harvesting path to obtain the crop harvesting path;

[0084] S14. Based on the operation area model and the crop harvesting path, perform an operation for constructing the crop model to obtain the crop model;

[0085] S15. Based on the crop model, perform an operation for calculating the working parameters to obtain the working parameters;

[0086] S16. Based on the working parameters, the crop harvesting path and the operation area model, execute the crop harvesting operation to achieve harvester control.

[0087] It should be noted that this method realizes the efficient and automated operation of the harvester through a series of steps. First, the environmental information and crop information of the operation area are accurately obtained through the positioning module and the harvesting management system, including key data such as position coordinates, crop types, and maturity. Then, an operation area model is constructed using this information, and a specific calculation formula is used to determine the harvesting priority of the crops to ensure the reasonable allocation of harvesting tasks. Based on the model construction and priority calculation, the optimal harvesting path is further determined through a path planning algorithm to reduce the ineffective movement of the harvester and improve the operation efficiency. At the same time, a crop model is constructed through image acquisition and processing technology to provide more accurate crop information for the harvester. After obtaining the crop model, the system dynamically adjusts the working parameters, such as cutting height and threshing force, according to the real-time collected crop and harvester operation information to meet the requirements of different crops. Finally, the harvester accurately drives into the target area and executes the crop harvesting operation according to the adjusted working parameters and the pre-planned harvesting path through the automatic navigation system.

[0088] In step S11, obtain the environmental information and crop information of the operation area, including:

[0089] Obtain the position coordinate information of the operation area through the positioning module;

[0090] Obtain the crop information of the operation area through the harvesting management system;

[0091] According to the position coordinate information, perform an operation for obtaining the terrain information to obtain the terrain information of the operation area;

[0092] According to the position coordinate information and the terrain information, perform an operation for processing the environmental information to obtain the environmental information of the operation area.

[0093] It should be noted that in the intelligent control method of the present invention, the implementation of step S11 is completed through precise positioning and data acquisition technologies. First, the harvester obtains the position coordinate information of the operation area through its positioning module. This module includes a satellite positioning sensor. Exemplarily, the satellite positioning sensor adopts the GPS positioning system. Of course, according to different actual applications and user requirements, the satellite positioning sensor can also adopt the Beidou system, etc., and the present invention does not limit this. The positioning module is installed in the driving part of the agricultural machinery and receives satellite signals in real time to determine the precise position of the agricultural machinery. If the coordinate accuracy does not reach the expectation, the system will adopt a ground ranging acquisition device, including a ranging camera and a ranging radar, to obtain more detailed coordinate data. These devices are installed at the bottom of the agricultural machinery and can provide the distribution and distance information of the crops around the agricultural machinery to ensure the accuracy of the operation. The obtained position coordinate information and terrain information will be used to construct an operation area model. This process involves a detailed analysis of the terrain, including slope, altitude, etc. These information are used to evaluate the passability and operation difficulty of the harvester. Subsequently, the system will perform an environmental information processing operation, integrating the position coordinate information and the terrain information to form the environmental information of the operation area.

[0094] It is worth noting that the wireless communication module uses the Zigbee protocol for communication to ensure safe and reliable transmission between multi-level nodes. The Zigbee protocol, with its characteristics of low power consumption and low data rate, is very suitable for the communication requirements between Internet of Things devices. This includes sending signal quality detection messages through the wireless communication module, obtaining wireless communication parameters, and establishing a data structure for the position data to improve the transmission speed and efficiency of wireless communication. Through these steps, the intelligent control system of the harvester can obtain the detailed environment and crop information of the operation area in real time, providing basic data for subsequent operation area model construction, harvesting priority calculation, path planning, and crop harvesting operations.

[0095] In step S12, according to the environmental information and the crop information, an operation area model is constructed and a harvesting priority calculation operation is performed to obtain an operation area model and a crop harvesting priority, including:

[0096] According to the environmental information and the crop information, an operation area model construction operation is performed to obtain an operation area model;

[0097] According to the operation area model, an operation task generation operation is performed to obtain an operation task;

[0098] According to the operation task and the crop information, a harvesting priority calculation operation is performed to obtain a crop harvesting priority;

[0099] The calculation formula for the harvesting priority is as follows:

[0100] P1 = C × (M × T × A1)

[0101] Among them, P1 represents the crop harvesting priority, C represents the crop type priority coefficient, M represents the crop maturity index, T represents the crop timeliness index, and A1 represents the crop area ratio.

[0102] It should be noted that in step S12, a job area model is constructed and the harvesting priority is calculated. The implementation of this step first depends on the accurate environmental information and crop information obtained from step S11. Using these data, the system will construct a detailed job area model, which will include key information such as the types, maturity, distribution density, and terrain features of the crops. The construction of the job area model is a comprehensive processing process, which involves Geographic Information System (GIS) technology. Through this technology, the location coordinate information and terrain information can be integrated into a detailed map, providing a comprehensive view of the job area for the harvester. Based on this model, the system will generate specific job tasks, and these tasks will be sorted according to the maturity, type, and distribution of the crops to ensure that the harvesting operation can be carried out in the most optimized order. Next, the system will perform the operation of calculating the harvesting priority. This operation uses the harvesting priority calculation formula, which ensures that the priority allocation of the harvesting operation is both scientific and reasonable and can be adjusted according to the different characteristics and requirements of the crops. In the actual application of a paddy field, the system will determine the harvesting order according to the maturity and distribution density of the rice. The rice in some areas has a higher maturity, and these areas will be given a higher priority to ensure that the rice can be harvested in time and avoid the quality decline caused by too high maturity. At the same time, the system will also consider the complexity of the terrain and avoid wasting resources in steep or difficult-to-pass areas. Through this accurate priority calculation, the intelligent control system of the harvester can achieve efficient management of the harvesting operation and ensure the accuracy and intelligence of the operation.

[0103] It should be noted that the operation of calculating the harvesting priority is achieved through the application of an accurate formula. This formula comprehensively considers the maturity, timeliness, area ratio, and crop type of the crops to ensure that the priority allocation of the harvesting operation is both scientific and reasonable. Crop type priority coefficient, this parameter is used to distinguish the harvesting priorities of different crop types. Exemplarily, the crop type priority coefficient of grains is set to 1.0, the crop type priority coefficient of legumes is set to 0.8, and the crop type priority coefficient of other crops is set to 0.6. Of course, according to different actual applications and user requirements, the crop type priority coefficient can also be set to other values to reflect the economic value and maturity cycle of different crops, and the present invention does not limit this. Crop maturity index, this parameter reflects the degree of crop maturity and ranges from 0 to 1. Exemplarily, when the crop maturity is 80%, the crop maturity index is set to 0.8. The crop maturity index changes as the crop growth cycle progresses to ensure that crops with a higher maturity are harvested first. Crop timeliness index, this parameter considers the urgency of crop harvesting. Exemplarily, for crops that are about to overripen, the crop timeliness index is set to 1.0, while for crops with a longer maturity period, the crop timeliness index is set to 0.5. Of course, according to different actual applications and user requirements, the crop timeliness index can also be set to other values, and the present invention does not limit this. This parameter helps the system to arrange the operation sequence according to the urgency of harvesting when facing multiple crops. Crop area ratio, this parameter represents the area ratio of a specific crop in the operation area. The crop area ratio helps the system to adjust the scale and sequence of the harvesting operation according to the distribution area of the crops.

[0104] In step S13, according to the operation area model and the crop harvesting priority, a harvesting path planning operation is performed to obtain a crop harvesting path, including:

[0105] According to the operation area model and the crop harvesting priority, a harvesting area priority calculation operation is performed to obtain a harvesting area priority;

[0106] According to the harvesting area priority and the operation area model, a harvesting path planning operation is performed to obtain a crop harvesting path;

[0107] The calculation formula for the harvesting area priority is as follows:

[0108]

[0109] Among them, P2 represents the harvesting area priority, A2 represents the crop area, k att represents the attraction coefficient, k rep represents the repulsion coefficient, d obs represents the obstacle distance, d goalThe distance to the target point is represented by [[ID=]], the standard deviation is represented by σ, and the balance coefficient is represented by B.

[0110] It should be noted that in step S13, the harvesting path planning operation is implemented by introducing the APF-FMT algorithm, which combines the advantages of the artificial potential field method (APF) and the fast marching tree (FMT) to optimize the movement path of the harvester in a complex farmland environment. This process first involves the calculation of the priority of the harvesting area, which is completed based on the operation area model and the crop harvesting priority. The formula for calculating the priority of the harvesting area assigns a priority value to each operation area. The APF-FMT algorithm uses these priority values and, through the concept of the artificial potential field method, defines an attractive force field for the harvester to guide it towards the crop area with a high priority while avoiding obstacles. In this potential field, the movement tendency of the harvester is affected by the distance to the target point and the distance to the obstacle, and these factors are adjusted through the attractive force coefficient and the repulsive force coefficient. Subsequently, the FMT algorithm is used to further optimize the path search process. The FMT algorithm quickly finds the shortest path from the current position to the target position by constructing a search tree. In the application of the harvester, the FMT algorithm takes into account the terrain features, crop distribution in the operation area model, and the priority calculated by APF, thereby planning an efficient harvesting path. Through the APF-FMT algorithm, the harvester can calculate the optimal harvesting path according to the operation area model and the crop harvesting priority. This includes considering factors such as crop area, attractive force coefficient, repulsive force coefficient, obstacle distance, target point distance, standard deviation, and balance coefficient. This precise path planning ensures that the harvester can move efficiently in the field, avoid repeated operations or missed areas, while reducing the time consumption in complex terrains, improving the overall efficiency and quality of the harvesting operation.

[0111] It should be noted that when calculating the priority of the harvesting area, each parameter has specific example values, but can be adjusted according to actual applications and user requirements. Crop area, which is an indicator to measure the importance of a specific area. Exemplarily, if the crop area of an area is 1000 square meters, the A2 parameter is set to 1000. Depending on different actual applications and user requirements, the crop area can also be set to other values to reflect the crop distribution in different areas, and the present invention does not limit this. Attraction coefficient, this parameter is used to adjust the attraction of the harvester to the target area. Exemplarily, the attraction coefficient is set to 1.5 to indicate a strong attraction to the target area. Of course, depending on different actual applications and user requirements, the attraction coefficient can also be set to other values to adapt to different operating environments. Repulsion coefficient, this parameter is used to adjust the repulsive force of the harvester to obstacles. Exemplarily, the repulsion coefficient is set to 0.5 to indicate a moderate repulsive force to obstacles. Depending on different actual applications and user requirements, the repulsion coefficient can also be set to other values to ensure that the harvester can effectively avoid obstacles. Obstacle distance, which is the distance between the harvester and the nearest obstacle. Exemplarily, if the obstacle distance is 5 meters, the obstacle distance is set to 5. This value will change according to the actual distribution of obstacles. Target point distance, which is the distance between the harvester and the target crop area. Exemplarily, if the target point distance is 30 meters, the target point distance parameter is set to 30. This value will change according to the actual position of the target area. Standard deviation, this parameter is used to adjust the distribution range of the repulsive force. The larger the standard deviation, the wider the influence range of the repulsive force, and vice versa. This allows the model to adjust the influence of the repulsive force according to the distribution characteristics of the obstacles. Exemplarily, the standard deviation is set to 2 meters to indicate that the repulsive force is effective within a range of 2 meters around the obstacle. Depending on different actual applications and user requirements, the standard deviation can also be set to other values to adapt to different obstacle distributions. Balance coefficient, which can be obtained through experiments or simulations, this parameter is used to balance the influence of the attraction and the repulsive force in the formula. By adjusting the balance coefficient, the dominant position of the attraction in path planning can be controlled. If the balance coefficient is small, the influence of the attraction will be more significant; if the balance coefficient is large, the influence of the repulsive force will be relatively enhanced. Exemplarily, the balance coefficient is set to 0.1 to ensure that the attraction dominates in path planning. Depending on different actual applications and user requirements, the balance coefficient can also be set to other values to achieve different path planning strategies. Through the specific settings of these parameters, the APF-FMT algorithm can calculate the optimal harvesting path according to the operation area model and the crop harvesting priority.

[0112] In step S14, according to the operation area model and the crop harvesting path, a crop model construction operation is performed to obtain a crop model, including:

[0113] Perform an image acquisition operation based on the operation area model and the crop harvesting path to obtain the first crop image information;

[0114] Perform an image processing operation based on the crop image information to obtain the second crop image information;

[0115] Perform a crop model construction operation based on the second crop image information to obtain a crop model.

[0116] It should be noted that in step S14, the crop model construction operation is implemented based on the detailed information of the operation area model and the crop harvesting path. This step is used to improve the accuracy of the harvester's crop recognition. First, the system uses image acquisition devices installed on the harvester, such as high-resolution cameras and infrared sensors, to obtain the image information of the crops in the operation area. These images contain features such as the color, shape, size, and distribution of the crops, providing basic data for crop recognition and model construction. Then, through image processing techniques, such as image segmentation, feature extraction, and pattern recognition, the system analyzes the obtained crop image information. The image processing operation can identify and distinguish different crop types, and at the same time evaluate the maturity and health status of the crops. These processed information, that is, the second crop image information, provides deeper data support for the construction of the crop model. Finally, using the processed crop image information, the system constructs a detailed crop model. The crop model not only includes the physical characteristics of the crops, but also contains information such as the growth cycle, harvest time, and other relevant information of the crops. This model will be used to guide the harvester to adjust working parameters, such as cutting height, threshing speed, etc., according to the specific characteristics of the crops when performing the harvesting operation to achieve the best harvesting effect. For example, in a paddy field, the system uses image acquisition devices to obtain the image information of the rice, and then uses image processing techniques to identify the maturity and distribution density of the rice. Based on this information, the constructed crop model will guide the harvester to adjust the cutting height and threshing speed to adapt to the specific situation of the rice, ensuring the efficiency and quality of the harvesting operation.

[0117] In step S15, perform a working parameter calculation operation based on the crop model to obtain working parameters, including:

[0118] Obtain preset working parameters through the harvesting management system based on the crop model to obtain working parameters;

[0119] Obtain the harvested crop information and the harvester operation information through the real-time acquisition module based on the working parameters;

[0120] Perform a working parameter calculation operation based on the harvested crop information and the harvester operation information to obtain working parameters.

[0121] It should be noted that in step S15, the specific process of implementing the working parameter calculation operation involves in-depth analysis of the crop model and accurate collection of real-time data. First, the system retrieves the preset working parameters that match the crop model through the harvesting management system. These parameters are preset based on historical operation data and crop characteristics, including but not limited to key operation indicators such as cutting height, threshing force, and harvesting speed. The preset working parameters provide an initial working state for the harvester, ensuring a basic operation standard under different crops and conditions. Subsequently, the system uses the real-time collection module to collect information on the harvested crops and the operating information of the harvester. These information include key data such as the actual maturity, humidity of the crops, the working efficiency and energy consumption of the harvester. The collection of real-time data is crucial for dynamically adjusting the working parameters. After obtaining the preset working parameters and real-time collection information, the system will perform the calculation operation of the working parameters. This step involves complex algorithms and models that can calculate the optimal working parameters based on the specific characteristics of the crops and the real-time performance of the harvester. If the real-time data shows that the humidity of the crops is high, the system will adjust the threshing force to adapt to this change and ensure the best threshing effect. In addition, the calculation of the working parameters also involves the adjustment and optimization of the preset parameters. The system will fine-tune the preset parameters according to the real-time collected data and the analysis results of the crop model to achieve more precise operation control. This dynamic adjustment mechanism enables the harvester to flexibly respond to different operating conditions. For example, in a paddy field, the system obtains the image information of the rice through the image acquisition device, and through image processing technology, it identifies the maturity and distribution density of the rice, and constructs a detailed crop model. Based on this model, the system presets the initial cutting height and threshing speed. During the actual harvesting process, the real-time collection module collects the actual humidity data of the rice, and the system dynamically adjusts the threshing force according to these data and the analysis results of the crop model to ensure the threshing effect of the rice under high humidity conditions. Through this precise parameter adjustment, the harvester can achieve efficient harvesting of the rice while ensuring that the quality of the rice is not damaged.

[0122] In step S16, according to the working parameters, the crop harvesting path, and the operation area model, perform the crop harvesting operation to achieve harvester control, including:

[0123] According to the crop harvesting path and the operation area model, use the automatic navigation system to drive the harvester to the target harvesting area;

[0124] According to the working parameters, perform the working parameter output operation to obtain the harvester operation parameters;

[0125] According to the harvester operation parameters, perform the crop harvesting operation to achieve harvester control.

[0126] It should be noted that the process of performing crop harvesting operations to achieve harvester control is the final implementation stage of the automated harvesting method of the present invention. This step ensures that the harvester can accurately and efficiently complete the harvesting task according to the working parameters calculated in the previous steps, the planned harvesting path, and the operation area model. First, the system guides the harvester to the target harvesting area through the automatic navigation system based on the crop harvesting path planned in step S13 and the operation area model constructed in step S11. The automatic navigation system uses the satellite navigation system and combines it with the positioning module of the agricultural machinery to accurately control the driving direction and speed of the harvester to ensure that it accurately reaches the predetermined harvesting starting point. Then, the system performs the operation of outputting working parameters according to the working parameters calculated in step S15. These parameters include, but are not limited to, cutting height, threshing force, harvesting speed, etc. These operating parameters are input into the control system of the harvester to precisely control each working component of the harvester. For example, if the crop model shows that the crops in the current operation area are relatively wet, the system will increase the threshing force to ensure effective threshing; if the crop maturity is uneven, the system will adjust the cutting height to adapt to crops of different maturities. Finally, according to the operating parameters of the harvester, the crop harvesting operation is performed. The harvester starts the harvesting operation along the predetermined harvesting path under the guidance of the automatic navigation system. During the entire harvesting process, the system continuously monitors the operating state of the harvester and the harvesting situation of the crops, and adjusts the working parameters in real time to cope with changes in field conditions. For example, when encountering obstacles or terrain changes, the automatic navigation system will promptly adjust the driving path to avoid collisions or getting stuck; if a component of the harvester fails, the system will immediately issue an alarm and take corresponding emergency measures.

[0127] For the convenience of understanding the present invention, some preferred embodiments of the present invention will be further described below.

[0128] Automated harvesting operations are carried out on the corn crops of a large farm. The terrain of the operation area of this farm is complex, including hills and plains. Therefore, the requirements for the automated operation of the harvester are very high.

[0129] Step 1: Obtain the environmental information and crop information of the operation area. Use the positioning system and ranging camera installed on the harvester to obtain the position coordinate information and crop distribution information of the operation area. Since the terrain of the operation area is complex, multiple measurements need to be carried out in different terrain areas to ensure comprehensive terrain information is obtained. The system automatically records these data and stores them as environmental information and crop information.

[0130] Step 2: Construct the operation area model and calculate the harvesting priority. The collected environmental information and crop information are input into the operation area model construction program. The program first analyzes the terrain, including slope and elevation, and then combines the maturity and distribution density of the crops to construct a detailed operation area model. Then, the harvesting priority calculation formula is used to calculate the harvesting priorities of different areas according to crop type, maturity, timeliness, and area ratio.

[0131] Step 3: Harvesting path planning. According to the operation area model and harvesting priority, the APF-FMT algorithm is used for harvesting path planning. The algorithm first calculates the harvesting area priority of each area, and then guides the harvester to move towards the crop area with high priority in the attraction field while avoiding obstacles. The FMT algorithm further optimizes the path search process to quickly find the shortest path from the current position to the target position.

[0132] Step 4: Crop model construction. After the path planning is completed, the corn crop image information of the operation area is obtained through a high-resolution camera, and image processing is performed to identify the maturity and distribution density of the corn. Based on this information, a detailed corn crop model is constructed to provide accurate crop information for the harvester.

[0133] Step 5: Working parameter calculation. According to the crop model, the system obtains the preset working parameters from the harvesting management system and combines the real-time collected corn and harvester operation information to dynamically adjust the working parameters, such as cutting height and threshing force, to adapt to the specific characteristics of corn in different areas.

[0134] Step 6: Execute the crop harvesting operation. Finally, the harvester accurately drives into the target area according to the adjusted working parameters and the pre-planned harvesting path through the automatic navigation system and starts the harvesting operation. During the whole process, the system continuously monitors the operation status of the harvester and the harvesting situation of the crops, and adjusts the working parameters in real time to ensure the efficiency and quality of the harvesting operation.

[0135] In summary, through a series of precise and efficient steps, the present invention realizes the automated operation of the harvester in a complex farmland environment. Starting from obtaining the environmental information and crop information of the operation area to finally performing the crop harvesting operation, each step of the present invention aims to improve the intelligent level and operation efficiency of the harvesting operation. By constructing the operation area model and crop model, the crop distribution, maturity and terrain features are accurately identified, thus providing detailed data support for the harvesting operation. The calculation of the harvesting priority and path planning further ensures that the harvester can operate in the most optimized order and path, reducing ineffective movement and improving efficiency. The calculation of the dynamically adjusted working parameters enables the harvester to flexibly adjust the operation according to real-time data and crop characteristics to meet the requirements of different crops and operation conditions. Finally, through the automatic navigation system and precise control of the working parameters, the harvester can accurately and efficiently complete the harvesting task, ensuring the quality and efficiency of crop harvesting.

[0136] Referring to Figure 2 , the third embodiment of the present invention provides an Internet of Things-based intelligent control system for a harvester, including:

[0137] A data acquisition module for acquiring the environmental information and crop information of the operation area;

[0138] A data preprocessing module for constructing an operation area model and performing a harvesting priority calculation operation according to the environmental information and the crop information to obtain an operation area model and a crop harvesting priority;

[0139] A path planning module for performing a harvesting path planning operation according to the operation area model and the crop harvesting priority to obtain a crop harvesting path;

[0140] A crop model generation module for performing a crop model construction operation according to the operation area model and the crop harvesting path to obtain a crop model;

[0141] A parameter calculation module for performing a working parameter calculation operation according to the crop model to obtain working parameters;

[0142] An execution module for performing a crop harvesting operation according to the working parameters, the crop harvesting path and the operation area model to realize harvester control.

[0143] It should be noted that the Internet of Things-based intelligent control system for a harvester provided by the embodiment of the present invention is used to execute all the process steps of the Internet of Things-based intelligent control method for a harvester in the above embodiment. The working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0144] An embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an intelligent control program for a harvester based on the Internet of Things. When the processor executes the computer program, the steps in each of the above embodiments of the intelligent control method for a harvester based on the Internet of Things are implemented, such as Figure 1 the step S11 shown. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the above device embodiments are implemented, such as the parameter calculation module.

[0145] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0146] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine certain components, or different components. For example, the electronic device may further include input / output devices, network access devices, a bus, etc.

[0147] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and lines.

[0148] The memory can be used to store the computer program and / or modules. By running or executing the computer program and / or modules stored in the memory, and invoking the data stored in the memory, the processor realizes various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the sound playback function, the image playback function, etc.); the data storage area can store the data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0149] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included 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, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0150] It should be noted that the device embodiments described above are merely illustrative. 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 to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0151] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A harvester, characterized in that, It includes a harvester main body, and a storage trolley is installed at the upper end of the harvester main body. The storage trolley is connected to the harvester main body through two sets of connection components. And a sloping plate is rotatably installed at the tail end of the harvester main body. The sloping plate is rotated by a driving mechanism. When one end of the sloping plate rotates to contact the ground, the storage trolley can transport the stored grain inside.

2. The harvester according to claim 1, wherein, The connection component includes an outer plate connected to one side of the storage trolley. A hook is rotatably arranged inside the outer plate. A positioning groove is opened on the upper side of the hook. A cylinder is installed on the outer plate. The piston end of the cylinder is fixedly connected to a positioning plate. The positioning plate passes through a through hole opened on the upper side of the outer plate and is inserted and matched with the positioning groove.

3. The harvester according to claim 1, characterized in that, The driving mechanism includes a first rotating rod rotatably connected to the tail end of the harvester main body. The first rotating rod is fixedly connected to one end of the sloping plate. A first bevel gear fixedly connected to the first rotating rod. A second rotating rod is rotatably arranged on one side of the harvester main body. Second bevel gears are connected to both the upper and lower ends of the second rotating rod. The second bevel gear located at the upper end meshes with the first bevel gear. A lead screw is rotatably arranged at the bottom of the harvester main body. One end of the lead screw is connected to a third bevel gear. The third bevel gear meshes with the second bevel gear located at the lower end.

4. The harvester according to claim 3, characterized in that, A support plate is threadedly connected to the lead screw. The upper end of the support plate slides in a limit groove opened at the bottom of the harvester main body.

5. The harvester according to claim 1, characterized in that, A conical hopper is arranged inside the storage trolley. The discharge port of the conical hopper corresponds to the feed port of a feeding auger installed at the bottom of the storage trolley. A feed port is opened at the upper end of the storage trolley.

6. An intelligent control method for a harvester based on the Internet of Things, characterized in that, Executed by a computer, including: Obtaining environmental information and crop information of the operation area; According to the environmental information and the crop information, constructing an operation area model and performing a harvesting priority calculation operation to obtain an operation area model and crop harvesting priorities; According to the operation area model and the crop harvesting priorities, performing a harvesting path planning operation to obtain a crop harvesting path; According to the operation area model and the crop harvesting path, performing a crop model construction operation to obtain a crop model; According to the crop model, performing a working parameter calculation operation to obtain working parameters; According to the working parameters, crop harvesting path and operation area model, performing a crop harvesting operation to achieve harvester control.

7. The intelligent control method of a harvester based on the Internet of Things according to claim 6, characterized in that, Obtaining environmental information and crop information of the operation area, including: Obtaining the position coordinate information of the operation area through a positioning module; Obtaining the crop information of the operation area through a harvesting management system; According to the position coordinate information, performing a terrain information obtaining operation to obtain the terrain information of the operation area; According to the position coordinate information and terrain information, performing an environmental information processing operation to obtain the environmental information of the operation area.

8. The intelligent control method of a harvester based on the Internet of Things according to claim 6, characterized in that According to the environmental information and the crop information, constructing an operation area model and performing a harvesting priority calculation operation to obtain an operation area model and crop harvesting priorities, including: According to the environmental information and the crop information, performing an operation area model construction operation to obtain an operation area model; According to the operation area model, performing an operation task generation operation to obtain an operation task; According to the operation task and the crop information, performing a harvesting priority calculation operation to obtain crop harvesting priorities; The calculation formula for the harvesting priority calculation is as follows: P1 = C × (M × T × A1) Wherein, P1 represents the crop harvesting priority, C represents the crop type priority coefficient, M represents the crop maturity index, T represents the crop timeliness index, and A1 represents the crop area ratio.

9. The intelligent control method of a harvester based on the Internet of Things according to claim 6, characterized in that According to the operation area model and the crop harvesting priority, perform a harvesting path planning operation to obtain a crop harvesting path, including: According to the operation area model and the crop harvesting priority, perform a harvesting area priority calculation operation to obtain a harvesting area priority. According to the harvesting area priority and the operation area model, perform a harvesting path planning operation to obtain a crop harvesting path. The calculation formula for the harvesting area priority is as follows: Among them, P2 represents the priority of the harvesting area, A2 represents the crop area, k att represents the attraction coefficient, k rep represents the repulsion coefficient, d obs represents the obstacle distance, d goal represents the target point distance, σ represents the standard deviation, and B represents the balance coefficient.

10. The intelligent control method for a harvester based on the Internet of Things according to claim 6, characterized in that, According to the operation area model and the crop harvesting path, perform a crop model construction operation to obtain a crop model, including: According to the operation area model and the crop harvesting path, perform an image acquisition operation to obtain the first crop image information. According to the crop image information, perform an image processing operation to obtain the second crop image information. According to the second crop image information, perform a crop model construction operation to obtain a crop model.

11. The intelligent control method of a harvester based on the Internet of Things according to claim 6, characterized in that, According to the crop model, perform a working parameter calculation operation to obtain working parameters, including: According to the crop model, obtain preset working parameters through a harvesting management system to obtain working parameters. According to the working parameters, obtain the harvested crop information and the harvester operation information through a real-time acquisition module. According to the harvested crop information and the harvester operation information, perform a working parameter calculation operation to obtain working parameters.

12. The intelligent control method of a harvester based on the Internet of Things according to claim 6, characterized in that, According to the working parameters, the crop harvesting path, and the operation area model, perform a crop harvesting operation to achieve harvester control, including: According to the crop harvesting path and the operation area model, drive the harvester to the target harvesting area through an automatic navigation system. According to the working parameters, perform a working parameter output operation to obtain harvester operation parameters. According to the harvester operation parameters, perform a crop harvesting operation to achieve harvester control.

13. An intelligent control system for a harvester based on the Internet of Things, including the intelligent control system for a harvester based on the Internet of Things according to any one of claims 6-12, characterized in that, Including: A data acquisition module for acquiring the environmental information and crop information of the operation area. A data preprocessing module for constructing an operation area model and performing a harvesting priority calculation operation according to the environmental information and the crop information to obtain an operation area model and a crop harvesting priority. A path planning module for performing a harvesting path planning operation according to the operation area model and the crop harvesting priority to obtain a crop harvesting path. A crop model generation module for performing a crop model construction operation according to the operation area model and the crop harvesting path to obtain a crop model. A parameter calculation module for performing a working parameter calculation operation according to the crop model to obtain working parameters. An execution module for performing a crop harvesting operation according to the working parameters, the crop harvesting path, and the operation area model to achieve harvester control.

14. An electronic device, comprising the IoT-based intelligent control system for a harvester according to any one of claims 6-12, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the IoT-based intelligent control method for a harvester as described in any one of claims 6 to 12.

15. A computer-readable storage medium, comprising the IoT-based intelligent control system for a harvester according to any one of claims 6-12, characterized in that, The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the IoT-based intelligent control method for a harvester as described in any one of claims 6 to 12.

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