Intelligent agricultural machine operation regulation system and method based on artificial intelligence and internet of things

Abstract

The present application relates to the technical field of agricultural machinery operation regulation, in particular to a smart agricultural machinery operation regulation system and method based on artificial intelligence and the Internet of Things, which quantifies the standard reaching of crop growth in each growth stage and the growth balance degree between different stages, accurately identifies the necessity of regulation, captures the multi-stage growth difference anomaly caused by environmental regulation imbalance in time, effectively solves the problem of fuzzy regulation demand judgment in staggered planting scenarios, uses the control variable method to strip the independent influence of a single environmental parameter, quantitatively calculates the action weight of each parameter on crop growth, and then determines the regulation priority according to the weight order, which can accurately lock the core environmental parameter that has the greatest impact on the current crop growth, so that the regulation resources are concentrated in the key link, which not only improves the regulation efficiency, but also reduces the invalid consumption of agricultural machinery resources, and at the same time adapts to the differentiated needs of crops in different growth stages for environmental parameters.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery operation control technology, and more specifically, to a smart agricultural machinery operation control system and method based on artificial intelligence and the Internet of Things. Background Technology

[0002] As agricultural production strives to balance economic returns, production efficiency, and risk, staggered planting patterns have become widely used in arable land, where crops at different growth stages coexist on the same plot. While this planting pattern optimizes resource utilization and income structure, it also places higher demands on the regulation of the crop growth environment. Crops at different growth stages have fundamentally different environmental requirements, necessitating targeted control measures.

[0003] In current intelligent agricultural machinery operation and control, when abnormal growth of crops at certain growth stages is detected, a method of uniformly controlling multiple environmental parameters of the cultivated land is often adopted. This control model does not fully consider the differences in environmental needs of crops at different growth stages. Not only is it difficult to accurately solve the problem of abnormal growth of crops at specific stages, but it may also have an adverse effect on crops at other normal growth stages, destroying their suitable growth environment and thus affecting the overall growth status of crops.

[0004] Meanwhile, existing control schemes lack scientifically quantifiable criteria for determining control needs and precise parameter selection logic, relying heavily on experience to determine whether control is needed and which environmental parameters to control. This approach cannot accurately identify the weight of each environmental parameter's impact on crop growth, easily leading to excessive consumption of control resources, low control efficiency, and even exacerbating crop growth abnormalities due to deviations in control direction. It is difficult to meet the needs of refined and differentiated agricultural machinery operation control under staggered planting patterns. Summary of the Invention

[0005] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a smart agricultural machinery operation control system and method based on artificial intelligence and the Internet of Things.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A smart agricultural machinery operation control system based on artificial intelligence and the Internet of Things includes: The agricultural machinery regulation intervention analysis module is used to regularly update the crop growth logs at each growth stage in the cultivated land, determine the agricultural machinery regulation intervention index based on the crop growth logs at each growth stage, and determine whether it is necessary to regulate the intervention of agricultural machinery in the cultivated land based on the comparison results between the agricultural machinery regulation intervention index and the agricultural machinery regulation intervention threshold. The agricultural machinery environment intervention arrangement module, after determining to regulate the intervention of agricultural machinery in cultivated land, obtains the independent regulation intervention values ​​and regulation reference data of various environmental parameters of cultivated land, sorts all types of environmental parameters in descending order of their independent regulation intervention values, and marks the environmental parameter at the top of the sort as the pre-selected primary regulation parameter. The intelligent agricultural machinery operation control module marks the next type of environmental parameter as the preselected primary control parameter when the actual data of the preselected primary control parameter is consistent with the control reference data. When the actual data of the preselected primary control parameter is inconsistent with the control reference data, the preselected primary control parameter is marked as the fixed primary control parameter, and the corresponding agricultural machinery is controlled to adjust the actual data of the fixed primary control parameter to be consistent with the control reference data.

[0007] Furthermore, a crop growth log for a growth stage includes: the crop's growth stage and the crop growth index; The crop growth index of the crop growth log is obtained as follows: the growth stage of the crop in the crop growth log is determined, and the growth compliance values ​​of various growth indicators in the cultivated land at that growth stage are obtained through the Internet of Things. The average value of the growth compliance values ​​of various growth indicators is summed to calculate the crop growth index.

[0008] Furthermore, the agricultural machinery intervention index is determined based on crop growth logs at each growth stage. The specific method is as follows: the crop growth index of the crop growth logs at each growth stage is marked as... Set the compliance threshold for the stage as Through formula The overall growth index was calculated. Synchronously obtain Bbz, through the formula The agricultural machinery regulation intervention index was calculated. .

[0009] Furthermore, the growth control index of the differential growth stage control group was obtained as follows: The disappointment level values ​​of the two crop growth logs in the differential growth stage control group were obtained, and the disappointment level values ​​of the two crop growth logs were summed to calculate the cumulative disappointment level value. The absolute difference between the crop growth indices of the two crop growth logs was calculated to obtain the stage difference value. Through formula The growth control index of the growth control group at this stage of difference was calculated. ;in, A reasonable threshold for stage differences.

[0010] Furthermore, the crop growth disappointment level value in the crop growth log is obtained as follows: Select a crop growth log and use the formula... The crop growth disappointment value was calculated from the crop growth log. ; This represents the crop growth index in the crop growth log.

[0011] Furthermore, the independent regulatory intervention value and regulatory reference data for an environmental parameter are obtained as follows: An environmental parameter is determined and marked as the regulatory parameter. All other environmental parameters are marked as fixed parameters. Real data for each fixed parameter are collected. A regulatory simulation model is constructed based on the crop growth indicators at each growth stage and the real data for each fixed parameter. The regulatory parameter is set with j types of simulated data. The agricultural machinery regulatory intervention index for each type of simulated data is then obtained. The average value of the agricultural machinery regulatory intervention indices for all simulated data is calculated to obtain the independent regulatory intervention value for that environmental parameter. The simulated data with the smallest agricultural machinery regulatory intervention index is marked as the regulatory reference data for that environmental parameter.

[0012] Furthermore, the agricultural machinery regulation intervention index of simulated data is obtained as follows: a kind of simulated data is input into the regulation simulation model, and simulated cultivation and growth for a duration of t is performed. After the duration of t ends, the agricultural machinery regulation intervention index in the regulation simulation model is obtained, which is the agricultural machinery regulation intervention index of the simulated data.

[0013] Furthermore, the method for obtaining Bbz is as follows: the crop growth logs of each growth stage are matched pairwise to obtain multiple differential growth control groups. The growth control index of each differential growth control group is obtained. When the growth control index is higher than the growth control threshold, the number of abnormal control groups is increased by one. Finally, the number of abnormal control groups is summed and marked as Bbz.

[0014] Furthermore, the intelligent agricultural machinery operation control method based on artificial intelligence and the Internet of Things has the following steps: Step 1: Data collection of crops at each growth stage in cultivated land and updating of crop growth logs; Step 2: Determine the demand for agricultural machinery regulation based on the agricultural machinery regulation intervention index; Step 3: Calculation and ranking of independent control intervention values ​​for environmental parameters; Step 4: Verify and finalize the consistency of the primary control parameters; Step 5: Precise control and parameter compliance verification of intelligent agricultural machinery.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The system and method of this invention accurately identify the necessity of regulation by quantifying the crop growth status at each growth stage and the balance of growth between different stages, and promptly capture abnormal differences in growth across multiple stages caused by imbalances in environmental regulation. This effectively solves the problem of ambiguous determination of regulation needs in staggered planting scenarios. By using the controlled variable method to isolate the independent influence of individual environmental parameters, and by quantitatively calculating the weight of each parameter on crop growth, the regulation priority is determined by ranking them according to their weights. This can accurately identify the core environmental parameters that have the greatest impact on the current crop growth, allowing regulation resources to be concentrated on key aspects. This not only improves regulation efficiency but also reduces the ineffective consumption of agricultural machinery resources. At the same time, it adapts to the differentiated needs of crops at different growth stages for environmental parameters, avoiding interference with normally growing crops and ensuring that the regulation target is accurately matched with the crop growth needs. Furthermore, by using simulation to predict the regulation effect, the accuracy and reliability of agricultural machinery operations are further improved, ensuring crop yield and quality. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system of the present invention; Figure 2 A flowchart illustrating the process of obtaining independent control intervention values ​​for environmental parameters and control reference data; Figure 3 A flowchart illustrating the process for determining the agricultural machinery regulation intervention index. Detailed Implementation

[0017] Example 1: Refer to Figures 1 to 3 The intelligent agricultural machinery operation control system based on artificial intelligence and the Internet of Things includes an agricultural machinery control intervention analysis module, an agricultural machinery environment intervention arrangement module, and an intelligent agricultural machinery operation control module.

[0018] The agricultural machinery control and analysis module regularly updates crop growth logs for each growth stage in the cultivated land. (The time interval for each regular update is set based on the crop's growth characteristics; for example, if the crop is tomato, the time interval can be set to 24 hours.) Currently, to balance economic benefits, production efficiency, and risks, crops are often planted at different stages simultaneously in cultivated land. However, the presence of crops at different growth stages can lead to the use of smart agricultural machinery to simultaneously regulate multiple environmental parameters of the cultivated land when abnormal growth is detected in some stages. For example, using frost protection machines to uniformly heat the soil temperature. This regulation method may not only fail to accurately solve the problem of abnormal growth but may also affect other stages. This can negatively impact normally growing crops and lead to overuse of agricultural machinery resources. For example, excessive soil temperature control may cause physiological disorders in normally growing crops, thus affecting their yield and quality. Based on crop growth logs at each growth stage, an agricultural machinery intervention index is determined. When the agricultural machinery intervention index is higher than the agricultural machinery intervention threshold (the agricultural machinery intervention threshold is set based on the cost-effectiveness of agricultural machinery resources, balancing the necessity of control with agricultural machinery energy consumption / loss, and avoiding resource waste caused by frequent small-scale controls—if the growth problem corresponding to the agricultural machinery intervention index can be recovered by the natural environment (such as a small humidity deviation), the agricultural machinery intervention threshold will be set to a higher value), it is determined that agricultural machinery should be used to control the cultivated land (if it is not higher, no agricultural machinery needs to be used for control).

[0019] A crop growth log for a growth stage includes: the crop's growth stage (including seedling stage, flowering stage, fruit expansion stage, etc.) and the crop growth index.

[0020] The crop growth index in the crop growth log is obtained as follows: Identify the crop's growth stage in the crop growth log, and obtain the growth target values ​​for various growth indicators at that stage in the cultivated land through the Internet of Things (the growth indicators for crops at different growth stages are not necessarily the same; for example, the growth indicators corresponding to the seedling stage include average plant height and average stem length, while the growth indicators corresponding to the fruit expansion stage include average stem length, average single fruit weight, and average number of fruits). Sum the growth target values ​​of each growth indicator and calculate the average to obtain the crop growth index.

[0021] The growth target value for a growth indicator is obtained as follows: Select a growth indicator, collect the actual performance value of the corresponding growth stage in the cultivated land for that growth indicator (taking the average stem length during the fruit expansion stage as an example; the actual performance value is obtained by randomly collecting crop samples from the cultivated land during the fruit expansion stage using a grid random sampling method and calculating the average stem length). Obtain the reasonable performance value of the corresponding growth stage in the cultivated land for that growth indicator (the reasonable performance value is the preset qualified target for the growth indicator, such as the qualified target for the average stem length during the seedling stage being 4mm, and the qualified target for the average single fruit weight during the fruit expansion stage being 200g), the minimum performance threshold, and the maximum performance threshold (the minimum and maximum performance thresholds are the minimum and maximum values ​​allowed for the preset growth target, such as the minimum threshold for the average stem length during the seedling stage being 2.5mm and the maximum threshold being 5mm, and the minimum threshold for the average single fruit weight during the fruit expansion stage being 100g and the maximum threshold being 300g). Then, use the formula... The growth target value for this growth indicator was calculated. K is the index correction coefficient. The correction coefficients for growth indicators at different growth stages are not the same. For example, the K value for the average stem length of seedlings is 1.1 (because the stem length of seedlings is the core indicator of strong / weak seedlings, and is extremely sensitive (stems that are too thin are prone to lodging, and stems that are too thick are prone to excessive growth), so the influence of deviation needs to be amplified). The K value for the average single fruit weight during the fruit expansion stage is 1.05 (single fruit weight is a yield indicator, but it has a certain growth elasticity (small deviations can be compensated for later), and is moderately sensitive).

[0022] The agricultural machinery intervention index is determined based on crop growth logs at each growth stage. The specific method is as follows: the crop growth index of the crop growth log at each growth stage is marked as... (i = 1, 2, ..., n, where n is the total number of growth stages and i represents the crop growth log for the corresponding growth stage), set the stage compliance threshold as follows: (If set to 0.8, ≥0.8 indicates that the growth at this stage is compliant), through the formula The overall growth index was calculated. The crop growth logs at each growth stage are paired to obtain multiple differential growth control groups (e.g., if there are crop growth logs for the seedling stage, flowering stage, and fruit expansion stage, three differential growth control groups will be obtained: seedling-flowering stage differential growth control group, seedling-fruit expansion stage differential growth control group, and flowering-fruit expansion stage differential growth control group). The growth control index for each differential growth control group is obtained. When the growth control index is higher than the growth control threshold, the number of abnormal control groups is increased by one (if it is not higher, no increase in the number of abnormal control groups is needed). Finally, the number of abnormal control groups is summed and labeled as Bbz, and then calculated using the formula... The agricultural machinery regulation intervention index was calculated. .

[0023] The growth control index of the differential growth stage control group was obtained as follows: The disappointment level values ​​of the two crop growth logs in the differential growth stage control group were obtained (each crop growth log corresponds to one disappointment level value). The disappointment level values ​​of the two crop growth logs were summed to calculate the cumulative disappointment level value. The absolute difference between the crop growth indices of the two crop growth logs was calculated to obtain the stage difference value. Through formula The growth control index of the growth control group at this stage of difference was calculated. ;in, A reasonable threshold for stage differences (e.g., if set) If the difference between the crop growth index in the seedling stage (e.g., 0.7) and the crop growth index in the fruit expansion stage (e.g., 0.9) is 0.2 (≤0.3), it is a reasonable difference. If the difference reaches 0.5 (>0.3), it indicates that the growth gap between the two stages is too large (e.g., the seedling stage growth is too poor and the fruit expansion stage growth is too good), which may be caused by an imbalance in environmental regulation.

[0024] The crop growth disappointment level value is obtained as follows: Select a crop growth log and use the formula... The crop growth disappointment value was calculated from the crop growth log. ; This represents the crop growth index in the crop growth log.

[0025] The agricultural machinery environmental intervention arrangement module determines that after the agricultural machinery is used to regulate the cultivated land, it obtains the independent regulation intervention values ​​and regulation reference data of various environmental parameters of the cultivated land (the types of environmental parameters include soil temperature, air temperature, soil moisture, air humidity, etc.). It sorts all types of environmental parameters in descending order of their independent regulation intervention values ​​and marks the environmental parameter with the highest value as the pre-selected primary regulation parameter.

[0026] The independent intervention value and reference data for an environmental parameter are obtained as follows: An environmental parameter is determined and marked as the control parameter. All other environmental parameters (excluding the determined parameter) are marked as fixed parameters. Real data for each fixed parameter are collected (e.g., after determining soil temperature, parameters other than soil temperature such as air temperature, soil moisture, and air humidity are marked as fixed parameters, and real data for these parameters are collected). Based on the growth indicators of each crop growth stage (i.e., the growth indicators for the seedling stage include average plant height and average stem length; the growth indicators for the fruit expansion stage include average stem length, average single fruit weight, and average number of fruits) and the real data of each fixed parameter, a control simulation model is constructed. The system sets j types of simulation data (j=1, 2, ..., j, each simulation data is different; taking soil temperature as the control parameter as an example, simulation data 1 is 10℃, simulation data 2 is 15℃, and simulation data j is 35℃. Taking simulation data 1 as an example, the control simulation model simulates cultivation and growth at a soil temperature of 10℃, and taking simulation data 2 as an example, the control simulation model simulates cultivation and growth at a soil temperature of 15℃. All other environmental parameters are the same in the two simulations). Then, the agricultural machinery control intervention index of each simulation data is obtained. The agricultural machinery control intervention index of various simulation data is summed and averaged to calculate the independent control intervention value of this environmental parameter (i.e., the control parameter). The simulation data with the smallest agricultural machinery control intervention index value is marked as the control reference data of this environmental parameter.

[0027] The agricultural machinery regulation intervention index of simulated data is obtained as follows: a simulated data is input into a regulation simulation model, and simulated cultivation and growth for a duration of t is performed. After the duration of t ends, the agricultural machinery regulation intervention index in the regulation simulation model is obtained, which is the agricultural machinery regulation intervention index of the simulated data (i.e., the agricultural machinery regulation intervention index is obtained in the simulation scenario).

[0028] The intelligent agricultural machinery operation control module, when the actual data of the pre-selected primary control parameter is consistent with the control reference data (or the difference is less than a%), the actual data of the pre-selected primary control parameter is the actual data of the corresponding environmental parameter, such as the actual ground temperature and the actual air temperature), marks the next type of environmental parameter in the sort as the pre-selected primary control parameter (and so on). When the actual data of the pre-selected primary control parameter is inconsistent with the control reference data (or the difference is greater than or equal to a%), the pre-selected primary control parameter is marked as the fixed primary control parameter, and the corresponding agricultural machinery is controlled to adjust the actual data of the fixed primary control parameter to be consistent with the control reference data (for example, if the ground temperature is ranked first, and the actual ground temperature data is 15℃, while the control reference data for the ground temperature is 25℃, and the two are inconsistent and the difference is large, then the frost protection machine is started to raise the temperature of the low temperature, during which the parameters such as air temperature and soil moisture do not change significantly).

[0029] A simulation model for crop regulation is constructed based on real data of crop growth indicators at each growth stage and various fixed parameters. The specific method is as follows: CropSyst + Unity simulation software is selected (CropSyst is responsible for crop physiological numerical calculations (e.g., calculating plant height growth rate and single fruit weight accumulation based on temperature / humidity); Unity is responsible for 3D visualization (e.g., displaying changes in crop plant height and morphological feedback after environmental parameter adjustments). Real-time synchronization is achieved through an API interface (CropSyst's numerical results drive Unity's visualization effects, and Unity's environmental operations are fed back to CropSyst for updated calculations)). To avoid discrepancies between the simulation and reality, the simulation model needs to be calibrated (model calibration: using historical data from the last two planting cycles, adjusting the influence coefficients of environmental parameters (e.g., temperature influence coefficient) to align the simulated growth indicators with historical measured values). The average relative error is ≤8%. In the simulation software, crop entities at each growth stage are created, and corresponding parameters are assigned to the crops based on growth indicators (e.g., plant height and stem length for seedlings, and stem length, single fruit weight, and fruit quantity for fruit-expanding stages; all seedlings are assigned parameters based on average plant height and average stem length, and the same applies to other stages). Based on real data for each fixed parameter, corresponding environmental parameters are added to the simulation software to quantify the influence relationships (example: suitable temperature for seedlings is 18-22℃: below 15℃, plant height growth rate decreases by 50%; above 28℃, stem thickening rate decreases by 40%; suitable soil moisture for seedlings is 70-80%: below 60%, root length growth stagnates; above 85%, the risk of root rot increases by 15%, including quantitative influence relationships across all stages, which are not exhaustively listed here). This allows for the construction of a regulatory simulation model.

[0030] Example 2: A smart agricultural machinery operation control method based on artificial intelligence and the Internet of Things, the method steps are as follows: Step 1: Data collection of crops at each growth stage in cultivated land and updating of crop growth logs; Step 2: Determine the demand for agricultural machinery regulation based on the agricultural machinery regulation intervention index; Step 3: Calculation and ranking of independent control intervention values ​​for environmental parameters; Step 4: Verify and finalize the consistency of the primary control parameters; Step 5: Precise control and parameter compliance verification of intelligent agricultural machinery.

[0031] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0032] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0033] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0034] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0035] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0036] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0037] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0038] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart agricultural machinery operation control system based on artificial intelligence and the Internet of Things, characterized in that, include: The agricultural machinery regulation intervention analysis module is used to regularly update the crop growth logs at each growth stage in the cultivated land, determine the agricultural machinery regulation intervention index based on the crop growth logs at each growth stage, and determine whether it is necessary to regulate the intervention of agricultural machinery in the cultivated land based on the comparison results between the agricultural machinery regulation intervention index and the agricultural machinery regulation intervention threshold. The agricultural machinery environment intervention arrangement module, after determining to regulate the intervention of agricultural machinery in cultivated land, obtains the independent regulation intervention values ​​and regulation reference data of various environmental parameters of cultivated land, sorts all types of environmental parameters in descending order of their independent regulation intervention values, and marks the environmental parameter at the top of the sort as the pre-selected primary regulation parameter. The intelligent agricultural machinery operation control module marks the next type of environmental parameter as the preselected primary control parameter when the actual data of the preselected primary control parameter is consistent with the control reference data. When the actual data of the preselected primary control parameter is inconsistent with the control reference data, the preselected primary control parameter is marked as the fixed primary control parameter, and the corresponding agricultural machinery is controlled to adjust the actual data of the fixed primary control parameter to be consistent with the control reference data.

2. The intelligent agricultural machinery operation control system based on artificial intelligence and the Internet of Things as described in claim 1, characterized in that, A crop growth log for a growth stage includes: the crop's growth stage and the crop growth index; The crop growth index of the crop growth log is obtained as follows: the growth stage of the crop in the crop growth log is determined, and the growth compliance values ​​of various growth indicators in the cultivated land at that growth stage are obtained through the Internet of Things. The average value of the growth compliance values ​​of various growth indicators is summed to calculate the crop growth index.

3. The intelligent agricultural machinery operation control system based on artificial intelligence and the Internet of Things according to claim 1, characterized in that, The agricultural machinery intervention index is determined based on crop growth logs at each growth stage. The specific method is as follows: the crop growth index of the crop growth log at each growth stage is marked as... Set the compliance threshold for the stage as Through formula The overall growth index was calculated. Synchronously obtain Bbz, through the formula The agricultural machinery regulation intervention index was calculated. .

4. The intelligent agricultural machinery operation control system based on artificial intelligence and the Internet of Things according to claim 3, characterized in that, The growth control index of the differential growth stage control group was obtained as follows: The disappointment level of growth was obtained from the growth logs of two crops in the differential growth stage control group. The disappointment level values ​​of the two crop growth logs were then summed to calculate the cumulative disappointment level value. The absolute difference between the crop growth indices of the two crop growth logs was calculated to obtain the stage difference value. Through formula The growth control index of the growth control group at this stage of difference was calculated. ;in, A reasonable threshold for stage differences.

5. The intelligent agricultural machinery operation control system based on artificial intelligence and the Internet of Things according to claim 4, characterized in that, The crop growth disappointment level value is obtained as follows: Select a crop growth log and use the formula... The crop growth disappointment value was calculated from the crop growth log. ; This represents the crop growth index in the crop growth log.

6. The intelligent agricultural machinery operation control system based on artificial intelligence and the Internet of Things according to claim 1, characterized in that, The independent intervention value and reference data for an environmental parameter are obtained as follows: An environmental parameter is determined and marked as the control parameter. All other environmental parameters are marked as fixed parameters. Real data for each fixed parameter are collected. A control simulation model is constructed based on the growth indicators of crops at each growth stage and the real data of each fixed parameter. The control parameter is set with j types of simulated data. The agricultural machinery control intervention index for each type of simulated data is then obtained. The average value of the agricultural machinery control intervention indices for all simulated data is calculated to obtain the independent intervention value for that environmental parameter. The simulated data with the smallest agricultural machinery control intervention index is marked as the reference data for the control of that environmental parameter.

7. The intelligent agricultural machinery operation control system based on artificial intelligence and the Internet of Things according to claim 6, characterized in that, An agricultural machinery regulation intervention index based on simulated data is obtained as follows: a simulated data is input into a regulation simulation model, and simulated cultivation and growth is performed for a duration of t. After the duration of t ends, the agricultural machinery regulation intervention index in the regulation simulation model is obtained, which is the agricultural machinery regulation intervention index of the simulated data.

8. The intelligent agricultural machinery operation control system based on artificial intelligence and the Internet of Things according to claim 3, characterized in that, The method for obtaining Bbz is as follows: the crop growth logs of each growth stage are matched pairwise to obtain multiple control groups for different growth stages. The growth control index of each control group for different growth stages is obtained. When the growth control index is higher than the growth control threshold, the number of abnormal control groups is increased by one. Finally, the number of abnormal control groups is summed and marked as Bbz.

9. A smart agricultural machinery operation control method based on artificial intelligence and the Internet of Things, applied to the smart agricultural machinery operation control system based on artificial intelligence and the Internet of Things as described in any one of claims 1-8, characterized in that, The steps are as follows: Step 1: Data collection of crops at each growth stage in cultivated land and updating of crop growth logs; Step 2: Determine the demand for agricultural machinery regulation based on the agricultural machinery regulation intervention index; Step 3: Calculation and ranking of independent control intervention values ​​for environmental parameters; Step 4: Verify and finalize the consistency of the primary control parameters; Step 5: Precise control and parameter compliance verification of intelligent agricultural machinery.

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