Regional accurate quantification operation control method and system based on artificial intelligence

Abstract

The invention provides a regional accurate quantification operation control method and system based on artificial intelligence, and the method comprises the steps: carrying out the three-dimensional scanning of a target region, obtaining the three-dimensional data of the target region, and collecting the environment data of the target region; performing analog simulation on the target area based on the three-dimensional data and the environmental data, and determining optimal operation parameters based on a simulation result and actual operation requirements; and controlling the execution equipment to execute operation based on the optimal operation parameters, dynamically monitoring the target area, and when a dynamic monitoring result does not meet an expected standard, performing variable adjustment on the pulse width of the execution equipment until the operation task of the target area is completed. The reliable execution of the operation task in the target area is ensured, the loss is reduced, and the operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring and processing, and in particular to a method and system for regional precise quantitative operation control based on artificial intelligence. Background Art

[0002] Currently, in many industrial and agricultural application scenarios, such as farmland irrigation and fertilization, mine material spraying, and construction site dust reduction, precise quantitative operations are required in specific areas;

[0003] Traditional operation methods often rely on manual experience, resulting in serious waste of resources and difficulty meeting the needs of operations in complex environments. In addition, operation control methods are often relatively extensive and cannot accurately and quantitatively control operations in corresponding areas based on work types, thus greatly reducing the effectiveness of accurate and quantitative regional operation control.

[0004] Therefore, in order to overcome the above-mentioned defects, the present invention provides a method and system for regional precise quantitative operation control based on artificial intelligence. Summary of the Invention

[0005] The present invention provides a method and system for regional precise quantitative operation control based on artificial intelligence, which is used to achieve effective and reliable simulation of the target area by acquiring three-dimensional data and environmental data of the target area, and then determine the optimal operation parameters corresponding to the actual operation requirements based on the simulation results, providing support for regional precise quantitative operation control. Finally, the execution equipment is controlled according to the obtained optimal operation parameters, and the target area is dynamically monitored during the control process, so as to facilitate corresponding dynamic adjustments when the preset standards are not met, thereby ensuring the reliable execution of the operation tasks in the target area, reducing losses, and improving operation efficiency.

[0006] An artificial intelligence-based regional precision quantitative operation control method, comprising:

[0007] Perform three-dimensional scanning on the target area to obtain three-dimensional data of the target area, and at the same time, collect environmental data of the target area;

[0008] Simulate the target area based on 3D data and environmental data, and determine the optimal operating parameters based on the simulation results and actual operating requirements;

[0009] The control execution equipment performs the operation based on the optimal operation parameters and dynamically monitors the target area. When the dynamic monitoring results do not meet the expected standards, the pulse width of the execution equipment is variable adjusted until the operation task of the target area is completed.

[0010] Preferably, a method for regional precise quantitative operation control based on artificial intelligence includes: three-dimensional scanning of the target area is: LiDAR three-dimensional scanning; simulation is: CFD simulation; dynamic monitoring of the target area is based on the result of real-time scanning of the target area by LiDAR three-dimensional scanning; variable adjustment of the pulse width of the execution equipment is achieved through PWN adjustment.

[0011] Preferably, an artificial intelligence-based regional precision quantitative operation control method simulates the target area based on three-dimensional data and environmental data, and determines the optimal operation parameters based on the simulation results and actual operation requirements, including:

[0012] Analyzing the three-dimensional data of the target area, determining characteristic information of the target area, and constructing a first area model based on the characteristic information;

[0013] Mapping and associating the environmental data in the first regional model to obtain a second regional model;

[0014] Retrieve different operating parameters that need to be simulated; simulate each operating parameter in the second area model, output the operating effect corresponding to each operating parameter, and select the optimal operating parameter combination based on the operating effect;

[0015] Obtain actual operation requirements; adjust the optimal operation parameter combination according to the actual operation requirements to obtain the target optimal parameter combination.

[0016] Preferably, an artificial intelligence-based regional precision quantitative operation control method performs three-dimensional scanning of a target area, including:

[0017] Based on the three-dimensional scanning device, a laser beam is emitted to the target area and a reflected signal is received;

[0018] A three-dimensional point cloud map of the target area is generated based on the reflected signal, wherein the three-dimensional point cloud map is the three-dimensional data of the target area.

[0019] Preferably, a regional precise quantitative operation control method based on artificial intelligence simulates each operation parameter in the second regional model, outputs the operation effect corresponding to each operation parameter, and selects the optimal operation parameter combination according to the operation effect, including

[0020] Obtain baseline attribute information of the operation object; obtain parameter types of the operation parameters, and obtain the parameter change range under each parameter type based on the baseline attribute information of the operation object; perform parameter positioning on the parameter change range under each parameter type according to the preset range, and determine the discrete parameter values of the parameter change range under each parameter type; analyze the discrete parameter values corresponding to the parameter change range under each parameter type, and determine the optimal operation parameter combination based on the analysis results.

[0021] Preferably, a regional precise quantitative operation control method based on artificial intelligence, when adjusting the optimal operation parameter combination according to actual operation requirements to obtain the target optimal parameter combination, includes:

[0022] Read the baseline attribute data of the operation object and obtain the demand attribute data corresponding to the actual operation demand; obtain the ratio value between the baseline attribute data and the demand attribute data, adjust the optimal operation parameter combination according to the ratio value, and obtain the target optimal parameter combination.

[0023] Preferably, a regional precise quantitative operation control method based on artificial intelligence, after determining the optimal operation parameter combination, also includes optimizing and judging the optimal operation parameter combination, and when optimization is required, adjusting and updating the optimal operation parameters to obtain the final optimal operation parameter combination.

[0024] Preferably, a regional precise quantitative operation control method based on artificial intelligence controls the execution equipment to perform operation based on optimal operation parameters, including:

[0025] Obtain the obtained optimal operating parameters, and split the optimal operating parameters into main parts based on the execution devices to obtain a sub-optimal operating parameter for each execution device;

[0026] Determine the business action and corresponding device component of each execution device, and convert the execution parameters of each sub-optimal operation parameter into the device component based on the business action;

[0027] Obtaining control parameters of each device component based on the execution parameter conversion, and generating personalized control instructions for each device component in the execution device based on the control parameters;

[0028] Control the execution equipment to perform work operations based on personalized control instructions.

[0029] An artificial intelligence-based regional precision quantitative operation control system, comprising:

[0030] A three-dimensional scanning module is used to perform three-dimensional scanning on the target area to obtain three-dimensional data of the target area and collect environmental data of the target area;

[0031] The simulation module is used to simulate the target area based on 3D data and environmental data, and determine the optimal operation parameters based on the simulation results and actual operation requirements;

[0032] The central control execution module is used to control the execution equipment to perform operation based on the optimal operation parameters and dynamically monitor the target area. When the dynamic monitoring results do not meet the expected standards, the pulse width of the execution equipment is adjusted until the operation task of the target area is completed.

[0033] Preferably, an artificial intelligence-based regional precision quantitative operation control system performs three-dimensional scanning of the target area by: LiDAR three-dimensional scanning; simulation is: CFD simulation; dynamic monitoring of the target area is based on the results of real-time scanning of the target area by LiDAR three-dimensional scanning; variable adjustment of the pulse width of the execution equipment is achieved through PWN adjustment.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] By acquiring the three-dimensional data and environmental data of the target area, effective and reliable simulation of the target area can be achieved, and then the optimal operating parameters corresponding to the actual operating requirements can be determined based on the simulation results, providing support for precise quantitative operation control of the area. Finally, the execution equipment is controlled according to the obtained optimal operating parameters, and the target area is dynamically monitored during the control process, so as to facilitate corresponding dynamic adjustments when the preset standards are not met, ensuring the reliable execution of the operation tasks in the target area, reducing losses and improving operation efficiency.

[0036] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 This is a flow chart of a method for regional precise quantitative operation control based on artificial intelligence in an embodiment of the present invention;

[0040] Figure 2 This is a structural diagram of an artificial intelligence-based regional precision quantitative operation control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0042] In one embodiment, a regional precise quantitative operation control method based on artificial intelligence is provided, such as Figure 1 Shown, including:

[0043] Step 1: Perform a three-dimensional scan of the target area to obtain three-dimensional data of the target area and collect environmental data of the target area;

[0044] Environmental data of the target area include: meteorological data (such as wind speed, wind direction, temperature and humidity, etc.);

[0045] The LiDAR three-dimensional scanning module scans a specific area to obtain three-dimensional data, while collecting environmental data required for CFD simulation and transmitting this data to the central control unit for preliminary analysis and processing.

[0046] Step 2: Simulate the target area based on 3D data and environmental data, and determine the optimal operating parameters based on the simulation results and actual operating requirements;

[0047] The CFD simulation module performs simulation calculations based on the collected data to obtain effect predictions under different operating conditions. The central control unit determines the optimal operating parameters based on the simulation results and actual operating requirements;

[0048] Analyze the three-dimensional data of the target area to determine the first characteristic information of the target area and the second characteristic information of the target operation object. At the same time, construct a first area model of the target area based on the first characteristic information and the second characteristic information, wherein the first characteristic information includes: terrain undulation and obstacle distribution in the target area; the second characteristic information includes: information such as the position and shape of the target operation object in the target area; the first area model is a model obtained after spatial simulation of the target area; map and associate the environmental data in the first area model to obtain a second area model, wherein the second area model is a model simulated after adding the environmental data to the first spatial model; call different operation parameters that need to be simulated, wherein the operation parameters include: spraying angle, flow rate, particle diffusion, etc.); simulate each operation parameter in the second area model respectively, output the operation effect corresponding to each operation parameter, and select the optimal operation parameter combination according to the operation effect; obtain actual operation requirements; adjust the optimal operation parameter combination according to the actual operation requirements to obtain the target optimal parameter combination.

[0049] Step 3: Control the execution equipment to perform the operation based on the optimal operation parameters, and dynamically monitor the target area. When the dynamic monitoring results do not meet the expected standards, the pulse width of the execution equipment is variable adjusted until the operation task of the target area is completed.

[0050] Dynamic monitoring of the target area includes: continuously monitoring the target area based on LiDAR during the operation, feeding back the monitored data to the central processing mechanism, and analyzing the monitored three-dimensional data in real time based on the central processing mechanism to determine the actual operation status. When the actual operation status deviates from the expected standard, the central processing mechanism generates an operation parameter adjustment instruction, and transmits the parameter adjustment instruction to the variable adjustment mechanism to adjust the pulse width of the execution device according to the PWN to accurately control the motor speed, valve opening, etc. of the execution device, thereby achieving precise adjustment of the operation volume (such as spraying flow, fertilizer application amount, etc.) to ensure operation accuracy.

[0051] In this embodiment, the target area refers to an area where precise quantitative operation control is required, for example, a field in an agricultural area.

[0052] In this embodiment, the three-dimensional data refers to the three-dimensional point cloud data of the target area. The purpose is to construct a corresponding simulation model through the three-dimensional data, thereby facilitating the determination of optimal operating parameters.

[0053] In this embodiment, the environmental data refers to various types of data related to the environment, such as humidity, temperature, and dust conditions in the target area.

[0054] In this embodiment, the actual operation demand refers to the business executed under the current operation type and the purpose or effect that the current business ultimately needs to achieve, such as the agricultural fertilization effect.

[0055] In this embodiment, the optimal operating parameters refer to the specific execution parameters corresponding to the precise quantitative operation control of the target area, including the specific data of the operation of each equipment. For example, in agriculture, the parameters may be the spraying angle, flow rate, and particle diffusion of the equipment.

[0056] In this embodiment, the execution device is a specific machine or tool used to meet the requirements of regional precise quantitative operation control.

[0057] In this embodiment, the preset standard is set in advance and is used to measure whether the regional precise quantitative operation control of the target area meets the required standard, and can be adjusted.

[0058] In this embodiment, the pulse width can be adjusted according to the pulse width of the execution device by PWN to accurately control the motor speed, valve opening, etc. of the execution device, thereby achieving precise adjustment of the operation volume (such as spraying flow, fertilizer amount, etc.) to ensure operation accuracy.

[0059] In this embodiment, variable adjustment refers to adjusting the pulse width of the execution device in different situations according to the working status of the execution device, with the purpose of ensuring effective execution of the work task in the target area.

[0060] The present invention provides an artificial intelligence-based regional precise quantitative operation control method, which has the following beneficial effects: by acquiring three-dimensional data and environmental data of the target area, effective and reliable simulation of the target area is achieved, and then the optimal operation parameters corresponding to the actual operation requirements are determined according to the simulation results, providing support for regional precise quantitative operation control. Finally, the execution equipment is controlled according to the obtained optimal operation parameters, and the target area is dynamically monitored during the control process, so as to facilitate corresponding dynamic adjustments when the preset standards are not met, thereby ensuring the reliable execution of the operation tasks in the target area, reducing losses, and improving operation efficiency.

[0061] In one embodiment, the method includes: performing three-dimensional scanning of the target area by LiDAR three-dimensional scanning; performing simulation by CFD simulation; performing dynamic monitoring of the target area by real-time scanning of the target area by LiDAR three-dimensional scanning; and performing variable adjustment of the pulse width of the execution device by PWN adjustment.

[0062] In one embodiment, performing a three-dimensional scan of a target area includes: emitting a laser beam at the target area using a three-dimensional scanning device and receiving reflected signals; and generating a three-dimensional point cloud map of the target area based on the reflected signals. The three-dimensional point cloud map is the three-dimensional data of the target area. The three-dimensional scanning device is a LiDAR device. The LiDAR device performs a full-scale scan of a specific area, emitting laser beams and receiving reflected signals to rapidly generate a high-precision three-dimensional point cloud map. By processing and analyzing the three-dimensional point cloud data, information such as terrain undulations, obstacle distribution, and the location and shape of target work objects within the area is identified and transmitted in real time to a central control unit.

[0063] To ensure the accuracy, reliability, and effectiveness of obtaining the optimal operating parameter combination, in one embodiment, a regional precise quantitative operating control method based on artificial intelligence is provided. Each operating parameter is simulated in the second regional model, and the operating effect corresponding to each operating parameter is output. The optimal operating parameter combination is selected based on the operating effect, including:

[0064] S1: Obtaining baseline attribute information of the operation object; wherein the baseline attribute information of the operation object is pre-set, and is generally obtained based on historical patterns, including the area of the operation area, the operation volume (e.g., the number of planted seedlings), and other attribute information, and the interval where the operation object is located belongs to the target area;

[0065] S2: Obtain the parameter type of the operation parameter, and obtain the parameter change range under each parameter type according to the baseline attribute information of the operation object;

[0066] S3: performing parameter positioning for the parameter variation interval under each parameter type according to a preset interval, and determining a discrete parameter value for the parameter variation interval under each parameter type;

[0067] S4: selecting any one type of operation parameter from different operation parameters as an independent variable, wherein the value of the independent variable is a discrete parameter value, and the remaining types of operation parameters are used as quantitative values. Simultaneously, the independent variable and the quantitative value are input into the second regional model to perform a first simulation of the operation object according to the discrete parameter value, and outputting an operation effect evaluation score corresponding to the discrete parameter value under the current type according to the second regional model; wherein the parameter value used in the quantitative value is set in advance and is set based on experience;

[0068] S5: extracting the best effect evaluation score from the multiple discrete parameter values, locating the target parameter value corresponding to the best effect evaluation score, and using the target parameter value as the optimal parameter value under the current category;

[0069] S6: Repeat steps S4-S5 to obtain the optimal parameter values under all parameter types;

[0070] In order to ensure that the optimal parameter values obtained under each parameter type achieve the maximum effect during the application process, it is also necessary to optimize and determine the optimal parameter values under each parameter type. The specific process is as follows: when the optimal parameter values under all parameter types are obtained, the optimal parameter values under all parameter types are input into the second regional model for a second simulation to obtain the target operation effect evaluation score;

[0071] Compare the target operation effect evaluation score with the preset effect evaluation threshold to determine whether the optimal parameter value needs to be optimized; the preset effect evaluation threshold is set in advance and used as a measurement standard for evaluating whether optimization is needed;

[0072] When the target operation effect evaluation score is less than the preset effect evaluation threshold, it is determined that the optimal parameter value needs to be optimized; otherwise, it is determined that the optimal parameter value does not need to be optimized;

[0073] When it is necessary to optimize the optimal parameter value, change the parameter value corresponding to any parameter type and observe the value changes corresponding to the remaining parameter types; analyze the value changes based on the fitting model, output the correlation between the parameter types, and determine the balance factor based on the correlation between the parameter types, where the balance factor is used to characterize the influence coefficient of the interaction between the parameter types; adjust the optimal parameter value under each parameter type according to the balance factor, and update the optimal parameter value. The updated optimal parameter values under each parameter type are integrated to obtain the optimal operation parameter combination.

[0074] When adjusting the optimal operation parameter combination according to the actual operation requirements to obtain the target optimal parameter combination, it includes: reading the baseline attribute data of the operation object, and obtaining the demand attribute data corresponding to the actual operation requirements (that is, the operation area, operation volume and other information of the actual operation object); obtaining the ratio value between the baseline attribute data and the demand attribute data, and adjusting the optimal operation parameter combination according to the ratio value to obtain the target optimal parameter combination.

[0075] In one embodiment, controlling the execution device to execute the operation based on the optimal operation parameters includes:

[0076] The optimal operating parameters are obtained, and the optimal operating parameters are split into main parts based on the execution device to obtain a sub-optimal operating parameter for each execution device; the business action and corresponding device component of each execution device are determined, and the execution parameter conversion of each sub-optimal operating parameter to the device component is performed based on the business action; the control parameters of each device component are obtained based on the execution parameter conversion, and personalized control instructions for each device component in the execution device are generated based on the control parameters; the execution device is controlled to perform operating operations based on the personalized control instructions.

[0077] The working principle and beneficial effects of the above technical solution are: by splitting the optimal operating parameters into the main body (split the optimal operating parameters into specific contents corresponding to each executing device according to the executing device, and the splitting result is the sub-optimal operating parameters corresponding to each executing device), the operating parameters of each executing device are determined; secondly, the sub-optimal operating parameters are formatted according to the business actions of each executing device (specific business to be executed, such as spraying actions, etc.) and equipment components to obtain specific execution data corresponding to each equipment component; finally, the control parameters of each equipment component (specific control data, including working time, etc.) are determined according to the execution parameter conversion results to generate corresponding control instructions (personalized control instructions), and the operating operations of the executing device are controlled according to the control instructions, ensuring accurate and reliable control of the executing device according to the optimal operating parameters, and also ensuring the effective execution of the operation.

[0078] In one embodiment, a regional precision quantitative operation control system based on artificial intelligence is provided. Figure 2 Shown, including:

[0079] A three-dimensional scanning module is used to perform three-dimensional scanning on the target area to obtain three-dimensional data of the target area and collect environmental data of the target area;

[0080] The simulation module is used to simulate the target area based on 3D data and environmental data, and determine the optimal operation parameters based on the simulation results and actual operation requirements;

[0081] The central control execution module is used to control the execution equipment to perform operation based on the optimal operation parameters and dynamically monitor the target area. When the dynamic monitoring results do not meet the expected standards, the pulse width of the execution equipment is adjusted until the operation task of the target area is completed.

[0082] Among them, three-dimensional scanning of the target area is: LiDAR three-dimensional scanning; simulation is: CFD simulation; dynamic monitoring of the target area is based on the result of real-time scanning of the target area by LiDAR three-dimensional scanning; variable adjustment of the pulse width of the execution device is achieved through PWN adjustment.

[0083] The working principle and beneficial effects of the above technical solution are: by acquiring the three-dimensional data and environmental data of the target area, effective and reliable simulation of the target area is achieved, and then the optimal operating parameters corresponding to the actual operating requirements are determined according to the simulation results, providing support for precise quantitative operation control of the area. Finally, the execution equipment is controlled according to the obtained optimal operating parameters, and the target area is dynamically monitored during the control process, so as to facilitate corresponding dynamic adjustments when the preset standards are not met, ensuring the reliable execution of the operation tasks in the target area, reducing losses and improving operation efficiency.

[0084] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A regional precise quantitative operation control method based on artificial intelligence, characterized in that: include: Perform three-dimensional scanning on the target area to obtain three-dimensional data of the target area, and at the same time, collect environmental data of the target area; Simulate the target area based on 3D data and environmental data, and determine the optimal operating parameters based on the simulation results and actual operating requirements; The control execution equipment performs the operation based on the optimal operation parameters and dynamically monitors the target area. When the dynamic monitoring results do not meet the expected standards, the pulse width of the execution equipment is variable adjusted until the operation task of the target area is completed.

2. The method for regional precise quantitative operation control based on artificial intelligence according to claim 1, characterized in that: include: The three-dimensional scanning of the target area is: LiDAR three-dimensional scanning; The simulation is: CFD simulation; Dynamic monitoring of the target area is based on the results of real-time scanning of the target area by LiDAR three-dimensional scanning; variable adjustment of the pulse width of the execution device is achieved through PWN adjustment.

3. The method for regional precise quantitative operation control based on artificial intelligence according to claim 1, characterized in that: Simulate the target area based on 3D data and environmental data, and determine the optimal operating parameters based on the simulation results and actual operating requirements, including: Analyzing the three-dimensional data of the target area, determining characteristic information of the target area, and constructing a first area model based on the characteristic information; Mapping and associating the environmental data in the first regional model to obtain a second regional model; Retrieve different operation parameters that need to be simulated; simulate each operation parameter in the second area model respectively, output the operation effect corresponding to each operation parameter, and select the optimal operation parameter combination based on the operation effect; Obtain actual operation requirements; adjust the optimal operation parameter combination according to the actual operation requirements to obtain the target optimal parameter combination.

4. The method for regional precise quantitative operation control based on artificial intelligence according to claim 1, characterized in that: Perform a 3D scan of the target area, including: Based on the three-dimensional scanning device, a laser beam is emitted to the target area and a reflected signal is received; A three-dimensional point cloud map of the target area is generated based on the reflected signal, wherein the three-dimensional point cloud map is the three-dimensional data of the target area.

5. The method for regional precise quantitative operation control based on artificial intelligence according to claim 3 is characterized in that: Simulate each operating parameter in the second area model, output the operating effect corresponding to each operating parameter, and select the optimal operating parameter combination according to the operating effect, including Obtaining the baseline attribute information of the operation object; obtaining the parameter type of the operation parameter, and obtaining the parameter change range under each parameter type according to the baseline attribute information of the operation object; The parameter change interval under each parameter type is positioned according to the preset interval, and the discrete parameter values of the parameter change interval under each parameter type are determined; the discrete parameter values corresponding to the parameter change interval under each parameter type are analyzed, and the optimal operation parameter combination is determined based on the analysis results.

6. The method for regional precise quantitative operation control based on artificial intelligence according to claim 3 is characterized in that: When adjusting the optimal operating parameter combination according to actual operating requirements to obtain the target optimal parameter combination, it includes: Read the baseline attribute data of the operation object and obtain the demand attribute data corresponding to the actual operation demand; obtain the ratio value between the baseline attribute data and the demand attribute data, adjust the optimal operation parameter combination according to the ratio value, and obtain the target optimal parameter combination.

7. The method for regional precise quantitative operation control based on artificial intelligence according to claim 5, characterized in that: After determining the optimal operating parameter combination, it also includes optimizing and judging the optimal operating parameter combination, and when optimization is required, adjusting and updating the optimal operating parameters to obtain the final optimal operating parameter combination.

8. The method for regional precise quantitative operation control based on artificial intelligence according to claim 1, characterized in that: Controlling the execution equipment to perform the operation based on the optimal operation parameters, including: Obtain the obtained optimal operating parameters, and split the optimal operating parameters into main parts based on the execution devices to obtain a sub-optimal operating parameter for each execution device; Determine the business action and corresponding device component of each execution device, and convert the execution parameters of each sub-optimal operation parameter into the device component based on the business action; Obtaining control parameters of each device component based on the execution parameter conversion, and generating personalized control instructions for each device component in the execution device based on the control parameters; Control the execution equipment to perform work operations based on personalized control instructions.

9. A regional precise quantitative operation control system based on artificial intelligence, characterized in that: include: A three-dimensional scanning module is used to perform three-dimensional scanning on the target area to obtain three-dimensional data of the target area and collect environmental data of the target area; The simulation module is used to simulate the target area based on 3D data and environmental data, and determine the optimal operation parameters based on the simulation results and actual operation requirements; The central control execution module is used to control the execution equipment to perform operation based on the optimal operation parameters and dynamically monitor the target area. When the dynamic monitoring results do not meet the expected standards, the pulse width of the execution equipment is adjusted until the operation task of the target area is completed.

10. The regional precise quantitative operation control system based on artificial intelligence according to claim 9 is characterized in that: The three-dimensional scanning of the target area is: LiDAR three-dimensional scanning; The simulation is: CFD simulation; dynamic monitoring of the target area is based on the results of real-time scanning of the target area by LiDAR three-dimensional scanning; variable adjustment of the pulse width of the execution device is achieved through PWN adjustment.

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