Multi-mode beam shaper

Abstract

The present invention discloses a multimodal beam shaper, which includes a central control module and a control unit. The central control module includes a scene recognition system and an artemisinin optimization algorithm, and multiple scenes are monitored and identified in real time through an integrated scene recognition system. On this basis, the artemisinin optimization algorithm is used to accurately control key control units such as aperture adjusters, frequency controllers, and light intensity transmitters, and optimizes the aperture shape, beam color, and laser intensity to achieve the optimal configuration of power consumption. Compared with the prior art, the present invention can adaptively adjust the beam shape according to specific scene requirements, while ensuring high efficiency and losslessness during the beam shaping process, significantly reducing energy loss, improving the sustainability of energy utilization, and meeting specific intensity requirements, and has wide applicability.

Description

Technical Field

[0001] The invention relates to a beam shaper, and in particular to a multi-mode beam shaper. Background Art

[0002] Beam shaping is the process of changing the shape and characteristics of a beam through technical means, and is widely used in the field of laser technology. At present, the aperture method, field mapping method and multi-aperture beam focusing method are mainly used for beam shaping. These methods each have their own advantages and disadvantages. As a traditional beam shaping technology, the aperture method has the advantages of simple principle and convenient operation, but it will cause energy loss of the input beam. The field mapping method is extremely sensitive to the setting of boundary conditions, and its application in nonlinear optical materials or scenarios with dynamic changes in beams is limited; the multi-aperture beam focusing method is suitable for multi-mode lasers with low spatial coherence, and its scope of application is relatively narrow. Summary of the invention

[0003] Purpose of the invention: In response to the above problems, the present invention proposes a multimodal beam shaper that can adaptively adjust the beam shape according to the needs of specific scenarios, while achieving efficient and lossless beam shaping, significantly reducing energy loss, improving the sustainability of energy utilization, and meeting specific intensity requirements, showing wide applicability.

[0004] Technical solution: The technical solution adopted by the present invention is a multimodal beam shaper, including a scene recognition system, a central control module and a control unit; the scene recognition system is used to recognize the use scene through image data, and determine the constraints of the aperture shape, beam color and laser intensity of the multimodal beam shaper in combination with the use scene and environmental information, and send the constraints to the central control module; the central control module uses the artemisinin optimization algorithm to obtain the optimized aperture shape, beam color and laser intensity parameters based on the aperture shape, beam color and laser intensity constraints of the multimodal beam shaper with the goal of minimum energy consumption; the control unit includes an aperture adjuster, a frequency controller and a light intensity controller, which respectively control the aperture shape, beam color and laser intensity; the central control module controls the aperture shape, beam color and laser intensity through the control unit according to the optimized aperture shape, beam color and laser intensity parameters to achieve the configuration with the best power consumption.

[0005] The scene recognition system includes an image sensor and an environmental parameter sensor. The image sensor is used to collect scene image data and send it to the scene recognition system, and the environmental parameter sensor is used to collect environmental information and send it to the scene recognition system.

[0006] The scene recognition system uses a scene recognition algorithm to recognize the use scene of the beam shaper.

[0007] The artemisinin optimization algorithm comprises the following steps:

[0008] (1) Initialize the population, the calculation formula is:

[0009]

[0010] Among them, Q is the initialization population, q i,j is the element in the i-th row and j-th column of the matrix, representing all beams in the solution space, n is the number of search agents, and d represents the multidimensional component within the search agent;

[0011] (2) The objective function formula is as follows:

[0012] U fit =F{U now ·exp(iφ now )}

[0013] I fit =k1U fit +k2i

[0014]

[0015] G fit =f(I fit ,e fit )

[0016] Among them, U fit is the aperture shape, F is the Fourier transform, U now is the current aperture shape, exp is the exponential operation, iφ now is the phase modulation function, I fit is the laser intensity, k1 and k2 are the correlation coefficients between the aperture shape and the power density, i is the power density, e is the fit is the beam color, e is the initial beam color, f and Maxf are the current and maximum iterations of the algorithm, G fit =f(·) is the fitness function, G fit is the objective function value, i.e., energy consumption;

[0017] The fitness function is:

[0018] G fit =σ i I fit +σ L ·e fit

[0019] Among them, σ i , σ L are the power regulation coefficients of the frequency controller and the light intensity controller equipment respectively;

[0020] (3) Bring the population into the objective function for evaluation and sort the population;

[0021] (4) Introducing the probability coefficient K, the calculation formula is:

[0022]

[0023] (5) Comprehensive algorithm evaluation, using a comprehensive elimination phase strategy to continuously eliminate beams that do not meet the requirements:

[0024]

[0025] In the formula represents the beam that meets the requirements in the solution space for the t+1th update, represents the beam obtained by the tth calculation in the solution space, represents the beam shaping group value of the jth dimension with the highest ranking in the solution space, rand represents a random number in the range of [0,1]; r1 is a random number in the range of [0,1], and K is the probability coefficient;

[0026] (6) In the post-consolidation stage of the algorithm, the calculation formula is:

[0027]

[0028] In the formula, Ogbest i,j represents the subvector of the current optimal solution of the i-th j-th dimension;

[0029] (7) Determine whether the set maximum number of iterations has been reached. If not, return to step 3). If so, end the calculation and output the optimization parameters.

[0030] The present invention provides a beam control method applied to a multi-mode beam shaper, the method comprising:

[0031] Identify the usage scenario through image data, and determine the aperture shape, beam color and laser intensity constraints of the multi-modal beam shaper by combining the usage scenario and environmental information;

[0032] According to the constraints of the aperture shape, beam color and laser intensity of the multi-modal beam shaper, and with the goal of minimum energy consumption, the artemisinin optimization algorithm is used to obtain the optimized aperture shape, beam color and laser intensity parameters;

[0033] According to the optimized aperture shape, beam color and laser intensity parameters, the aperture shape, beam color and laser intensity are controlled by the control unit to achieve the optimal configuration for power consumption.

[0034] Identifying the usage scenario through image data is to use a scene recognition algorithm to identify the usage scenario of the beam shaper.

[0035] The artemisinin optimization algorithm comprises the following steps:

[0036] (1) Initialize the population, the calculation formula is:

[0037]

[0038] Among them, Q is the initialization population, q i,j is the element in the i-th row and j-th column of the matrix, representing all beams in the solution space, n is the number of search agents, and d represents the multidimensional component within the search agent;

[0039] (2) The objective function formula is as follows:

[0040] U fit =F{U now ·exp(iφ now )}

[0041] I fit =k1U fit +k2i

[0042]

[0043] G fit =f(I fit ,e fit )

[0044] Among them, U fit is the aperture shape, F is the Fourier transform, U now is the current aperture shape, exp is the exponential operation, iφ now is the phase modulation function, I fit is the laser intensity, k1 and k2 are the correlation coefficients between the aperture shape and the power density, i is the power density, e is the fit is the beam color, e is the initial beam color, f and Maxf are the current and maximum iterations of the algorithm, G fit =f(·) is the fitness function, G fit is the objective function value, i.e., energy consumption;

[0045] The fitness function is:

[0046] G fit =σ i I fit +σ L ·e fit

[0047] Among them, σ i , σ L are the power regulation coefficients of the frequency controller and the light intensity controller equipment respectively;

[0048] (3) Bring the population into the objective function for evaluation and sort the population;

[0049] (4) Introducing the probability coefficient K, the calculation formula is:

[0050]

[0051] (5) Comprehensive algorithm evaluation, using a comprehensive elimination phase strategy to continuously eliminate beams that do not meet the requirements:

[0052]

[0053] In the formula represents the beam that meets the requirements in the solution space for the t+1th update, represents the beam obtained by the tth calculation in the solution space, represents the beam shaping group value of the jth dimension with the highest ranking in the solution space, rand represents a random number in the range of [0,1]; r1 is a random number in the range of [0,1], and K is the probability coefficient;

[0054] (6) In the post-consolidation stage of the algorithm, the calculation formula is:

[0055]

[0056] In the formula, Ogbest i,j represents the subvector of the current optimal solution of the i-th j-th dimension;

[0057] (7) Determine whether the set maximum number of iterations has been reached. If not, return to step 3). If so, end the calculation and output the optimization parameters.

[0058] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the beam control method applied to a multi-mode beam shaper.

[0059] The present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the beam control method applied to a multi-mode beam shaper is implemented.

[0060] The present invention provides a computer program product, comprising a computer program and / or instructions, which implement the beam control method applied to a multi-mode beam shaper when executed by a processor.

[0061] Beneficial effects: Compared with the prior art, the present invention has the following advantages: 1. The scene recognition system can accurately identify complex and changeable scenes, monitor and identify multiple scenes in real time, and adjust the aperture shape adaptively according to the needs of different scenes to meet the diverse needs of the scenes. The multimodal beam shaper can be specifically applied to laser material processing (such as laser welding, cutting, punching, etc.); medical cosmetic treatment (such as laser treatment, ophthalmic surgery); optical research and precision measurement (such as microscope illumination, metrology applications, etc.) and other fields. 2. The present invention uses the central control module to accurately control the key control unit, realizes the optimal configuration of power consumption, significantly reduces energy loss, improves energy utilization, has the advantages of high efficiency and energy saving, helps to reduce operating costs, and improves economic benefits. 3. The artemisinin optimization algorithm proposed by the intelligent optimization module of the present invention is a meta-optimization algorithm based on nature inspiration. The algorithm can maintain a balance between exploration and development during the optimization process, avoid local optimal situations, and effectively search the space and find high-quality solutions, so that the control unit can more accurately adjust the corresponding variables to achieve the purpose of optimizing power consumption. In summary, the present invention can adaptively adjust the beam shape according to the needs of specific scenarios, while achieving efficient and lossless beam shaping, greatly reducing energy loss, improving the sustainability of energy utilization, and meeting specific intensity requirements, showing wide applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a schematic structural diagram of the multi-mode beam shaper of the present invention;

[0063] Figure 2 is a schematic diagram of the operation logic of the multi-mode beam shaper of the present invention;

[0064] Figure 3 It is a flow chart of the artemisinin algorithm described in the present invention. DETAILED DESCRIPTION

[0065] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0066] Example 1

[0067] The multi-mode beam shaper of the present invention has a structure as follows: Figure 1As shown. The multimodal beam shaper includes a scene recognition system, a central control module and a control unit; the scene recognition system is used to identify the use scene through image data, and determine the aperture shape, beam color and laser intensity constraints of the multimodal beam shaper in combination with the use scene and environmental information, and send the constraints to the central control module; the central control module uses the artemisinin optimization algorithm to obtain the optimized aperture shape, beam color and laser intensity parameters based on the aperture shape, beam color and laser intensity constraints of the multimodal beam shaper with the goal of minimum energy consumption; the control unit includes an aperture adjuster, a frequency controller and a light intensity controller, which respectively control the aperture shape, beam color and laser intensity; the central control module controls the aperture shape, beam color and laser intensity through the control unit according to the optimized aperture shape, beam color and laser intensity parameters to achieve the optimal power consumption configuration. The control unit includes an aperture adjuster, a frequency controller and a light intensity controller, which respectively control the aperture shape, beam color and laser intensity. The image sensor collects scene image data, and the environmental parameter sensor collects environmental information such as temperature, humidity, and light intensity, all of which are sent to the scene recognition system. The scene recognition system is used to monitor and identify a variety of scenes in real time, and determine the beam constraints based on the environmental information. The central control module contains a central processing unit (CPU) to calculate the artemisinin optimization algorithm and make intelligent decisions. The central control module makes intelligent decisions based on the strategy provided by the artemisinin optimization algorithm and accurately adjusts the control unit to achieve the optimal energy consumption configuration of the system.

[0068] Example 2

[0069] The beam control method applied to a multi-mode beam shaper of the present invention comprises:

[0070] The usage scenario is identified through image data, and the aperture shape, beam color and laser intensity constraints of the multimodal beam shaper are determined in combination with the usage scenario and environmental information; wherein the usage scenario is identified through image data, and the usage scenario of the beam shaper is identified by using a scene recognition algorithm.

[0071] According to the constraints of the aperture shape, beam color and laser intensity of the multimodal beam shaper, and with the goal of minimum energy consumption, the artemisinin optimization algorithm is used to obtain the optimized aperture shape, beam color and laser intensity parameters.

[0072] According to the optimized aperture shape, beam color and laser intensity parameters, the aperture shape, beam color and laser intensity are controlled by the control unit to achieve the optimal configuration for power consumption.

[0073] like Figure 2The figure shows the operation logic diagram of the multimodal beam shaper. The artemisinin optimization algorithm outputs the optimal operation strategy of the device based on the real-time data collected by the image sensor module and the data pre-stored by the central processing unit (CPU); the CPU makes intelligent decisions based on the strategy provided by the artemisinin optimization algorithm and accurately adjusts the control unit to achieve the optimal energy consumption configuration of the system. The scene recognition system accurately identifies complex and changing scenes, and the application areas covered include: laser material processing (such as laser welding, cutting, punching, etc.); medical cosmetic treatment (such as laser treatment, ophthalmic surgery); optical research and precision measurement (such as microscope illumination, metrology applications, etc.). The artemisinin optimization algorithm executed by the central control module dynamically adjusts the aperture shape, beam color, and laser intensity to achieve the optimal configuration of the system in terms of energy consumption. This design enables the multimodal beam shaper to operate efficiently in a variety of scenarios while minimizing energy consumption.

[0074] like Figure 3 The figure shows a flowchart of the artemisinin algorithm of the present invention. The control unit uses the artemisinin optimization algorithm to provide an optimization strategy, and the specific optimization process is as follows:

[0075] (1) Initialization phase: Initialize the entire population, denoted as Q, set T1 and B1 to represent the boundaries of the solution space, and R to represent a set of random number sequences, whose value range is [0,1]. The complete population consists of n search agents, where d represents the multidimensional components within the search agent. This reflects the adaptability of the multimodal beam shaper to diverse scenarios.

[0076]

[0077] Among them, q i,j is the element in the i-th row and j-th column of the matrix, representing all beams in the solution space.

[0078] (2) Evaluate the objective function, input the aperture shape, beam color, and laser intensity into the objective function to be evaluated, and the objective function formula is as follows:

[0079] U fit =F{U now ·exp(iφ now )}

[0080] I fit =k1U fit +k2i

[0081]

[0082] G fit =f(I fit ,e fit )

[0083] Among them, U fit is the aperture shape, F is the Fourier transform, U now is the current aperture shape, exp is the exponential operation, iφ now is the phase modulation function, I fit is the laser intensity, k1 and k2 are the correlation coefficients between the aperture shape and the power density, i is the power density, e is the fit is the beam color, e is the initial beam color, f and Maxf are the current and maximum iterations of the algorithm, G fit =f(·) is the fitness function, G fit This is the desired objective function value, i.e., energy consumption.

[0084] Optionally, the fitness function is:

[0085] G fit =σ i I fit +σ L ·e fit

[0086] σ i , σ L It is the power regulation factor of frequency controller and light intensity transmitter equipment.

[0087] (3) The initial population is brought into the objective function for evaluation, and the population is sorted. The group value of the beam shaping corresponding to the population with the highest sorting degree is taken as Ogbest.

[0088] (4) Considering the differences in aperture shape, beam frequency, laser intensity, suitability for diverse scenarios, power consumption, and the accuracy of variable adjustment by the control unit, in order to summarize this inherent variability, we introduced a probability coefficient K, the evaluation coefficient of the comprehensive algorithm;

[0089]

[0090] (5) The evaluation progress of the comprehensive algorithm is simulated, and according to the scenario requirements, a comprehensive elimination phase strategy is adopted to continuously eliminate beams that do not meet the requirements within a range.

[0091]

[0092] Where r1 is a random number in the range of [0,1]. After the initial elimination stage, the system will gradually transition to the maintenance stage to ensure that the optimal power consumption can be achieved in the end. represents the beam that meets the requirements in the solution space for the t+1th update, represents the beam obtained by the tth calculation in the solution space, Indicates that the order of the beam shaping group value of the i-th j-th dimension in the solution space is higher, and rand represents a random number in [0,1]. Beams with r1≥K are eliminated.

[0093] (6) In the post-consolidation phase of the algorithm, this part aims to identify potential uncertainties such as temperature, humidity, noise, etc. and simulate this specific situation.

[0094]

[0095] In this equation, Ogbest i,j The subvector represents the current optimal solution of the jth dimension. This strategy enhances the ability of the search agent to escape the local optimum and effectively avoids the local optimum trap, thus providing a guarantee for the stability and reliability of the power consumption optimization device.

[0096] (7) Determine whether the set maximum number of iterations has been reached. If not, return to step 3). If so, end the calculation and output the optimization parameters.

[0097] By continuously adjusting the aperture shape, beam color, and laser intensity to adapt to diverse scenarios, the device can ultimately achieve optimal power consumption.

[0098] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned beam control method for a multi-modal beam shaper when executing the computer program.

[0099] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the computer program implements the above-mentioned beam control method applied to a multi-mode beam shaper.

[0100] In one embodiment, a computer program product is provided, comprising a computer program / instruction, which, when executed by a processor, implements the beam control method for a multi-mode beam shaper.

[0101] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A multi-mode beam shaper, characterized in that: It includes a scene recognition system, a central control module and a control unit; the scene recognition system is used to recognize the use scene through image data, determine the aperture shape of the multimodal beam shaper, the constraint conditions of the beam color and the laser intensity in combination with the use scene and environmental information, and send the constraint conditions to the central control module; the central control module uses the artemisinin optimization algorithm to obtain the optimized aperture shape, beam color and laser intensity parameters based on the aperture shape, beam color and laser intensity constraints of the multimodal beam shaper and the minimum energy consumption as the goal; the control unit includes an aperture regulator, a frequency controller and a light intensity controller, which respectively control the aperture shape, beam color and laser intensity; the central control module controls the aperture shape, beam color and laser intensity through the control unit according to the optimized aperture shape, beam color and laser intensity parameters to achieve the configuration with the best power consumption; The artemisinin optimization algorithm comprises the following steps: (1) Initialize the population, the calculation formula is: Among them, Q is the initialization population, q i,j is the element in the i-th row and j-th column of the matrix, representing all beams in the solution space, n is the number of search agents, and d represents the multidimensional component within the search agent; (2) The objective function formula is as follows: U fit =F{U now ·exp(iφ now )} I fit =k1U fit +k2i G fit =f(I fit ,e fit ) Among them, U fit is the aperture shape, F is the Fourier transform, U now is the current aperture shape, exp is the exponential operation, iφ now is the phase modulation function, I fit is the laser intensity, k1 and k2 are the correlation coefficients between the aperture shape and the power density, i is the power density, e is the fit is the beam color, e is the initial beam color, f and Maxf are the current and maximum iterations of the algorithm, G fit =f(·) is the fitness function, G fit is the objective function value, i.e., energy consumption; The fitness function is: G fit =s i ·I fit +s L ·e fit Among them, σ i , σ L are the power regulation coefficients of the frequency controller and the light intensity controller equipment respectively; (3) Bring the population into the objective function for evaluation and sort the population; (4) Introducing the probability coefficient K, the calculation formula is: (5) Comprehensive algorithm evaluation, using a comprehensive elimination phase strategy to continuously eliminate beams that do not meet the requirements: In the formula represents the beam that meets the requirements in the solution space for the t+1th update, represents the beam obtained by the tth calculation in the solution space, represents the beam shaping group value of the jth dimension with the highest ranking in the solution space, rand represents a random number in the range of [0,1]; r1 is a random number in the range of [0,1], and K is the probability coefficient; (6) In the post-consolidation stage of the algorithm, the calculation formula is: In the formula, Ogbest i,j represents the subvector of the current optimal solution of the i-th j-th dimension; (7) Determine whether the set maximum number of iterations has been reached. If not, return to step 3). If so, end the calculation and output the optimization parameters.

2. The multi-mode beam shaper according to claim 1, characterized in that: The scene recognition system includes an image sensor and an environmental parameter sensor. The image sensor is used to collect scene image data and send it to the scene recognition system, and the environmental parameter sensor is used to collect environmental information and send it to the scene recognition system.

3. The multi-mode beam shaper according to claim 1, characterized in that: The scene recognition system uses a scene recognition algorithm to recognize the use scene of the beam shaper.

4. A beam control method for a multi-modal beam shaper, characterized in that: The method includes: Identify the usage scenario through image data, and determine the aperture shape, beam color and laser intensity constraints of the multi-modal beam shaper by combining the usage scenario and environmental information; According to the constraints of the aperture shape, beam color and laser intensity of the multi-modal beam shaper, and with the goal of minimum energy consumption, the artemisinin optimization algorithm is used to obtain the optimized aperture shape, beam color and laser intensity parameters; According to the optimized aperture shape, beam color and laser intensity parameters, the aperture shape, beam color and laser intensity are controlled by the control unit to achieve the optimal configuration of power consumption; The artemisinin optimization algorithm comprises the following steps: (1) Initialize the population, the calculation formula is: Among them, Q is the initialization population, q i,j is the element in the i-th row and j-th column of the matrix, representing all beams in the solution space, n is the number of search agents, and d represents the multidimensional component within the search agent; (2) The objective function formula is as follows: U fit =F{U now ·exp(iφ now )} I fit =k1U fit +k2i G fit =f(I fit ,e fit ) Among them, U fit is the aperture shape, F is the Fourier transform, U now is the current aperture shape, exp is the exponential operation, iφ now is the phase modulation function, I fit is the laser intensity, k1 and k2 are the correlation coefficients between the aperture shape and the power density, i is the power density, e is the fit is the beam color, e is the initial beam color, f and Maxf are the current and maximum iterations of the algorithm, G fit =f(·) is the fitness function, G fit is the objective function value, i.e., energy consumption; The fitness function is: G fit =s i ·I fit +s L ·e fit Among them, σ i , σ L are the power regulation coefficients of the frequency controller and the light intensity controller equipment respectively; (3) Bring the population into the objective function for evaluation and sort the population; (4) Introducing the probability coefficient K, the calculation formula is: (5) Comprehensive algorithm evaluation, using a comprehensive elimination phase strategy to continuously eliminate beams that do not meet the requirements: In the formula represents the beam that meets the requirements in the solution space for the t+1th update, represents the beam obtained by the tth calculation in the solution space, represents the beam shaping group value of the jth dimension with the highest ranking in the solution space, rand represents a random number in the range of [0,1]; r1 is a random number in the range of [0,1], and K is the probability coefficient; (6) In the post-consolidation stage of the algorithm, the calculation formula is: In the formula, Ogbest i,j represents the subvector of the current optimal solution of the i-th j-th dimension; (7) Determine whether the set maximum number of iterations has been reached. If not, return to step 3). If so, end the calculation and output the optimization parameters.

5. The beam control method for a multi-mode beam shaper according to claim 4, characterized in that: Identifying the usage scenario through image data is to use a scene recognition algorithm to identify the usage scenario of the beam shaper.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the beam control method applied to a multi-mode beam shaper described in claim 4 or 5 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the beam control method for a multi-mode beam shaper described in claim 4 or 5 is implemented.

8. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the beam control method for a multi-mode beam shaper as claimed in claim 4 or 5 is implemented.

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