A multi-device parallel sand prevention grading printing scheduling optimization method

Abstract

The present application relates to the technical field of industrial scheduling, and more particularly to a multi-device parallel sand prevention pattern printing scheduling optimization method, a scientific device thermal-mechanical coupling operation state quantitative system is constructed, the thermal accumulation saturation is calculated based on the material rheology characteristics, the degree of approximation of the device temperature relative to the performance failure critical point is accurately measured, and the dynamic stiffness retention rate is calculated by combining the feeding speed and the end vibration acceleration to represent the device mechanical performance state, and then the thermal-mechanical coupling stability potential is synthesized through an exponential decay model, fully reflecting the nonlinear coupling relationship between thermal accumulation and mechanical stiffness decay; the sand prevention pattern printing task is disassembled into multiple job units, the process potential demand is quantified based on the geometric characteristic analysis results of the job units, and then the task allocation is performed according to the matching relationship between the device thermal-mechanical coupling stability potential and the process potential demand of the job units, effectively improving the sand prevention pattern printing precision and the component forming quality.

Description

Technical Field

[0001] This invention relates to the field of industrial scheduling technology, and in particular to a method for optimizing the scheduling of multi-device parallel anti-sand grid printing. Background Technology

[0002] As a core protective component in desertification control, slope protection, and engineering sand fixation, the demand for sand-fixing grids continues to grow with the expansion of ecological governance and infrastructure construction. Traditional single-equipment printing methods, limited by equipment operating efficiency, cannot meet the demands of large-scale, high-efficiency sand-fixing grid production. Therefore, multi-equipment parallel printing technology has gradually become the core technology direction for the mass production of sand-fixing grids. The core of multi-equipment parallel printing lies in achieving coordinated operation of multiple printing devices through a reasonable scheduling strategy, thereby improving overall production efficiency. The scientific nature of the scheduling strategy directly determines the stability of equipment operation and the quality of printed products. Therefore, the optimization method for scheduling multi-equipment parallel sand-fixing grid printing has become a research focus in this field. In the existing technology system, the multi-device parallel printing scheduling method has been applied in some component production fields. Its technical approach mainly revolves around single-dimensional parameters such as equipment operating speed and task queue length to make scheduling decisions, providing a basic technical reference for multi-device parallel printing of sand-proof grids. However, sand-proof grid printing has technical characteristics such as significant differences in the geometric features of work units, easy heat accumulation in equipment joint modules during printing, and dynamic decay of mechanical stiffness with operating status. Existing scheduling methods are difficult to adapt to the special needs of sand-proof grid printing, and targeted technical optimization is urgently needed.

[0003] The shortcomings of existing technologies are as follows: First, the perception of equipment operating status is limited to a single dimension. Existing scheduling methods mostly collect basic parameters such as the feed speed and task completion progress of the printing equipment, without considering core parameters affecting the stability of equipment operation, such as heat accumulation of equipment joint modules and end-effector vibration during the sand-resistant grate printing process. At the same time, they ignore the interference of ambient temperature on the mechanical performance of the equipment, making it impossible to comprehensively and accurately judge the actual operating status of the equipment, resulting in a lack of scientific data support for scheduling decisions. Second, the quantitative indicators of equipment operating status are incomplete. A coupled indicator that can comprehensively reflect the degree of heat accumulation and the maintenance of mechanical stiffness has not been constructed. Relying on a single parameter (such as temperature or vibration amplitude) to judge the equipment status cannot reflect the nonlinear coupling effect between heat accumulation and mechanical stiffness decay, which can easily lead to misjudgment of equipment operating stability and thus cause equipment overload operation. Third, task allocation lacks precise matching with the process requirements of the work units. Existing scheduling methods mostly adopt average allocation or allocation according to queue order, without considering the differences in geometric characteristics of different work units in the sand-resistant grate printing task (such as the process difficulty differences of intersection nodes, large curvature corners and straight line filling). This results in work units with higher process difficulty being assigned to equipment with poor condition, causing problems such as decreased printing accuracy and component forming defects. Summary of the Invention

[0004] The main objective of this invention is to provide a multi-device parallel sand-resistant grid printing scheduling optimization method. By collecting multi-source data such as real-time temperature of equipment joint modules, amplitude of end-effector vibration acceleration, real-time command feed speed, and ambient temperature, it covers the core parameters affecting the operational stability of the sand-resistant grid printing equipment, providing a complete and accurate data foundation for subsequent equipment status quantification and scheduling decisions. A scientific equipment thermo-mechanical coupling operational status quantification system is constructed. Based on the rheological properties of materials, the thermal accumulation saturation is calculated to accurately measure the degree to which the equipment temperature approaches the performance failure critical point. Simultaneously, the dynamic stiffness retention rate is calculated by combining the feed speed and end-effector vibration acceleration to characterize the mechanical performance status of the equipment. Finally, the thermo-mechanical coupling stability potential energy is synthesized through an exponential decay model, fully demonstrating the nonlinear coupling relationship between thermal accumulation and mechanical stiffness decay, thus achieving optimal equipment performance. The system achieves precise and comprehensive judgment of actual operating status; it realizes accurate matching between printing tasks and equipment status. By breaking down the sand-proof grid printing task into multiple work units, it quantifies the process potential energy requirements based on the geometric feature analysis results of the work units, and then allocates tasks according to the matching relationship between the equipment's thermo-mechanical coupling stability potential energy and the process potential energy requirements of the work units. This ensures that work units with higher process difficulty are assigned to equipment in good condition, effectively improving the printing accuracy of sand-proof grids and the forming quality of components. It also establishes an active control mechanism for abnormal equipment status. By setting a safe lower limit for the equipment's thermo-mechanical coupling stability potential energy, it generates no-load cooling or zero-return calibration commands in a timely manner when the equipment status approaches the critical value, until the equipment status returns to the safe range. This avoids equipment overload operation, ensures long-term stable operation of the equipment, extends the equipment's service life, and maintains the continuity of the overall production progress.

[0005] The technical solution of the present invention is as follows:

[0006] A method for optimizing the scheduling of parallel anti-sand grid printing across multiple devices is proposed, which includes the following steps:

[0007] S1. Real-time acquisition of operating status data and on-site environmental data from multiple parallel-running printing devices;

[0008] S2. Based on the operating status data and on-site environmental data, calculate the real-time thermal cumulative saturation and real-time dynamic stiffness retention rate of each printing device;

[0009] S3. Based on the real-time thermal accumulation saturation and real-time dynamic stiffness retention rate of each printing device, the real-time thermo-mechanical coupling stability potential energy of each printing device is synthesized using the exponential decay model formula.

[0010] S4. Analyze the geometric features of the sand-proof grid printing tasks to be assigned and quantify the process potential energy requirements of each work unit.

[0011] S5. Based on the matching relationship between the real-time thermo-mechanical coupling stability potential energy and the process potential energy requirement, perform multi-printing device task allocation and scheduling control.

[0012] A further improvement of the present invention is that the specific content of S1 is as follows: the real-time temperature of the i-th joint module of the k-th printing device is read by the thermistor built into the servo driver; the real-time vibration acceleration amplitude of the end is collected by the microelectromechanical system inertial measurement unit installed at the end of the print head; the current real-time command feed speed is read by the motion controller; and the real-time ambient temperature is obtained by the on-site weather station.

[0013] A further improvement of the present invention is that step S2 includes the following specific steps:

[0014] S21. Based on the rheological properties of materials, calculate the real-time cumulative thermal saturation of each printing device to measure how close the current temperature of the printing device is to the critical point of mechanical performance failure. The formula for calculating the real-time cumulative thermal saturation is as follows:

[0015] ;

[0016] in, This represents the real-time temperature of the i-th joint module of the k-th printing device. This indicates the preset critical temperature for rheological failure. Indicates the real-time ambient temperature. This represents the real-time cumulative thermal saturation of the k-th printing device, where t is the index of the time.

[0017] S22. Calculate the real-time dynamic stiffness retention rate of each printing device. The calculation formula is as follows:

[0018] ;

[0019] in, This represents the real-time command feed rate of the k-th printing device. This represents the real-time vibration acceleration amplitude at the end of the k-th printing device. This represents the real-time dynamic stiffness retention rate of the k-th printing device. These are dimensionless normalization coefficients calibrated based on the mechanical structure parameters of the equipment, used to map the ratio of velocity to acceleration to a standard range. To prevent division by zero of constants.

[0020] A further improvement of this invention is that the specific formula for the real-time thermo-mechanical coupling stability potential energy in S3 is as follows:

[0021] ;

[0022] in, This represents the real-time thermo-mechanical coupling stability potential energy of the k-th printing device. This is the preset heat sensitivity tolerance coefficient.

[0023] A further improvement of this invention is that the specific content of S4 is as follows: the sand-proof grid printing task is decomposed into a task queue containing M job units; for each job unit j to be assigned in the queue, its geometric path file is parsed and the process potential energy requirement value is calculated. The calculation formula is: ;in, The geometric complexity coefficient is set to 1 for operation units containing intersection nodes or large curvature corners, and to 0.5 for line-filling units. This is the process weighting coefficient. Based on the basic potential threshold, Let be the process potential energy requirement value for work unit j.

[0024] A further improvement of the present invention is that step S5 includes the following specific steps:

[0025] S51. For job unit j, select the printing devices that meet the following criteria from all parallel-running printing devices: A set of candidate printing devices; calculate the difference between the real-time thermo-mechanical coupling stability potential energy of each printing device in the candidate printing device set and the process potential energy requirement. ;

[0026] S52. Select the difference between the real-time thermo-mechanical coupling stability potential energy and the process potential energy requirement. The smallest printing device acts as the execution device, and the real-time thermo-mechanical coupling stability potential of printing device k... Below the preset safety lower limit At that time, an idle cooling command or a zero-return calibration command is generated until the real-time thermo-mechanical coupling stability potential energy of the printing device k rises back to the preset safety lower limit. above.

[0027] A further improvement of the present invention is that the critical temperature for rheological failure in S21 is the physical temperature point at which the viscosity of the lubricating grease inside the reducer drops to 80% of the rated value.

[0028] The technical effects of this invention are as follows:

[0029] A method for scheduling and optimizing multi-device parallel sand-resistant grid printing was developed. By collecting multi-source data, including real-time temperature of equipment joint modules, amplitude of end-effector vibration acceleration, real-time command feed speed, and ambient temperature, the method covers core parameters affecting the operational stability of the sand-resistant grid printing equipment, providing a complete and accurate data foundation for subsequent equipment status quantification and scheduling decisions. A scientific equipment thermo-mechanical coupling operational status quantification system was constructed. Based on material rheological properties, thermal accumulation saturation was calculated to accurately measure the degree to which the equipment temperature approaches the performance failure critical point. Simultaneously, the dynamic stiffness retention rate was calculated by combining feed speed and end-effector vibration acceleration to characterize the equipment's mechanical performance status. Finally, a thermo-mechanical coupling stability potential energy was synthesized through an exponential decay model, fully demonstrating the nonlinear coupling relationship between thermal accumulation and mechanical stiffness decay, thus realizing the actual operation of the equipment. The system achieves precise and comprehensive judgment of equipment status, enabling accurate matching between printing tasks and equipment status. By breaking down sand-resistant grate printing tasks into multiple work units, the system quantifies the process potential energy requirements based on the geometric feature analysis results of the work units. Then, tasks are allocated according to the matching relationship between the equipment's thermo-mechanical coupling stability potential energy and the process potential energy requirements of the work units. This ensures that work units with higher process difficulty are assigned to equipment in good condition, effectively improving the printing accuracy of sand-resistant grate and the forming quality of components. An active control mechanism for abnormal equipment status is established. By setting a safe lower limit for the equipment's thermo-mechanical coupling stability potential energy, the system promptly generates no-load cooling or zero-return calibration commands when the equipment status approaches the critical value, until the equipment status recovers to the safe range. This avoids equipment overload operation, ensures long-term stable operation of the equipment, extends the equipment's service life, and maintains the continuity of the overall production schedule. Attached Figure Description

[0030] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0031] Figure 1 This is a flowchart illustrating a multi-device parallel anti-sand grid printing scheduling optimization method according to Embodiment 1 of the present invention. Detailed Implementation

[0032] Example 1: This example proposes a multi-device parallel sand-resistant grid printing scheduling optimization method. By collecting multi-source data such as real-time temperature of equipment joint modules, amplitude of end-effector vibration acceleration, real-time command feed speed, and ambient temperature, it covers the core parameters affecting the operational stability of the sand-resistant grid printing equipment, providing a complete and accurate data foundation for subsequent equipment status quantification and scheduling decisions. A scientific equipment thermo-mechanical coupling operational status quantification system is constructed. Based on the rheological properties of materials, the thermal accumulation saturation is calculated to accurately measure the degree to which the equipment temperature approaches the performance failure critical point. At the same time, the dynamic stiffness retention rate is calculated by combining the feed speed and end-effector vibration acceleration to characterize the mechanical performance status of the equipment. Then, the thermo-mechanical coupling stability potential energy is synthesized through an exponential decay model, which fully reflects the nonlinear coupling relationship between thermal accumulation and mechanical stiffness decay, realizing the optimization of equipment status. The system achieves precise and comprehensive judgment of actual operating status; it realizes accurate matching between printing tasks and equipment status. By breaking down the sand-resistant grate printing task into multiple work units, it quantifies the process potential energy requirements based on the geometric feature analysis results of the work units, and then allocates tasks according to the matching relationship between the equipment's thermo-mechanical coupling stability potential energy and the process potential energy requirements of the work units. This ensures that work units with higher process difficulty are assigned to equipment in good condition, effectively improving the printing accuracy of sand-resistant grate and the quality of component forming. An active control mechanism for abnormal equipment status is established. By setting a safe lower limit for the equipment's thermo-mechanical coupling stability potential energy, it promptly generates no-load cooling or zero-return calibration commands when the equipment status approaches the critical value, until the equipment status recovers to the safe range. This avoids equipment overload operation, ensures long-term stable operation of the equipment, extends equipment life, and maintains the continuity of the overall production schedule. Specifically, such as... Figure 1 As shown, the multi-device parallel sand-proof grid printing scheduling optimization method proposed in this embodiment includes the following specific steps:

[0033] S1. Real-time acquisition of operating status data and on-site environmental data from multiple parallel-running printing devices.

[0034] In this embodiment, the specific content of S1 is as follows: reading the real-time temperature of the i-th joint module of the k-th printing device through the thermistor built into the servo driver; collecting the real-time vibration acceleration amplitude of the end through the microelectromechanical system inertial measurement unit installed at the end of the print head; reading the current real-time command feed speed through the motion controller; and obtaining the real-time ambient temperature through the on-site weather station.

[0035] S2. Based on the operating status data and on-site environmental data, calculate the real-time thermal cumulative saturation and real-time dynamic stiffness retention rate of each printing device.

[0036] In this embodiment, step S2 includes the following specific steps:

[0037] S21. Based on the rheological properties of materials, calculate the real-time cumulative thermal saturation of each printing device to measure how close the current temperature of the printing device is to the critical point of mechanical performance failure. The formula for calculating the real-time cumulative thermal saturation is as follows:

[0038] ;

[0039] in, This represents the real-time temperature of the i-th joint module of the k-th printing device. This indicates the preset rheological failure critical temperature, which is the physical temperature point at which the viscosity of the lubricating grease inside the reducer drops to 80% of its rated value. Indicates the real-time ambient temperature. The formula represents the real-time cumulative thermal saturation of the k-th printing device, where t is the index of the time. This formula eliminates the interference of local high temperature on the judgment of device status by taking the highest real-time temperature of all joint modules of a single printing device. At the same time, it introduces the real-time ambient temperature on site to correct the calculation results, making the calculation results more consistent with the actual operating status of the printing device.

[0040] S22. Calculate the real-time dynamic stiffness retention rate of each printing device. The calculation formula is as follows:

[0041] ;

[0042] in, This represents the real-time command feed rate of the k-th printing device. This represents the real-time vibration acceleration amplitude at the end of the k-th printing device. This represents the real-time dynamic stiffness retention rate of the k-th printing device. These are dimensional normalization coefficients calibrated based on the mechanical structure parameters of the equipment, with dimensions of... The coefficient was calibrated by conducting multiple bench tests on each joint module of the printing equipment with different feed speeds and vibration accelerations, establishing a mapping relationship between the speed and acceleration ratio, and mapping this ratio to a standard range of 0-1. To prevent division by zero, the constant is set to a value of This formula characterizes the mechanical stiffness of the printing equipment by the ratio of feed rate to end-effector acceleration, while using dimensional normalization coefficients and a zero constant to ensure the validity and rationality of the calculation results.

[0043] S3. Based on the real-time thermal cumulative saturation and real-time dynamic stiffness retention rate of each printing device, the real-time thermo-mechanical coupling stability potential energy of each printing device is synthesized using the exponential decay model formula.

[0044] In this embodiment, the specific formula for the real-time thermo-mechanical coupling stability potential energy in S3 is as follows:

[0045] ;

[0046] in, This represents the real-time thermo-mechanical coupling stability potential energy of the k-th printing device. The preset thermal tolerance coefficient is set to 0.3-0.5. This formula couples the real-time dynamic stiffness retention rate with the real-time thermal accumulation saturation through an exponential decay model. The change in thermal accumulation saturation has a non-linear decay effect on the overall stability potential energy of the equipment, which is more in line with the actual impact of thermal accumulation on mechanical stiffness.

[0047] S4. Analyze the geometric features of the sand-proof grid printing tasks to be assigned and quantify the process potential energy requirements of each work unit.

[0048] In this embodiment, the specific content of S4 is as follows: decompose the sand-proof grid printing task into a task queue containing M job units; for each job unit j to be assigned in the queue, parse its geometric path file and calculate the process potential energy requirement value. The calculation formula is: ;in, The geometric complexity coefficient is set to 1 for operation units containing intersection nodes or large curvature corners, and to 0.5 for line-filling units. This is the process weighting coefficient, a dimensionless quantity. Its value can be set according to the printing process requirements of the sand-proof grid, with a preferred value range of 0.8-1.2. The basic potential energy threshold can be set according to the basic operating performance of the printing equipment; Let be the process potential energy requirement value of work unit j; this formula quantifies the process difficulty of different work units through the geometric complexity coefficient, and then combines the process weight coefficient and the basic potential energy threshold to achieve accurate quantification of the process potential energy requirement of the work unit.

[0049] In this embodiment, a large curvature corner refers to a corner structure in the geometric path of the sand-proof grid printing unit where the radius of curvature at the corner is less than or equal to a preset corner curvature radius threshold. The corner curvature radius threshold is determined based on the molding accuracy requirements of the sand-proof grid and the motion control capability of the printing equipment. Specifically, the determination method is to extract the radius of curvature at the corner position in the geometric path of the working unit. When the radius of curvature is not greater than the preset corner curvature radius threshold, the corner is determined to be a large curvature corner. The preferred value of the corner curvature radius threshold is 5mm. This value is determined based on the common molding size of the sand-proof grid (side length 10-50cm) and the minimum turning radius of the end effector of the printing equipment. Corners with a radius of curvature less than the corner curvature radius threshold will increase the difficulty of adjusting the motion posture of the equipment during the printing process, thereby increasing the demand for the thermal-mechanical coupling stability potential energy of the equipment.

[0050] S5. Based on the matching relationship between the real-time thermo-mechanical coupling stability potential energy and the process potential energy requirement, perform multi-printing device task allocation and scheduling control.

[0051] In this embodiment, step S5 includes the following specific steps:

[0052] S51. For job unit j, select the printing devices that meet the following criteria from all parallel-running printing devices: A set of candidate printing devices; calculate the difference between the real-time thermo-mechanical coupling stability potential energy of each printing device in the candidate printing device set and the process potential energy requirement. ;

[0053] S52. Select the difference between the real-time thermo-mechanical coupling stability potential energy and the process potential energy requirement. The smallest printing device acts as the execution device, and the real-time thermo-mechanical coupling stability potential of printing device k... Below the preset safety lower limit At that time, an idle cooling command or a zero-return calibration command is generated until the real-time thermo-mechanical coupling stability potential energy of the printing device k rises back to the preset safety lower limit. After that, restore the task assignment and execution permissions for the printing device. Preset security lower limit. The value can be set according to the process requirements of anti-sand grid printing and the stable operating range of the equipment.

[0054] The threshold and weight settings can be based on the default settings of this invention, or they can be set by the operator.

[0055] Example 2: This example provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-described multi-device parallel anti-sand grid printing scheduling optimization method by calling the computer program stored in the memory.

[0056] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the multi-device parallel anti-sand grid printing scheduling optimization method provided in the above-described embodiment. The electronic device may also include other components for implementing device functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0057] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this disclosure can be embodied in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0058] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0059] This invention is described with reference to flowchart illustrations and block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and block diagrams, as well as combinations of blocks in the flowchart illustrations and block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0060] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and boxes Figure 1 The steps of the function specified in one or more boxes.

[0061] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for optimizing the scheduling of parallel sand-proof grid printing using multiple devices, characterized in that: The specific steps include the following: S1. Real-time acquisition of operating status data and on-site environmental data from multiple parallel-running printing devices; S2. Based on the operating status data and on-site environmental data, calculate the real-time thermal cumulative saturation and real-time dynamic stiffness retention rate of each printing device; S21. Based on the rheological properties of materials, calculate the real-time cumulative thermal saturation of each printing device to measure how close the current temperature of the printing device is to the critical point of mechanical performance failure. The formula for calculating the real-time cumulative thermal saturation is as follows: ； in, This represents the real-time temperature of the i-th joint module of the k-th printing device. This indicates the preset critical temperature for rheological failure. Indicates the real-time ambient temperature. This represents the real-time cumulative thermal saturation of the k-th printing device, where t is the index of the time. S22. Calculate the real-time dynamic stiffness retention rate of each printing device. The calculation formula is as follows: ； in, This represents the real-time command feed rate of the k-th printing device. This represents the real-time vibration acceleration amplitude at the end of the k-th printing device. This represents the real-time dynamic stiffness retention rate of the k-th printing device. These are dimensionless normalization coefficients calibrated based on the mechanical structure parameters of the equipment, used to map the ratio of velocity to acceleration to a standard range. To prevent division by zero of constants; S3. Based on the real-time thermal accumulation saturation and real-time dynamic stiffness retention rate of each printing device, the real-time thermo-mechanical coupling stability potential energy of each printing device is synthesized using the exponential decay model formula; the specific formula for the real-time thermo-mechanical coupling stability potential energy is as follows: ； in, This represents the real-time thermo-mechanical coupling stability potential energy of the k-th printing device. The preset heat resistance coefficient; S4. Analyze the geometric features of the sand-proof grid printing tasks to be assigned, and quantify the process potential energy requirement of each job unit. This includes: decomposing the sand-proof grid printing task into a task queue containing M job units; for each job unit j to be assigned in the queue, parsing its geometric path file and calculating the process potential energy requirement value. The calculation formula is: ;in, The geometric complexity coefficient is set to 1 for operation units containing intersection nodes or large curvature corners, and to 0.5 for line-filling units. This is the process weighting coefficient. Based on the basic potential threshold, Let J be the process potential energy requirement value for work unit j; S5. Based on the matching relationship between the real-time thermo-mechanical coupling stability potential energy and the process potential energy requirement, perform multi-printing device task allocation and scheduling control.

2. The method for optimizing the scheduling of multi-device parallel sand-proof grid printing according to claim 1, characterized in that: The specific content of S1 is as follows: read the real-time temperature of the i-th joint module of the k-th printing device through the thermistor built into the servo driver; collect the real-time vibration acceleration amplitude of the end through the microelectromechanical system inertial measurement unit installed at the end of the print head; read the current real-time command feed speed through the motion controller; and obtain the real-time ambient temperature through the on-site weather station.

3. The method for optimizing the scheduling of multi-device parallel sand-proof grid printing according to claim 2, characterized in that: S5 includes the following specific steps: S51. For job unit j, select the printing devices that meet the following criteria from all parallel-running printing devices: A set of candidate printing devices; Calculate the difference between the real-time thermo-mechanical coupling stability potential energy of each printing device in the candidate printing device set and the process potential energy requirement. ; S52. Select the difference between the real-time thermo-mechanical coupling stability potential energy and the process potential energy requirement. The smallest printing device acts as the execution device, and the real-time thermo-mechanical coupling stability potential of printing device k... Below the preset safety lower limit At that time, an idle cooling command or a zero-return calibration command is generated until the real-time thermo-mechanical coupling stability potential energy of the printing device k rises back to the preset safety lower limit. above.

4. The method for scheduling and optimizing multi-device parallel sand-proof grid printing according to claim 3, characterized in that: The critical temperature for rheological failure in S21 is the physical temperature point at which the viscosity of the lubricating grease inside the reducer drops to 80% of the rated value.

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