Method and system for optimizing processing energy consumption of intelligent machine tool

Abstract

The invention discloses an optimization method and system for processing energy consumption of an intelligent machine tool, and belongs to the technical field of processing equipment control, and the method comprises the steps: obtaining operation data of the current intelligent machine tool during operation and material data on a conveying line, building a state switching demand analysis model, and generating a state switching demand index; according to the state switching demand index, whether the intelligent machine tool needs to operate at the lowest power or not is judged, and the operation state of the intelligent machine tool is adjusted to operate at the lowest power through the control end; according to the material fault end position, the material arrival prediction time is generated, a machine tool starting opportunity analysis model is established, the intelligent machine tool starting time is generated, and the intelligent machine tool is started through the control end; according to the method, precise shutdown decision and start-stop opportunity optimization can be realized, invalid idle energy consumption and frequent start-stop loss can be reduced, energy consumption saving and equipment service life maintenance are balanced, and the energy utilization efficiency is effectively improved on the premise that the machining efficiency is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of processing equipment control, and particularly relates to an optimization method and system for the processing energy consumption of intelligent machine tools. Background Art

[0002] With the rapid development of intelligent manufacturing technology, intelligent machine tools have been widely used in modern manufacturing. Intelligent machine tools not only have highly automated processing capabilities but also can complete complex processing tasks on the basis of high precision and high efficiency.

[0003] In the processing of traditional intelligent machine tools, the management of energy consumption often relies on the continuous operation of mechanical equipment without effectively judging whether the machine tool is in the actual processing state. In this way, when the machine tool is in an idle state, a large amount of energy will still be consumed, resulting in energy waste and an increase in operating costs.

[0004] In the existing technology, although there are some studies on energy conservation and emission reduction, most of them focus on the energy-saving optimization of the overall working mode of the machine tool, lacking specific methods for real-time monitoring and analysis of the operating state of the machine tool. Moreover, the existing energy-saving technologies mainly adjust the working state of the machine tool through manual intervention, lacking automated and intelligent processing methods, which not only increases the manual operation burden in the production process but also makes it difficult to make precise adjustments in a short time, resulting in energy waste and a decrease in production efficiency. Summary of the Invention

[0005] In view of the deficiencies of the existing technology, the present invention provides an optimization method and system for the processing energy consumption of intelligent machine tools, which solves the above problems.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An optimization method for the processing energy consumption of an intelligent machine tool includes the following steps: Obtain the operating data of the current intelligent machine tool during operation and the material data on the conveyor line; wherein, the operating data includes the energy consumption of running without material and the energy consumption of state switching; the material data includes the material conveying speed, the length of the material fault, and the position of the material fault; the position of the material fault includes the start position and the end position of the fault; Establish a state switching demand analysis model based on the operating data of the current intelligent machine tool during operation and the material data on the conveyor line, and generate a state switching demand index; Judge whether the intelligent machine tool needs to operate at the minimum power according to the state switching demand index; If the intelligent machine tool needs to operate at the minimum power, adjust the operating state of the intelligent machine tool to the minimum power operation through the control terminal; When the intelligent machine tool enters the minimum power operation, generate a predicted material arrival time according to the end position of the material fault; Based on the predicted arrival time of materials, an analysis model for the starting time of the machine tool is established to generate the intelligent starting time of the machine tool. Start the intelligent machine tool through the machine tool control terminal according to the intelligent starting time of the machine tool.

[0007] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions: Further technical solution: The specific generation method of the state switching demand index is as follows: Generate the material fault energy consumption according to the no-load running energy consumption of the intelligent machine tool and the predicted value of the material fault arrival; wherein, the material fault energy consumption refers to the product value between the no-load running energy consumption of the intelligent machine tool and the material fault time; the material fault time refers to the ratio between the material fault length and the material conveying speed; Generate the material fault influence index according to the material fault length; wherein, the material fault influence index refers to the ratio between the material fault length and the fault length threshold; Establish a state switching demand analysis model, and substitute the material fault influence index, the material fault energy consumption, the state switching energy consumption and the start-stop frequency factor into the state switching demand analysis model to generate the state switching demand index.

[0008] Further technical solution: The expression of the state switching demand analysis model is: ; In the expression, Q represents the state switching demand index, represents the material fault energy consumption, represents the state switching energy consumption, represents the material fault influence index, represents the start-stop frequency factor; the state switching energy consumption refers to the additional energy consumption generated when the intelligent machine tool switches from the lowest power operation state to the standard working state.

[0009] Further technical solution: The specific acquisition method of the start-stop frequency factor is as follows: Obtain the start-stop times of the current operation cycle of the intelligent machine tool; the operation cycle refers to a time period of the same duration; the current operation cycle refers to the operation cycle that has not been completed in terms of time sequence; Generate the start-stop frequency according to the start-stop times of the current operation cycle of the intelligent machine tool and the duration of the operation cycle; wherein, the start-stop frequency refers to the ratio between the start-stop times of the current operation cycle of the intelligent machine tool and the duration of the operation cycle; Generate the start-stop factor according to the start-stop frequency; wherein, the start-stop factor refers to the ratio between the start-stop frequency and the start-stop frequency threshold.

[0010] Further technical solution: The specific method for controlling the intelligent machine tool to operate at the lowest power is as follows: Generate a predicted value for the arrival of the material fault based on the position of the material fault and the material conveying speed; wherein, the position of the material fault refers to the end closest to the intelligent machine tool among the distances between the material fault and the intelligent machine tool. Control the intelligent machine tool to operate at the minimum power through the control end according to the predicted value of the arrival of the material fault.

[0011] Further technical solution: The specific method for generating the predicted value of the arrival of the material fault is as follows: Establish a horizontal number axis according to the conveying trajectory and direction of the material conveying line, and obtain the numerical value of the material fault position and the numerical value of the intelligent machine tool position in the horizontal number axis. Through the formula: ; Generate the predicted value of the arrival of the material fault ; In the formula, represents the starting position of the fault, M represents the numerical value of the intelligent machine tool position, and V represents the material conveying speed.

[0012] Further technical solution: The specific method for generating the predicted time of material arrival is as follows: Through the formula: ; Generate the predicted time of material arrival ; In the formula, represents the ending position of the fault, M represents the numerical value of the intelligent machine tool position, and V represents the material conveying speed.

[0013] Further technical solution: The expression of the machine tool start timing analysis model is: ; In the expression, represents the start time of the intelligent machine tool, represents the predicted time of material arrival, represents the time correction term; the value of the time correction term refers to the maximum value of the time required for the intelligent machine tool to switch from the minimum power operation to the standard working state.

[0014] Further technical solution: The specific method for obtaining the time correction term is as follows: Obtain the temperature value of the current intelligent machine tool, and generate a temperature difference according to the temperature value of the current intelligent machine tool and the standard working temperature value of the intelligent machine tool; wherein, the temperature difference refers to the difference between the temperature value of the current intelligent machine tool and the standard working temperature value of the intelligent machine tool. Generate the time required for temperature rise according to the temperature difference and the heating rate; wherein, the time required for temperature rise refers to the ratio between the temperature difference and the heating rate; Obtain the time required for the intelligent machine tool to stop and start, and compare the time required for temperature rise with the time required for the intelligent machine tool to stop and start; If the time required for temperature rise is less than the time required for the intelligent machine tool to stop and start, the time correction term is the time required for the intelligent machine tool to stop and start; If the time required for temperature rise is greater than the time required for the intelligent machine tool to stop and start, the time correction term is the time required for temperature rise.

[0015] An optimization system for the processing energy consumption of an intelligent machine tool, the system includes: A data acquisition module, used to acquire the operation data of the current intelligent machine tool during operation and the material data on the conveyor line; wherein, the operation data includes the energy consumption of running with empty material and the energy consumption of state switching; the material data includes the material conveying speed, the length of the material fault, and the position of the material fault; the position of the material fault includes the start position and the end position of the fault; A state switching demand analysis unit, used to establish a state switching demand analysis model based on the operation data of the current intelligent machine tool during operation and the material data on the conveyor line, and generate a state switching demand index; A demand judgment module, used to judge whether the intelligent machine tool needs to operate at the minimum power according to the state switching demand index; A switching control module, if the intelligent machine tool needs to operate at the minimum power, the switching control module is used to adjust the operation state of the intelligent machine tool to the minimum power operation through the control terminal; A material analysis module, when the intelligent machine tool enters the minimum power operation, the material analysis module is used to generate the predicted time for the material to arrive according to the end position of the material fault; A starting opportunity analysis module, used to establish a machine tool starting opportunity analysis model according to the predicted time for the material to arrive, and generate the starting time of the intelligent machine tool; A starting control module, used to start the intelligent machine tool through the machine tool control terminal according to the starting time of the intelligent machine tool.

[0016] The present invention provides an optimization method and system for the processing energy consumption of an intelligent machine tool, and has the following beneficial effects compared with the prior art: The present invention realizes precise shutdown decision-making and optimization of starting and stopping opportunities by constructing a state switching demand analysis model and a starting opportunity analysis model, dynamically integrating multi-dimensional parameters such as the energy consumption of running with empty material, the energy consumption of state switching, the material fault influence index, and the starting and stopping frequency factor. It can also predict the material arrival time in real time according to the material conveying speed and the fault position, and combine with the time correction term to reduce the ineffective idling energy consumption and frequent starting and stopping losses, balance the energy consumption saving and the equipment life maintenance, and effectively improve the energy utilization efficiency on the premise of ensuring the processing efficiency. Brief Description of the Drawings

[0017] Figure 1 It is a flowchart of an optimization method for the processing energy consumption of an intelligent machine tool provided by an embodiment of the present invention.

[0018] Figure 2 It is a flowchart of step S20 provided by an embodiment of the present invention.

[0019] Figure 3 It is a schematic structural diagram of an optimization system for the processing energy consumption of an intelligent machine tool provided by an embodiment of the present invention.

[0020] Figure 4 It is a module block diagram of a shutdown requirement analysis unit provided by an embodiment of the present invention. Detailed Description of the Invention

[0021] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The following describes in detail the specific implementation of the present invention with reference to specific embodiments.

[0023] Please refer to Figure 1 , an optimization method for the processing energy consumption of an intelligent machine tool provided by an embodiment of the present invention, includes the following steps: Step S10: Obtain the operation data of the current intelligent machine tool during operation and the material data on the conveyor line; wherein, the operation data includes the energy consumption of running without material and the energy consumption of state switching; the material data includes the material conveying speed, the length of the material fault, and the position of the material fault; the position of the material fault includes the start position and the end position of the fault; Step S20: According to the operation data of the current intelligent machine tool during operation and the material data on the conveyor line, establish a state switching requirement analysis model and generate a state switching requirement index; Step S30: According to the state switching requirement index, determine whether the intelligent machine tool needs to operate at the lowest power; Step S40: If the intelligent machine tool needs to operate at the lowest power, adjust the operation state of the intelligent machine tool to the lowest power operation through the control end; Step S50: When the intelligent machine tool enters the lowest power operation, generate a predicted material arrival time according to the end position of the material fault; Step S60: According to the predicted material arrival time, establish a machine tool startup timing analysis model and generate the startup time of the intelligent machine tool; Step S70: Start the intelligent machine tool through the machine tool control end according to the startup time of the intelligent machine tool.

[0024] As a preferred embodiment of the present invention, step S10 specifically includes: When obtaining the material data on the conveyor line, the monitoring points can be set on the conveyor line of the material, and the conveying speed and the position of the material can be obtained at the monitoring points; In addition, the setting method of the monitoring points includes but is not limited to setting multiple monitoring points on the conveyor line and setting panoramic monitoring points on one side of the conveyor line (the panorama here refers to the monitoring range of the monitoring points); In this embodiment, the material fault refers to the material-free area that appears between adjacent materials on the conveyor line due to the interval exceeding the threshold; The method for judging whether it is a material fault is specifically as follows: Monitor the interval between the materials on the transmission line to generate a material interval value; the material interval value refers to the distance value between adjacent materials; Compare the material interval value with the interval threshold; The interval threshold is a set value, which is set by relevant personnel in the field; its value-taking method can be set according to the preset interval during material transmission; for example, if the preset interval during material transmission is n, the interval threshold can be n + x, where x is an error value and x is not less than 0; If the material interval value is less than or equal to the interval threshold, it is determined that there is no material fault on the conveyor line; If the material interval value is greater than the interval threshold, it is determined that there is a material fault on the conveyor line, that is, the gap generated between the adjacent materials is the material fault (the gap length is the material fault length).

[0025] Please refer to Figure 2 , as a preferred embodiment of the present invention, step S20 specifically includes the following steps: S21: Generate the material fault energy consumption according to the no-load running energy consumption of the intelligent machine tool and the predicted value of the material fault arrival; wherein, the material fault energy consumption refers to the product value of the no-load running energy consumption of the intelligent machine tool and the material fault time; the material fault time refers to the ratio of the material fault length to the material conveying speed; It should be explained that the material fault energy consumption refers to the energy consumption when the intelligent machine tool runs idly (without material processing) during the material fault; S22: Generate the material fault influence index according to the material fault length; wherein, the material fault influence index refers to the ratio of the material fault length to the fault length threshold; It should be explained that the fault length threshold refers to the maximum value of the material fault length under standard working conditions; the standard working conditions refer to the normal transmission of materials; S23: Establish a state transition requirement analysis model, substitute the material fault impact index, material fault energy consumption, state transition energy consumption, and start-stop frequency factor into the state transition requirement analysis model to generate a state transition requirement index.

[0026] As a preferred embodiment of the present invention, the expression of the state transition requirement analysis model is: ; In the expression, Q represents the state transition requirement index, represents the material fault energy consumption, represents the state transition energy consumption, represents the material fault impact index, represents the start-stop frequency factor; It should be explained that the state transition energy consumption refers to the additional energy consumption generated when the intelligent machine tool switches from the lowest power operation state to the standard working state.

[0027] As a preferred embodiment of the present invention, the specific method for obtaining the start-stop frequency factor is as follows: Obtain the number of start-stops in the current operation cycle of the intelligent machine tool; It should be explained that the operation cycle refers to a time period of the same length; the current operation cycle refers to the operation cycle that has not been completed in time sequence; for example, taking one hour as the length of the operation cycle (corresponding to the timetable, the start and end points of each hour are the two endpoints of the operation cycle in time sequence), within the current operation cycle, the intelligent machine tool has started and stopped a total of c times (that is, one cycle from starting to the lowest power operation and then starting again is counted as one time), that is, the number of start-stops in the current operation cycle of the intelligent machine tool is c; Generate a start-stop frequency based on the number of start-stops in the current operation cycle of the intelligent machine tool and the duration of the operation cycle; among them, the start-stop frequency refers to the ratio between the number of start-stops in the current operation cycle of the intelligent machine tool and the duration of the operation cycle; Generate a start-stop factor based on the start-stop frequency; among them, the start-stop factor refers to the ratio between the start-stop frequency and the start-stop frequency threshold; It should be explained that the start-stop frequency threshold is a set value, which is set by relevant personnel in the field; In addition, frequent start-stops of the intelligent machine tool will cause more starting impacts on components such as the motor, transmission system, and bearings of the machine tool; each time it starts, the motor needs to overcome the static inertia, and this load change will increase mechanical wear, thereby shortening the service life of the equipment and also increasing the maintenance cost of the intelligent machine tool.

[0028] As a preferred embodiment of the present invention, the specific method for determining whether the intelligent machine tool needs to operate at the lowest power is as follows: Compare the status switching demand index with the status switching demand index threshold value; The value of the status switching demand index threshold is set by relevant personnel in the field; If the status switching demand index is less than or equal to the status switching demand index threshold value, it is determined that the intelligent machine tool does not need to operate at the minimum power; at this time, the smaller the status switching demand index, the smaller the demand for the intelligent machine tool to operate at the minimum power; If the status switching demand index is greater than the status switching demand index threshold value, it is determined that the intelligent machine tool needs to operate at the minimum power; at this time, the greater the status switching demand index, the greater the demand for the intelligent machine tool to operate at the minimum power.

[0029] As a preferred embodiment of the present invention, the method for controlling the intelligent machine tool to operate at the minimum power is specifically as follows: Generate a predicted value for the arrival of the material fault according to the position of the material fault and the material conveying speed; wherein, the position of the material fault refers to the end closest to the intelligent machine tool among the distances between the material fault and the intelligent machine tool; It should be explained that the distance between the material fault and the intelligent machine tool refers to the path length formed when the material moves on the conveyor line and finally reaches the intelligent machine tool; In this embodiment, the material fault can also be understood as a line segment that moves along a fixed trajectory. Among the two endpoints of the line segment, the endpoint that reaches the position of the intelligent machine tool first is the end closest to the intelligent machine tool among the distances between the material fault and the intelligent machine tool; Control the intelligent machine tool to operate at the minimum power through the control end according to the predicted value of the arrival of the material fault; In this embodiment, the minimum power operation of the intelligent machine tool includes stop operation and status maintenance operation; wherein, the operating power of the stop operation is 0, then the status switching energy consumption in the status switching demand analysis model represents the additional energy consumption when the intelligent machine tool switches from the stop operation state to the standard working state; In practical applications, when the intelligent machine tool cannot operate at the minimum power due to processing requirements or other reasons, the minimum demand state of the intelligent machine tool can be maintained (i.e., status maintenance operation) to minimize the energy consumption when the intelligent processing machine tool waits for materials; if the minimum power operation is status maintenance operation, then the status switching energy consumption in the status switching demand analysis model represents the additional energy consumption when the intelligent machine tool switches from the status maintenance power to the standard working state.

[0030] As a preferred embodiment of the present invention, the method for generating the predicted value of the arrival of the material fault is specifically as follows: Establish a horizontal number axis according to the conveying trajectory and direction of the material conveying line, and obtain the numerical values of the material fault positions and the intelligent machine tool positions in the horizontal number axis; Through the formula: ; Generate the predicted value of the arrival of the material fault ; In the formula, represents the starting position of the fault, M represents the numerical value of the intelligent machine tool position, and V represents the material conveying speed.

[0031] As a preferred embodiment of the present invention, the generation method of the predicted arrival time of the material is specifically: Through the formula: ; Generate the predicted arrival time of the material ; In the formula, represents the ending position of the fault, M represents the numerical value of the intelligent machine tool position, and V represents the material conveying speed.

[0032] As a preferred embodiment of the present invention, the expression of the machine tool startup timing analysis model is: ; In the expression, represents the startup time of the intelligent machine tool, represents the predicted arrival time of the material, represents the time correction term; the value of the time correction term refers to the maximum value of the time required for the intelligent machine tool to switch from the lowest power operation to the standard working state; It should be explained that the time correction term is a constant.

[0033] As a preferred embodiment of the present invention, the acquisition method of the time correction term is specifically: Obtain the temperature value of the current intelligent machine tool, and generate a temperature difference according to the temperature value of the current intelligent machine tool and the standard working temperature value of the intelligent machine tool; wherein, the temperature difference refers to the difference between the temperature value of the current intelligent machine tool and the standard working temperature value of the intelligent machine tool; Generate the time required for heating up according to the temperature difference and the heating rate; wherein, the time required for heating up refers to the ratio of the temperature difference to the heating rate; It should be explained that the heating rate refers to the heating rate of the intelligent machine tool, and the way of obtaining its value includes but is not limited to linear regression equations, etc.; In addition, in this embodiment, the temperature value of the current intelligent machine tool is lower than the standard operating temperature value of the intelligent machine tool; if the temperature value of the current intelligent machine tool is higher than the standard operating temperature value of the intelligent machine tool, the value of the time required for heating is 0; Obtain the time required for starting and stopping the intelligent machine tool, and compare the time required for heating with the time required for starting and stopping the intelligent machine tool; If the time required for heating is less than the time required for starting and stopping the intelligent machine tool, the time correction term is the time required for starting and stopping the intelligent machine tool; If the time required for heating is greater than the time required for starting and stopping the intelligent machine tool, the time correction term is the time required for heating; It should be noted that if the time required for heating is equal to the time required for starting and stopping the intelligent machine tool, either of them can be selected as the time correction term; In this embodiment, the time correction term may also include the difference in conveying time caused by the fluctuation of the material conveying speed and the time required from the time required for starting and stopping the intelligent machine tool (if operating at the lowest power, it is the low-power state) to the normal operating duration, etc.

[0034] Please refer to Figure 3 , the present invention also provides an optimization system for the processing energy consumption of an intelligent machine tool, and the system includes: A data acquisition module 10, configured to acquire the operating data during the operation of the current intelligent machine tool and the material data on the conveyor line; wherein, the operating data includes the energy consumption during empty material operation and the energy consumption during state switching; the material data includes the material conveying speed, the length of the material fault, and the position of the material fault; the position of the material fault includes the start position and the end position of the fault; A state switching requirement analysis unit 20, configured to establish a state switching requirement analysis model and generate a state switching requirement index according to the operating data during the operation of the current intelligent machine tool and the material data on the conveyor line; A requirement judgment module 30, configured to judge whether the intelligent machine tool needs to operate at the lowest power according to the state switching requirement index; A switching control module 40, if the intelligent machine tool needs to operate at the lowest power, the switching control module is configured to adjust the operating state of the intelligent machine tool to the lowest power operation through the control end; A material analysis module 50, when the intelligent machine tool enters the lowest power operation, the material analysis module is configured to generate a predicted material arrival time according to the end position of the material fault; A starting time analysis module 60, configured to establish a machine tool starting time analysis model and generate the starting time of the intelligent machine tool according to the predicted material arrival time; A starting control module 70, configured to start the intelligent machine tool through the machine tool control end according to the starting time of the intelligent machine tool.

[0035] Please refer to Figure 4, as a preferred embodiment of the present invention, the shutdown requirement analysis unit specifically includes: A fault energy consumption analysis module 21, configured to generate a material fault energy consumption according to the no-load operation energy consumption of the intelligent machine tool and the predicted value of the arrival of the material fault; wherein, the material fault energy consumption refers to the product value between the no-load operation energy consumption of the intelligent machine tool and the material fault time; the material fault time refers to the ratio between the material fault length and the material conveying speed; A fault influence analysis model 22, configured to generate a material fault influence index according to the material fault length; wherein, the material fault influence index refers to the ratio between the material fault length and the fault length threshold; A requirement analysis module 23, configured to establish a state transition requirement analysis model, and substitute the material fault influence index, the material fault energy consumption, the state transition energy consumption, and the start-stop frequency factor into the state transition requirement analysis model to generate a state transition requirement index.

[0036] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An optimization method for the machining energy consumption of an intelligent machine tool, characterized in that, Including the following steps: Obtain the operation data during the current operation of the intelligent machine tool and the material data on the conveyor line; among them, the operation data includes the energy consumption during empty material operation and the energy consumption during state switching; the material data includes the material conveying speed, the length of the material fault, and the position of the material fault; the position of the material fault includes the start position and the end position of the fault; Based on the operation data during the current operation of the intelligent machine tool and the material data on the conveyor line, establish a state switching demand analysis model and generate a state switching demand index; Based on the state switching demand index, determine whether the intelligent machine tool needs to operate at the minimum power; If the intelligent machine tool needs to operate at the minimum power, adjust the operation state of the intelligent machine tool to the minimum power operation through the control terminal; When the intelligent machine tool enters the minimum power operation, generate the predicted material arrival time according to the end position of the material fault; Based on the predicted material arrival time, establish a machine tool startup timing analysis model and generate the startup time of the intelligent machine tool; Based on the startup time of the intelligent machine tool, start the intelligent machine tool through the machine tool control terminal.

2. The optimization method for the machining energy consumption of an intelligent machine tool according to claim 1, wherein The specific generation method of the state switching demand index is as follows: Generate the material fault energy consumption according to the energy consumption during empty material operation of the intelligent machine tool and the predicted value of the material fault arrival; among them, the material fault energy consumption refers to the product value between the energy consumption during empty material operation of the intelligent machine tool and the material fault time; the material fault time refers to the ratio between the length of the material fault and the material conveying speed; Generate the material fault influence index according to the length of the material fault; among them, the material fault influence index refers to the ratio between the length of the material fault and the fault length threshold; Establish a state switching demand analysis model, substitute the material fault influence index, the material fault energy consumption, the state switching energy consumption, and the start-stop frequency factor into the state switching demand analysis model, and generate a state switching demand index.

3. The optimization method for the machining energy consumption of an intelligent machine tool according to claim 2, wherein, The expression of the state switching demand analysis model is: ； In the expression, Q represents the state switching requirement index, represents the energy consumption of the material fault, represents the state switching energy consumption, represents the material fault impact index, represents the start-stop frequency factor; the state switching energy consumption refers to the additional energy consumption generated when the intelligent machine tool switches from the lowest power operation state to the standard working state.

4. The optimization method for the machining energy consumption of an intelligent machine tool according to claim 3, characterized in that, The specific acquisition method of the start-stop frequency factor is as follows: Obtain the number of start-stops in the current operation cycle of the intelligent machine tool; the operation cycle refers to a time period of the same duration; the current operation cycle refers to the operation cycle that has not been completed in terms of time sequence; Generate the start-stop frequency according to the number of start-stops in the current operation cycle of the intelligent machine tool and the duration of the operation cycle; among them, the start-stop frequency refers to the ratio between the number of start-stops in the current operation cycle of the intelligent machine tool and the duration of the operation cycle; Generate the start-stop factor according to the start-stop frequency; among them, the start-stop factor refers to the ratio between the start-stop frequency and the start-stop frequency threshold.

5. The optimization method for the machining energy consumption of an intelligent machine tool according to claim 1, characterized in that The specific method of controlling the intelligent machine tool to operate at the minimum power is as follows: Generate the predicted value of the material fault arrival according to the position of the material fault and the material conveying speed; among them, the position of the material fault refers to the end closest to the intelligent machine tool among the material faults; Based on the predicted value of the material fault arrival, control the intelligent machine tool to operate at the minimum power through the control terminal.

6. The optimization method for the machining energy consumption of an intelligent machine tool according to claim 5, wherein, The specific generation method of the predicted value of the material fault arrival is as follows: Based on the conveying trajectory and the conveying direction of the material conveyor line, establish a horizontal number axis, and obtain the numerical value of the material fault position and the numerical value of the intelligent machine tool position in the horizontal number axis; Through the formula: ； Generate the predicted value of the material fault arrival ; In the formula, represents the starting position of the fault, M represents the position value of the intelligent machine tool, and V represents the material conveying speed.

7. The optimization method for the machining energy consumption of an intelligent machine tool according to claim 1, characterized in that, The specific generation method of the predicted material arrival time is as follows: Through the formula: ； Generate the predicted arrival time of materials ; In the formula, represents the end position of the fault, M represents the position value of the intelligent machine tool, and V represents the material conveying speed.

8. The optimization method for the machining energy consumption of an intelligent machine tool according to claim 7, wherein The expression of the machine tool startup timing analysis model is: ； In the expression, represents the startup time of the intelligent machine tool, represents the predicted arrival time of the material, represents the time correction term; the value of the time correction term refers to the maximum duration required for the intelligent machine tool to switch from running at the lowest power to the standard working state.

9. The optimization method for the machining energy consumption of an intelligent machine tool according to claim 8, wherein, The specific method for obtaining the time correction term is as follows: Obtain the temperature value of the current intelligent machine tool, and generate a temperature difference based on the temperature value of the current intelligent machine tool and the standard working temperature value of the intelligent machine tool; wherein, the temperature difference refers to the difference between the temperature value of the current intelligent machine tool and the standard working temperature value of the intelligent machine tool; Generate the time required for temperature rise based on the temperature difference and the temperature rise rate; wherein, the time required for temperature rise refers to the ratio of the temperature difference to the temperature rise rate; Obtain the time required for the start and stop of the intelligent machine tool, and compare the time required for temperature rise with the time required for the start and stop of the intelligent machine tool; If the time required for temperature rise is less than the time required for the start and stop of the intelligent machine tool, the time correction term is the time required for the start and stop of the intelligent machine tool; If the time required for temperature rise is greater than the time required for the start and stop of the intelligent machine tool, the time correction term is the time required for temperature rise.

10. An optimization system for the machining energy consumption of an intelligent machine tool, characterized in that, For implementing an optimization method for the processing energy consumption of an intelligent machine tool according to any one of claims 1-9, the system includes: A data acquisition module, configured to acquire the operation data of the current intelligent machine tool during operation and the material data on the conveyor line; wherein, the operation data includes the energy consumption during empty material operation and the energy consumption during state switching; the material data includes the material conveying speed, the material fault length, and the material fault position; the material fault position includes the fault start position and the fault end position; A state switching requirement analysis unit, configured to establish a state switching requirement analysis model based on the operation data of the current intelligent machine tool during operation and the material data on the conveyor line, and generate a state switching requirement index; A requirement judgment module, configured to judge whether the intelligent machine tool needs to operate at the minimum power according to the state switching requirement index; A switching control module, if the intelligent machine tool needs to operate at the minimum power, the switching control module is configured to adjust the operation state of the intelligent machine tool to the minimum power operation through the control end; A material analysis module, when the intelligent machine tool enters the minimum power operation, the material analysis module is configured to generate a predicted material arrival time according to the material fault end position; A start timing analysis module, configured to establish a machine tool start timing analysis model according to the predicted material arrival time, and generate the start time of the intelligent machine tool; A start control module, configured to start the intelligent machine tool through the machine tool control end according to the start time of the intelligent machine tool.

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