A method and system for optimizing energy consumption of intelligent machine tools

Abstract

The present invention discloses a method and system for optimizing the processing energy consumption of intelligent machine tools, which belongs to the field of processing equipment control technology. The method comprises obtaining the operating data of the current intelligent machine tool and the material data on the conveyor line, establishing a state switching demand analysis model, and generating a state switching demand index; judging whether the intelligent machine tool needs to operate at the lowest power according to the state switching demand index, and adjusting the operating state of the intelligent machine tool to the lowest power operation through the control end; generating a material arrival prediction time according to the end position of the material fault, and then establishing a machine tool start-up timing analysis model, generating the intelligent machine tool start-up time, and starting the intelligent machine tool through the control end; the present invention can realize accurate shutdown decision-making and start-stop timing optimization, and can also reduce invalid idling energy consumption and frequent start-stop loss, balance energy saving and equipment life maintenance, and effectively improve energy utilization efficiency while ensuring processing efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of processing equipment control, and in particular relates to a method and system for optimizing 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 can also complete complex processing tasks with high precision and high efficiency.

[0003] Traditional intelligent machine tools often rely on continuous operation of the machine during processing to manage energy consumption, without effectively determining whether the machine is actually processing. Consequently, even when the machine is idling, it still consumes significant amounts of energy, leading to energy waste and increased operating costs.

[0004] While existing research on energy conservation and emission reduction has been conducted, most efforts focus on optimizing the overall operating mode of machine tools, lacking specific methods for real-time monitoring and analysis of their operating status. Furthermore, existing energy-saving technologies primarily rely on manual intervention to adjust machine tool operating conditions, lacking automated or intelligent processing methods. This not only increases the manual operation burden during production but also makes it difficult to make precise adjustments in a short period of time, resulting in wasted energy and reduced production efficiency. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and system for optimizing the processing energy consumption of intelligent machine tools, which solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for optimizing the processing energy consumption of an intelligent machine tool, comprising the following steps:

[0007] Obtain the current operating data of the intelligent machine tool and the material data on the conveyor line; the operating data includes the energy consumption of empty material operation and state switching energy consumption; the material data includes the material conveying speed, material fault length and material fault position; the material fault position includes the fault start position and the fault end position;

[0008] Based on the current operating data of the intelligent machine tool and the material data on the conveyor line, a state switching demand analysis model is established to generate a state switching demand index;

[0009] According to the state switching demand index, determine whether the intelligent machine tool needs to operate at the lowest power;

[0010] If the intelligent machine tool needs to operate at the lowest power, the operating state of the intelligent machine tool is adjusted to the lowest power through the control terminal;

[0011] When the intelligent machine tool enters the lowest power operation mode, the material arrival prediction time is generated based on the end position of the material fault;

[0012] Based on the predicted material arrival time, a machine tool startup timing analysis model is established to generate the intelligent machine tool startup time;

[0013] According to the intelligent machine tool startup time, start the intelligent machine tool through the machine tool control terminal.

[0014] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0015] Further technical solution: The state switching demand index is generated in the following manner:

[0016] The material gap energy consumption is generated based on the energy consumption of the IMT during empty-material operation and the predicted material gap arrival value. The material gap energy consumption is the product of the energy consumption of the IMT during empty-material operation and the material gap time. The material gap time is the ratio of the material gap length to the material conveying speed.

[0017] Generate a material fault impact index based on the material fault length; the material fault impact index refers to the ratio between the material fault length and the fault length threshold;

[0018] A state switching demand analysis model is established, and the material fault impact index, material fault energy consumption, state switching energy consumption and start-stop frequency factor are substituted into the state switching demand analysis model to generate the state switching demand index.

[0019] Further technical solution: The expression of the state switching demand analysis model is:

[0020] ;

[0021] In the expression, Q represents the state switching demand index, It represents the energy consumption of material fault. It represents the energy consumption of state switching. It represents the material fault impact index. It 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 operating state to the standard working state.

[0022] Further technical solution: The start-stop frequency factor is obtained in the following manner:

[0023] Get the number of starts and stops in the current operating cycle of the intelligent machine tool; an operating cycle refers to a time period of equal length; the current operating cycle refers to the operating cycle that has not yet been completed in terms of time sequence;

[0024] Generate a start-stop frequency based on the number of starts and stops in the current operating cycle of the intelligent machine tool and the duration of the operating cycle. The start-stop frequency refers to the ratio between the number of starts and stops in the current operating cycle of the intelligent machine tool and the duration of the operating cycle.

[0025] A start-stop factor is generated 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.

[0026] Further technical solution: The method of controlling the intelligent machine tool to operate at the lowest power is specifically as follows:

[0027] Generate a material fault arrival prediction value based on the material fault location and material conveying speed. The material fault location refers to the end of the material fault closest to the intelligent machine tool.

[0028] According to the predicted value of material fault arrival, the intelligent machine tool is controlled by the control end to operate at the lowest power.

[0029] Further technical solution: The method for generating the material fault arrival prediction value is specifically as follows:

[0030] According to the conveying trajectory and conveying direction of the material conveying line, a horizontal axis is established to obtain the material fault position value and the intelligent machine tool position value in the horizontal axis;

[0031] By formula:

[0032] ;

[0033] Generate material fault arrival prediction value ;

[0034] In the formula, It indicates the starting position of the fault, M indicates the position value of the intelligent machine tool, and V indicates the material conveying speed.

[0035] Further technical solution: The method for generating the predicted material arrival time is specifically as follows:

[0036] By formula:

[0037] ;

[0038] Generate material arrival forecast time ;

[0039] In the formula, It indicates the end position of the fault, M indicates the position value of the intelligent machine tool, and V indicates the material conveying speed.

[0040] Further technical solution: The expression of the machine tool startup timing analysis model is:

[0041] ;

[0042] In the expression, It indicates the startup time of the intelligent machine tool. It indicates the predicted arrival time of materials. It represents the time correction item; the time correction item value refers to the maximum time required for the intelligent machine tool to switch from the lowest power operation to the standard working state.

[0043] Further technical solution: The time correction item is obtained in the following manner:

[0044] Obtaining a current temperature value of the intelligent machine tool, and generating a temperature difference value based on the current temperature value of the intelligent machine tool and a standard operating temperature value of the intelligent machine tool; wherein the temperature difference value refers to the difference between the current temperature value of the intelligent machine tool and the standard operating temperature value of the intelligent machine tool;

[0045] Generate the required heating time according to the temperature difference and the heating rate; wherein the required heating time refers to the ratio between the temperature difference and the heating rate;

[0046] Obtain the time required for the intelligent machine tool to stop and start, and compare the time required for heating up with the time required for the intelligent machine tool to stop and start;

[0047] If the time required for heating is less than the time required for the intelligent machine tool to stop and start, the time correction item is the time required for the intelligent machine tool to stop and start;

[0048] If the time required for heating is greater than the time required for the intelligent machine tool to stop and start, the time correction item is the time required for heating.

[0049] A system for optimizing energy consumption of intelligent machine tools, the system comprising:

[0050] The data acquisition module is used to obtain the operating data of the current intelligent machine tool and the material data on the conveyor line; the operating data includes the energy consumption of empty material operation and state switching energy consumption; the material data includes the material conveying speed, material fault length and material fault position; the material fault position includes the fault start position and the fault end position;

[0051] A state switching demand analysis unit is used to establish a state switching demand analysis model and generate a state switching demand index based on the current operating data of the intelligent machine tool and the material data on the conveyor line;

[0052] The demand judgment module is used to judge whether the intelligent machine tool needs to operate at the lowest power according to the state switching demand index;

[0053] A switching control module, which is used to adjust the operating state of the intelligent machine tool to the lowest power operation through the control terminal if the intelligent machine tool needs to operate at the lowest power;

[0054] A material analysis module, when the intelligent machine tool enters the lowest power operation, the material analysis module is used to generate a material arrival prediction time based on the end position of the material fault;

[0055] The startup timing analysis module is used to establish a machine tool startup timing analysis model based on the predicted material arrival time and generate the intelligent machine tool startup time;

[0056] The startup control module is used to start the intelligent machine tool through the machine tool control terminal according to the intelligent machine tool startup time.

[0057] The present invention provides a method and system for optimizing the energy consumption of intelligent machine tools, which have the following advantages compared with the prior art:

[0058] The present invention constructs a state switching demand analysis model and a start-up timing analysis model, dynamically integrates multi-dimensional parameters such as empty material operation energy consumption, state switching energy consumption, material fault impact index and start-stop frequency factor, thereby realizing accurate shutdown decision-making and start-stop timing optimization. It can also predict the material arrival time in real time according to the material conveying speed and fault position, and combine with the time correction item to reduce invalid idling energy consumption and frequent start-stop loss, balance energy saving and equipment life maintenance, and effectively improve energy utilization efficiency while ensuring processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart of a method for optimizing energy consumption of intelligent machine tools provided in an embodiment of the present invention.

[0060] Figure 2 This is a flowchart of step S20 provided in an embodiment of the present invention.

[0061] Figure 3 A schematic diagram of the structure of a system for optimizing energy consumption of intelligent machine tools provided by an embodiment of the present invention.

[0062] Figure 4 This is a module block diagram of a state switching requirement analysis unit provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0064] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0065] See also Figure 1 , a method for optimizing the processing energy consumption of an intelligent machine tool provided by an embodiment of the present invention, comprising the following steps:

[0066] Step S10: Acquire the operating data of the current intelligent machine tool and the material data on the conveyor line; wherein the operating data includes the energy consumption of empty material operation and the energy consumption of state switching; the material data includes the material conveying speed, material fault length and material fault position; the material fault position includes the fault start position and the fault end position;

[0067] Step S20: establishing a state switching demand analysis model based on the current operating data of the intelligent machine tool and the material data on the conveyor line, and generating a state switching demand index;

[0068] Step S30: judging whether the intelligent machine tool needs to operate at the lowest power according to the state switching demand index;

[0069] Step S40: If the intelligent machine tool needs to be operated at the lowest power, the operating state of the intelligent machine tool is adjusted to the lowest power operation through the control terminal;

[0070] Step S50: When the intelligent machine tool enters the lowest power operation mode, a material arrival prediction time is generated based on the end position of the material fault;

[0071] Step S60: Based on the predicted material arrival time, a machine tool startup timing analysis model is established to generate the intelligent machine tool startup time;

[0072] Step S70: starting the intelligent machine tool through the machine tool control terminal according to the intelligent machine tool starting time.

[0073] As a preferred embodiment of the present invention, step S10 specifically includes:

[0074] When acquiring material data on the conveyor line, monitoring points can be set on the material conveyor line to obtain the material conveying speed and material position at the monitoring points;

[0075] In addition, the setting methods of the monitoring points include but are not limited to setting multiple monitoring points on the conveyor line, setting a panoramic monitoring point on one side of the conveyor line (the panoramic view refers to the monitoring range of the monitoring point);

[0076] In this embodiment, the material fault refers to a material-free area between adjacent materials on the conveyor line due to the interval exceeding a threshold;

[0077] The specific method for determining whether it is a material fault is:

[0078] Monitor the intervals between materials on the transmission line and generate a material interval value; the material interval value refers to the distance between adjacent materials;

[0079] comparing the material interval value to an interval threshold;

[0080] The interval threshold is a set value, which is set by relevant personnel in this field. The value 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 the error value, and x is not less than 0.

[0081] 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;

[0082] 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 between adjacent materials is a material fault (the gap length is the material fault length).

[0083] See also Figure 2 As a preferred embodiment of the present invention, step S20 specifically includes the following steps:

[0084] S21: Generate material gap energy consumption based on the empty material operation energy consumption of the intelligent machine tool and the material gap arrival prediction value; wherein the material gap energy consumption refers to the product of the empty material operation energy consumption of the intelligent machine tool and the material gap time; and the material gap time refers to the ratio between the material gap length and the material conveying speed;

[0085] It should be explained that the energy consumption during material fault refers to the energy consumption when the intelligent machine tool is idling (no material processing) during material fault.

[0086] S22: generating a material fault impact index according to the material fault length; wherein the material fault impact index refers to the ratio between the material fault length and the fault length threshold;

[0087] It should be explained that the fault length threshold refers to the maximum value of the material fault length under standard working conditions; standard working conditions refer to normal material transmission;

[0088] S23: Establish a state switching demand analysis model, substitute the material fault impact index, material fault energy consumption, state switching energy consumption and start-stop frequency factor into the state switching demand analysis model, and generate a state switching demand index.

[0089] As a preferred embodiment of the present invention, the expression of the state switching demand analysis model is:

[0090] ;

[0091] In the expression, Q represents the state switching demand index, It represents the energy consumption of material fault. It represents the energy consumption of state switching. It represents the material fault impact index. It represents the start-stop frequency factor;

[0092] It should be explained that state switching energy consumption refers to the additional energy consumption generated when the intelligent machine tool switches from the lowest power operating state to the standard working state.

[0093] As a preferred embodiment of the present invention, the start-stop frequency factor is obtained in the following manner:

[0094] Get the number of starts and stops in the current operation cycle of the intelligent machine tool;

[0095] It should be explained that an operating cycle refers to a period of time of equal length; the current operating cycle refers to an operating cycle that has not yet been completed in terms of time sequence. For example, if the operating cycle is one hour long (corresponding to the timetable, the start and end points of each hour are the two endpoints of the operating cycle in terms of time sequence), and within the current operating cycle, the intelligent machine tool has started and stopped a total of c times (i.e., one cycle is counted as starting, running at minimum power, and then starting again), then the number of starts and stops in the current operating cycle of the intelligent machine tool is c.

[0096] Generate a start-stop frequency based on the number of starts and stops in the current operating cycle of the intelligent machine tool and the duration of the operating cycle. The start-stop frequency refers to the ratio between the number of starts and stops in the current operating cycle of the intelligent machine tool and the duration of the operating cycle.

[0097] Generate a start-stop factor based on the start-stop frequency; the start-stop factor refers to the ratio between the start-stop frequency and the start-stop frequency threshold;

[0098] It should be explained that the start-stop frequency threshold is a set value, which is set by relevant personnel in this field;

[0099] In addition, the frequent start and stop of intelligent machine tools will cause the motor, transmission system, bearings and other components of the machine tool to suffer more startup impact; each time it is started, the motor needs to overcome static inertia. 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.

[0100] As a preferred embodiment of the present invention, the method of determining whether the intelligent machine tool needs to operate at the lowest power is specifically as follows:

[0101] comparing the state switching demand index with a state switching demand index threshold;

[0102] The value of the state switching demand index threshold is set by relevant personnel in this field;

[0103] If the state switching demand index is less than or equal to the state switching demand index threshold, it is determined that the intelligent machine tool does not need to operate at the minimum power. In this case, the smaller the state switching demand index, the less demand the intelligent machine tool needs to operate at the minimum power.

[0104] If the state switching demand index is greater than the state switching demand index threshold, it is determined that the intelligent machine tool needs to operate at the lowest power; at this time, the larger the state switching demand index is, the greater the demand for the intelligent machine tool to operate at the lowest power.

[0105] As a preferred embodiment of the present invention, the method of controlling the intelligent machine tool to operate at the lowest power is specifically as follows:

[0106] Generate a material fault arrival prediction value based on the material fault location and material conveying speed. The material fault location refers to the end of the material fault closest to the intelligent machine tool.

[0107] It should be explained that the distance between the material fault and the intelligent machine tool refers to the length of the path formed by the material moving on the conveyor line and finally reaching the intelligent machine tool;

[0108] In this embodiment, the material fault can also be understood as a line segment that moves along a fixed trajectory. The endpoint of the line segment that first reaches the position of the intelligent machine tool is the end of the material fault that is closest to the intelligent machine tool.

[0109] According to the predicted value of material fault arrival, the intelligent machine tool is controlled by the control terminal to operate at the lowest power;

[0110] In this embodiment, the lowest power operation of the intelligent machine tool includes stop operation and state maintenance operation; wherein, the operation power of stop operation is 0, and the state switching energy consumption in the state switching demand analysis model is It represents the additional energy consumption when the intelligent machine tool switches from the stop state to the standard working state;

[0111] In actual applications, when the intelligent machine tool cannot operate at the minimum power due to processing requirements, the minimum required state of the intelligent machine tool can be maintained (i.e., state-maintaining operation) to minimize the energy consumption of the intelligent processing machine tool when waiting for materials; if the minimum power operation is state-maintaining operation, the state switching energy consumption in the state switching demand analysis model It represents the additional energy consumption when the intelligent machine tool switches from the state maintenance power to the standard working state.

[0112] As a preferred embodiment of the present invention, the method for generating the material fault arrival prediction value is specifically as follows:

[0113] According to the conveying trajectory and conveying direction of the material conveying line, a horizontal axis is established to obtain the material fault position value and the intelligent machine tool position value in the horizontal axis;

[0114] By formula:

[0115] ;

[0116] Generate material fault arrival prediction value ;

[0117] In the formula, It indicates the starting position of the fault, M indicates the position value of the intelligent machine tool, and V indicates the material conveying speed.

[0118] As a preferred embodiment of the present invention, the method for generating the predicted material arrival time is specifically as follows:

[0119] By formula:

[0120] ;

[0121] Generate material arrival forecast time ;

[0122] In the formula, It indicates the end position of the fault, M indicates the position value of the intelligent machine tool, and V indicates the material conveying speed.

[0123] As a preferred embodiment of the present invention, the expression of the machine tool startup timing analysis model is:

[0124] ;

[0125] In the expression, It indicates the startup time of the intelligent machine tool. It indicates the predicted arrival time of materials. It represents the time correction item. The time correction item value refers to the maximum time required for the intelligent machine tool to switch from the lowest power operation to the standard working state.

[0126] It should be explained that the time correction item is a constant.

[0127] As a preferred embodiment of the present invention, the time correction item is obtained in the following manner:

[0128] Obtaining a current temperature value of the intelligent machine tool, and generating a temperature difference value based on the current temperature value of the intelligent machine tool and a standard operating temperature value of the intelligent machine tool; wherein the temperature difference value refers to the difference between the current temperature value of the intelligent machine tool and the standard operating temperature value of the intelligent machine tool;

[0129] Generate the required heating time according to the temperature difference and the heating rate; wherein the required heating time refers to the ratio between the temperature difference and the heating rate;

[0130] It should be explained that the heating rate refers to the heating rate of the intelligent machine tool, and its value is obtained by means of, but not limited to, linear regression equations;

[0131] In addition, in this embodiment, the current temperature value of the intelligent machine tool is lower than the standard operating temperature value of the intelligent machine tool; if the current temperature value of the intelligent machine tool is higher than the standard operating temperature value of the intelligent machine tool, the value of the heating time is 0;

[0132] Obtain the time required for the intelligent machine tool to stop and start, and compare the time required for heating up with the time required for the intelligent machine tool to stop and start;

[0133] If the time required for heating is less than the time required for the intelligent machine tool to stop and start, the time correction item is the time required for the intelligent machine tool to stop and start;

[0134] If the time required for heating is longer than the time required for the intelligent machine tool to stop and start, the time correction item is the time required for heating;

[0135] It should be explained that if the time required for heating is equal to the time required for the intelligent machine tool to shut down and start up, then one of the two should be selected as the time correction item;

[0136] In this embodiment, the time correction term It can also include the conveying time difference caused by fluctuations in material conveying speed and the time required for the intelligent machine tool to stop and start (if it is running at the lowest power, it is in low power state) to the time required for normal operation, etc.

[0137] See also Figure 3 The present invention also provides an optimization system for intelligent machine tool processing energy consumption, the system comprising:

[0138] The data acquisition module 10 is used to obtain the operating data of the current intelligent machine tool and the material data on the conveyor line; wherein the operating data includes the energy consumption of empty material operation and the energy consumption of state switching; the material data includes the material conveying speed, material fault length and material fault position; the material fault position includes the fault start position and the fault end position;

[0139] The state switching demand analysis unit 20 is used to establish a state switching demand analysis model and generate a state switching demand index based on the current operation data of the intelligent machine tool and the material data on the conveyor line;

[0140] The demand judgment module 30 is used to judge whether the intelligent machine tool needs to operate at the lowest power according to the state switching demand index;

[0141] The switching control module 40 is used to adjust the operating state of the intelligent machine tool to the lowest power operation through the control terminal if the intelligent machine tool needs to operate at the lowest power.

[0142] See also Figure 4 As a preferred embodiment of the present invention, the state switching demand analysis unit specifically includes:

[0143] The fault energy consumption analysis module 21 is used to generate the material fault energy consumption based on the empty material operation energy consumption of the intelligent machine tool and the material fault arrival prediction value. The material fault energy consumption is the product of the empty material operation energy consumption of the intelligent machine tool and the material fault time. The material fault time is the ratio between the material fault length and the material conveying speed.

[0144] The fault impact analysis model 22 is used to generate a material fault impact index based on the material fault length; wherein the material fault impact index refers to the ratio between the material fault length and the fault length threshold;

[0145] The demand analysis module 23 is used to establish a state switching demand analysis model, substitute the material fault impact index, material fault energy consumption, state switching energy consumption and start-stop frequency factor into the state switching demand analysis model, and generate a state switching demand index.

[0146] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing energy consumption of intelligent machine tools, characterized in that: The following steps are involved: Obtain the current operating data of the intelligent machine tool and the material data on the conveyor line; the operating data includes the energy consumption of empty material operation and state switching energy consumption; the material data includes the material conveying speed, material fault length and material fault position; the material fault position includes the fault start position and the fault end position; Based on the current operating data of the intelligent machine tool and the material data on the conveyor line, a state switching demand analysis model is established to generate a state switching demand index; The state switching demand index is generated in the following manner: The material gap energy consumption is generated based on the energy consumption of the IMT during empty-material operation and the predicted material gap arrival value. The material gap energy consumption is the product of the energy consumption of the IMT during empty-material operation and the material gap time. The material gap time is the ratio of the material gap length to the material conveying speed. Generate a material fault impact index based on the material fault length; the material fault impact index refers to the ratio between the material fault length and the fault length threshold; Establish a state switching demand analysis model, substitute the material fault impact index, material fault energy consumption, state switching energy consumption and start-stop frequency factor into the state switching demand analysis model to generate the state switching demand index; The expression of the state switching demand analysis model is: ； In the expression, Q represents the state switching demand index, It represents the energy consumption of material fault. It represents the energy consumption of state switching. It represents the material fault impact index. It 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 operating state to the standard working state; According to the state switching demand index, determine whether the intelligent machine tool needs to operate at the lowest power; If the intelligent machine tool needs to operate at the lowest power, the operating state of the intelligent machine tool is adjusted to the lowest power through the control terminal; When the intelligent machine tool enters the lowest power operation mode, the material arrival prediction time is generated based on the end position of the material fault; Based on the predicted material arrival time, a machine tool startup timing analysis model is established to generate the intelligent machine tool startup time; According to the intelligent machine tool startup time, start the intelligent machine tool through the machine tool control terminal.

2. The method for optimizing the processing energy consumption of an intelligent machine tool according to claim 1, characterized in that: The method for obtaining the start-stop frequency factor is specifically as follows: Get the number of starts and stops in the current operating cycle of the intelligent machine tool; an operating cycle refers to a time period of equal length; the current operating cycle refers to the operating cycle that has not yet been completed in terms of time sequence; Generate a start-stop frequency based on the number of starts and stops in the current operating cycle of the intelligent machine tool and the duration of the operating cycle. The start-stop frequency refers to the ratio between the number of starts and stops in the current operating cycle of the intelligent machine tool and the duration of the operating cycle. A start-stop factor is generated 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.

3. The method for optimizing the processing energy consumption of an intelligent machine tool according to claim 1, characterized in that: The method of controlling the intelligent machine tool to operate at the lowest power is specifically as follows: Generate a material fault arrival prediction value based on the material fault location and material conveying speed. The material fault location refers to the end of the material fault closest to the intelligent machine tool. According to the predicted value of material fault arrival, the intelligent machine tool is controlled by the control end to operate at the lowest power.

4. The method for optimizing energy consumption of intelligent machine tools according to claim 3, characterized in that: The method for generating the material fault arrival prediction value is specifically as follows: According to the conveying trajectory and conveying direction of the material conveying line, a horizontal axis is established to obtain the material fault position value and the intelligent machine tool position value in the horizontal axis; By formula: ； Generate material fault arrival prediction value ; In the formula, It indicates the starting position of the fault, M indicates the position value of the intelligent machine tool, and V indicates the material conveying speed.

5. The method for optimizing energy consumption of intelligent machine tools according to claim 1, characterized in that: The method for generating the predicted material arrival time is specifically as follows: By formula: ； Generate material arrival forecast time ; In the formula, It indicates the end position of the fault, M indicates the position value of the intelligent machine tool, and V indicates the material conveying speed.

6. The method for optimizing energy consumption of intelligent machine tools according to claim 5, characterized in that: The expression of the machine tool startup timing analysis model is: ； In the expression, It indicates the startup time of the intelligent machine tool. It indicates the predicted arrival time of materials. It represents the time correction item; the time correction item value refers to the maximum time required for the intelligent machine tool to switch from the lowest power operation to the standard working state.

7. The method for optimizing the processing energy consumption of an intelligent machine tool according to claim 6, characterized in that: The method for obtaining the time correction item is specifically as follows: Obtaining a current temperature value of the intelligent machine tool, and generating a temperature difference value based on the current temperature value of the intelligent machine tool and a standard operating temperature value of the intelligent machine tool; wherein the temperature difference value refers to the difference between the current temperature value of the intelligent machine tool and the standard operating temperature value of the intelligent machine tool; Generate the required heating time according to the temperature difference and the heating rate; wherein the required heating time 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 heating up with the time required for the intelligent machine tool to stop and start; If the time required for heating is less than the time required for the intelligent machine tool to stop and start, the time correction item is the time required for the intelligent machine tool to stop and start; If the time required for heating is greater than the time required for the intelligent machine tool to stop and start, the time correction item is the time required for heating.

8. An optimization system for energy consumption of intelligent machine tools, characterized in that: A method for optimizing energy consumption of an intelligent machine tool according to any one of claims 1 to 7, the system comprising: The data acquisition module is used to obtain the operating data of the current intelligent machine tool and the material data on the conveyor line; the operating data includes the energy consumption of empty material operation and state switching energy consumption; the material data includes the material conveying speed, material fault length and material fault position; the material fault position includes the fault start position and the fault end position; A state switching demand analysis unit is used to establish a state switching demand analysis model and generate a state switching demand index based on the current operating data of the intelligent machine tool and the material data on the conveyor line; The demand judgment module is used to judge whether the intelligent machine tool needs to operate at the lowest power according to the state switching demand index; A switching control module, which is used to adjust the operating state of the intelligent machine tool to the lowest power operation through the control terminal if the intelligent machine tool needs to operate at the lowest power; A material analysis module, when the intelligent machine tool enters the lowest power operation, the material analysis module is used to generate a material arrival prediction time based on the end position of the material fault; The startup timing analysis module is used to establish a machine tool startup timing analysis model based on the predicted material arrival time and generate the intelligent machine tool startup time; A startup control module is used to start the intelligent machine tool through the machine tool control terminal according to the startup time of the intelligent machine tool; The state switching demand analysis unit specifically includes: The fault energy consumption analysis module is used to generate material fault energy consumption based on the energy consumption of the intelligent machine tool's empty material operation and the predicted value of the material fault arrival. The material fault energy consumption is the product of the energy consumption of the intelligent machine tool's empty material operation and the material fault time. The material fault time is the ratio between the material fault length and the material conveying speed. The fault impact analysis model is used to generate a material fault impact index based on the material fault length; wherein the material fault impact index refers to the ratio between the material fault length and the fault length threshold; The demand analysis module is used to establish a state switching demand analysis model, substitute the material fault impact index, material fault energy consumption, state switching energy consumption and start-stop frequency factor into the state switching demand analysis model to generate the state switching demand index; The expression of the state switching demand analysis model is: ； In the expression, Q represents the state switching demand index, It represents the energy consumption of material fault. It represents the energy consumption of state switching. It represents the material fault impact index. It 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 operating state to the standard working state.

Images