Method for intelligent control of electric energy in an engine production plant

Abstract

The application discloses a kind of engine production workshop electric energy intelligent control method, comprising: the processing equipment XYZ axis processing state temperature variation model under different working conditions is obtained;The heat treatment equipment processing state radiation temperature variation model under different working conditions is obtained;According to the processing equipment XYZ axis processing state temperature variation model and the heat treatment equipment processing state radiation temperature variation model, processing equipment XYZ axis comprehensive temperature prediction model is established, and processing equipment XYZ axis comprehensive temperature prediction model is used to obtain the XYZ axis temperature of the processing equipment in different working conditions when standard field temperature;Processing equipment XYZ axis XYZ axis temperature compensation parameter at different temperatures is obtained, and XYZ axis temperature compensation parameter is obtained according to the temperature and thermal deformation corresponding relationship of the measured processing equipment XYZ axis;The processing equipment XYZ axis comprehensive temperature prediction model and the XYZ axis temperature compensation parameter are used for the XYZ axis accuracy compensation of processing equipment processing.

Description

Technical Field

[0001] This invention relates to the field of automobile manufacturing, and more specifically to a method for intelligent control of electrical energy in an engine production workshop. Background Technology

[0002] The machining of automotive powertrain components needs to be completed in a machining workshop. With the improvement of automobile quality, the machining precision of automotive powertrain components has reached the micron level and has extremely high requirements for temperature. Figure 1 This is a schematic diagram of the equipment layout in a current automotive powertrain manufacturing workshop. Figure 1 As shown, the automotive powertrain machining workshop mainly includes machining equipment 1, heat treatment equipment 2, temperature sensors 3, and a logistics door 4. The machining equipment preferably uses PLC program-controlled machining equipment to perform milling, boring, drilling, and other machining operations on the workpieces. Machining equipment 1 generates a significant amount of heat from cutting heat, grinding heat, hydraulic stations, and motor operation. Heat treatment equipment 2 mainly includes quenching and tempering equipment for parts such as crankshafts, and also generates a significant amount of heat during operation. The opening and closing of the workshop logistics door 4 allows a large amount of outdoor air to enter the workshop, causing changes in the ambient temperature. Measurements show that the factors affecting the part machining temperature are mainly the temperatures generated by machining equipment 1 and heat treatment equipment 2 themselves, and the ambient temperature of the workshop. The temperature of machining equipment 1 itself includes cutting heat from the cutting tools and frictional heat from friction pairs such as the spindle, guide rails, and lead screws. The ambient temperature of the workshop refers to the influence of the surrounding environment temperature on the machining equipment. Thermal expansion and contraction caused by temperature variations in the equipment leads to deformation of the XYZ axes of motion during machining on machining equipment 1, affecting the machining accuracy of the product. Therefore, in the existing technology, temperature sensors 3 and air conditioners are used to monitor and regulate the temperature of the workshop. However, if precise monitoring and regulation of the workshop temperature is required, temperature sensors 3 corresponding to each processing equipment need to be installed, which leads to problems such as low control accuracy and temperature response lag. Due to the high requirements of the processing workshop for ambient temperature, the air conditioner needs to run continuously to maintain the ambient temperature, consuming a large amount of electrical energy. Summary of the Invention

[0003] To address the aforementioned problems, the purpose of this application is to provide a method for intelligent power control in engine production workshops that can save electrical energy and increase machining accuracy.

[0004] This application provides a method for intelligent control of electrical energy in an engine production workshop, characterized by the following steps:

[0005] S1) Obtain the temperature variation model of the XYZ axis machining state of the machining equipment under different working conditions;

[0006] S2) Obtain the heat treatment equipment's radiation temperature variation model under different working conditions;

[0007] S3) Based on the temperature change model of the XYZ axis of the processing equipment and the radiation temperature change model of the processing equipment, a comprehensive temperature prediction model of the XYZ axis of the processing equipment is established. The comprehensive temperature prediction model of the XYZ axis of the processing equipment is used to obtain the XYZ axis temperature of the processing equipment under different working conditions at the standard field temperature.

[0008] S4) Obtain the XYZ axis temperature compensation parameters of the processing equipment at different temperatures. The XYZ axis temperature compensation parameters are obtained by measuring the correspondence between the temperature and thermal deformation of the XYZ axis of the processing equipment.

[0009] S5) The comprehensive temperature prediction model of the XYZ axis of the processing equipment and the temperature compensation parameters of the XYZ axis are used for XYZ axis accuracy compensation during processing.

[0010] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, step S1) further includes:

[0011] S11) Acquire the different status signals of the processing equipment under different working conditions;

[0012] S12) Measure the temperature change process of the XYZ axis of the processing equipment when the processing equipment outputs different state signals and establish a temperature change model of the XYZ axis of the processing equipment in the processing state.

[0013] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, step S2) further includes:

[0014] S21) Obtain the output signals of different states of the heat treatment equipment under different working conditions;

[0015] S22) Measure the radiant temperature change of the heat treatment equipment under different working conditions based on the output signals of the heat treatment equipment under different state conditions, and establish a model of the radiant temperature change of the heat treatment equipment under different processing conditions.

[0016] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, the comprehensive temperature prediction model of the XYZ axis of the processing equipment is obtained through machine learning based on the temperature change model of the XYZ axis of the processing equipment and the radiation temperature change model of the heat treatment equipment.

[0017] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, the different working states of the heat treatment equipment include start-up, quenching, tempering, standby, and shutdown.

[0018] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, the different working states of the processing equipment include start-up, normal operation, heavy-load rough machining, light-load fine machining, and stop.

[0019] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, step S4) further includes:

[0020] S41) Determine the standard length of the XYZ axis of the machining equipment at the standard field temperature;

[0021] S42) Obtain the percentage of thermal deformation of the XYZ axis at different temperatures. The percentage of thermal deformation of the XYZ axis at different temperatures is obtained by measuring the length of the XYZ axis of the processing equipment and the standard length of the XYZ axis in the actual working temperature range. The actual working temperature range is obtained based on the lowest and highest temperatures when the processing equipment is working.

[0022] S43) Convert the percentage of thermal deformation of the XYZ axis at different temperatures into XYZ axis temperature compensation parameters.

[0023] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, the actual operating temperature range is 14℃~26℃.

[0024] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, the standard on-site temperature is 20°C.

[0025] Furthermore, in the method for intelligent control of electrical energy in the engine production workshop, step S5) further includes:

[0026] S51) Obtain the output signals of different states of the processing equipment under different working conditions;

[0027] S52) Determine the state of the processing equipment based on the output of different state signals of the processing equipment, and obtain the XYZ axis temperature of the processing equipment using the comprehensive temperature prediction model of the XYZ axis of the processing equipment;

[0028] S53) Based on the XYZ axis temperature of the machining equipment, the accuracy of the XYZ axis during machining is compensated using the XYZ axis temperature compensation parameters.

[0029] The technical solution provided in this application has the following advantages:

[0030] 1. Because the established comprehensive temperature prediction model for the XYZ axes of the machining equipment can effectively predict the temperature of the machining equipment;

[0031] 2. By adopting XYZ axis temperature compensation parameters, the machining accuracy error caused by temperature during equipment processing is effectively eliminated, the accuracy of the XYZ axis during processing is increased, and repeated physical temperature adjustment of air conditioning is avoided, thereby saving a lot of energy;

[0032] 3. It effectively reduces the need for sensor placement and saves costs. Attached Figure Description

[0033] Figure 1 A schematic diagram of the equipment layout in a current automotive powertrain processing workshop.

[0034] Figure 2 A flowchart illustrating a preferred embodiment of the intelligent power control method for an engine production workshop according to an embodiment of the present invention;

[0035] Figure 3a and 3b This is a schematic diagram of a curve model illustrating the influence of changes in the processing equipment status signal on the temperature of the processing equipment itself, as preferred in an embodiment of the present invention.

[0036] Figure 4 This is a schematic diagram illustrating the temperature gradient of a preferred heat treatment device according to different ranges in an embodiment of the present invention.

[0037] Figure 5 This is a schematic diagram illustrating the acquisition of XYZ axis temperature compensation parameters, which is a preferred application example of an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. The described embodiments are some embodiments of this application, but not all embodiments. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0039] Furthermore, it should be noted that, unless otherwise explicitly specified and limited, terms such as “installation,” “connection,” and “linkage” used in the description of this application should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; or it can be a connection within two components. Those skilled in the art can understand its specific meaning in this application according to the specific circumstances.

[0040] A schematic diagram of the equipment layout in an existing automotive powertrain processing workshop is shown below. Figure 1 As shown. The automotive powertrain processing workshop mainly includes processing equipment 1, heat treatment equipment 2, temperature sensors 3, and logistics gates 4.

[0041] Figure 2 This is a flowchart illustrating a preferred embodiment of the intelligent electrical control method for an engine production workshop according to an embodiment of the present invention. Figure 2 As shown, the method for intelligent electrical control in an engine production workshop includes the following steps:

[0042] S1) Obtain the temperature variation model of the XYZ axis machining state of the machining equipment under different working conditions;

[0043] S2) Obtain the heat treatment equipment's radiation temperature variation model under different working conditions;

[0044] S3) Establish a comprehensive temperature prediction model for the XYZ axis of the processing equipment based on the temperature change model of the XYZ axis of the processing equipment and the radiation temperature change model of the heat treatment equipment. The comprehensive temperature prediction model for the XYZ axis of the processing equipment is used to obtain the XYZ axis temperature of the processing equipment under different working conditions at the standard field temperature.

[0045] S4) Obtain the XYZ axis temperature compensation parameters of the processing equipment at different temperatures. The XYZ axis temperature compensation parameters are obtained by measuring the relationship between the temperature and thermal deformation of the XYZ axis of the processing equipment.

[0046] S5) Use the comprehensive temperature prediction model of the XYZ axis of the machining equipment and the temperature compensation parameters of the XYZ axis to compensate for the accuracy of the XYZ axis during machining.

[0047] The preferred method for intelligent electrical control in engine production workshops according to the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0048] Step S1) Obtain the temperature change model of the XYZ axis machining state of the machining equipment under different working conditions.

[0049] Preferably, step S1) further includes:

[0050] S11) Acquire the different status signals of the processing equipment under different working conditions;

[0051] S12) Measure the temperature change process of the XYZ axis of the machining equipment when the machining equipment outputs different state signals and establish a temperature change model of the XYZ axis of the machining equipment under different machining states.

[0052] Preferably, the different working states of the processing equipment include start-up, normal operation, heavy-load roughing, light-load finishing, and stop.

[0053] In this preferred embodiment, the different status signal outputs of the processing equipment are PLC output status signals.

[0054] In this step, the preferred specific steps of this embodiment are as follows:

[0055] Define the state variables of the processing equipment:

[0056] By setting corresponding PLC output status signals for the processing equipment's start-up, normal operation, heavy-load roughing, light-load finishing, and stop states, and by acquiring these signals, the system can perceive the different states of the processing equipment.

[0057] Specifically, the operating status variable signals of the equipment are set and defined in the PLC program of the equipment. For example, the processing equipment is divided into three categories: heavy-duty rough processing (PLC output port M100.0 is set to 1 otherwise), light-duty fine processing (PLC output port M100.1 is set to 1 otherwise), and shutdown (PLC output port M100.2 is set to 1 otherwise). When the equipment enters different process states according to the program, it outputs corresponding status signals. Thus, the operating status of the processing equipment can be determined based on the collected output status signals.

[0058] The relationship between different operating states of the processing equipment and the temperature of the processing equipment itself was obtained:

[0059] By collecting data on the process states of the machining equipment during startup, normal operation, heavy-load roughing, light-load finishing, and shutdown, and by measuring the temperature changes of the XYZ axes of the machining equipment, the relationship between the temperature changes corresponding to different process states is obtained.

[0060] In this embodiment, the standard ambient temperature is preferably 20°C.

[0061] Specifically, under the standard condition of a standard ambient temperature of 20°C, when a single processing equipment is operated, this embodiment preferably uses a handheld infrared temperature test gun or arranges a temperature sensor at the corresponding position of the processing equipment to measure and record the temperature changes of the XYZ axes during startup, normal operation, heavy-load roughing, light-load finishing, and shutdown, so as to obtain the temperature change relationship.

[0062] Establish a model to illustrate the impact of changes in the processing equipment's status signals on the equipment's own temperature:

[0063] Figure 3a and 3b This is a schematic diagram of a curve model illustrating the influence of changes in the processing equipment's status signal on the processing equipment's own temperature, as preferred in an embodiment of the present invention. Figure 3a and3b As shown, the time of the machining equipment's state change is used as the horizontal axis, and temperature as the vertical axis. Using the state change signal as the origin, a curve model of the influence of the machining equipment's state signal changes on the XYZ axis temperature can be obtained. When the machining equipment is in a low-power state signal output of 1 (e.g., power-on, normal operation, and stop state) or a high-power state signal output of 1 (e.g., heavy-load roughing and light-load finishing), the temperature of the machining equipment's XYZ axes will change with time, exhibiting a temperature rise phase and a temperature equilibrium phase. When the machining equipment is in a low-power state signal output of 0 or a high-power state signal output of 0, the temperature of the machining equipment's XYZ axes will decrease with time. By recording the data of the temperature rise and fall of the machining equipment's XYZ axes, a model of the machining equipment's XYZ axis temperature change during machining can be established.

[0064] Step S2) Obtain the heat treatment equipment processing state radiation temperature change model under different working conditions.

[0065] Preferably, step S2) further includes:

[0066] S21) Obtain the output signals of different states of the heat treatment equipment under different working conditions;

[0067] S22) Measure the radiant temperature change of the heat treatment equipment under different working conditions based on the output signals of the heat treatment equipment under different state conditions, and establish a model of the radiant temperature change of the heat treatment equipment under processing conditions.

[0068] Preferably, the different operating states of the heat treatment equipment include start-up, quenching, tempering, standby, and shutdown.

[0069] In this preferred embodiment, the different status signal outputs of the heat treatment equipment are PLC output status signals.

[0070] In this step, the preferred specific steps of this embodiment are as follows:

[0071] Define the state variables of the heat treatment equipment:

[0072] By setting corresponding PLC output status signals for different process states of heat treatment equipment, such as startup, quenching, tempering, standby, and shutdown, and by acquiring these signals, the system can perceive the different states of the heat treatment equipment.

[0073] Specifically, the operating status variable signals of the equipment are set and defined in the PLC program of the equipment. For example, for heat treatment equipment, the PLC output port M101.0 is set to 1 otherwise for quenching high-load heating, and 0 otherwise for tempering low-load holding heat treatment; the PLC output port M101.1 is set to 1 otherwise for tempering low-load holding heat treatment, and 0 otherwise for shutdown; the PLC output port M101.2 is set to 1 otherwise for shutdown. When the equipment performs different process states according to the program, it outputs corresponding status signals. Thus, the operating status of the heat treatment equipment can be determined based on the collected output status signals.

[0074] The relationship between different operating states of the heat treatment equipment and the external temperature change of the processing equipment was obtained:

[0075] Figure 4 This is a schematic diagram illustrating the temperature gradient of a preferred heat treatment device according to different ranges, as described in an embodiment of the present invention. Figure 4 As shown, by measuring the temperature change process of the XYZ axis of the processing equipment under the influence of the surrounding temperature distribution gradient during different process states of the heat treatment equipment (start-up, quenching, tempering, standby, and shutdown), the relationship between the different operating states of the heat treatment equipment and the external temperature change of the processing equipment is obtained.

[0076] Specifically, under the standard condition of a standard on-site temperature of 20℃, the heat treatment equipment was operated alone, and the influence of different process states of the heat treatment equipment (start-up, quenching, tempering, standby, and shutdown) on the ambient temperature gradient distribution of the surrounding processing equipment was measured using temperature sensors arranged in the workshop.

[0077] Establish a model to illustrate the impact of changes in the state signal of the heat treatment equipment on the external temperature of the processing equipment:

[0078] The area surrounding the heat treatment equipment is divided into different zones according to a gradient. Target points are established within these zones (e.g., measuring temperature at zones 1, 2, 3, 4, etc. in the diagram). The operating state change time of the heat treatment equipment is plotted on the horizontal axis, and temperature on the vertical axis. Using the state change signal as the origin of the coordinate system, a curve model can be obtained showing the influence of the heat treatment equipment's state signal changes on the external temperature of the processing equipment within the target point zone. The method for collecting data to establish a radiation temperature change model of the heat treatment equipment's processing state is similar to establishing a temperature change model of the processing equipment's XYZ axis processing state, and therefore will not be elaborated upon further.

[0079] Step S3) Establish a comprehensive temperature prediction model for the XYZ axes of the processing equipment based on the temperature change model of the XYZ axes of the processing equipment and the radiation temperature change model of the heat treatment equipment. The comprehensive temperature prediction model for the XYZ axes of the processing equipment is used to obtain the XYZ axis temperature of the processing equipment under different working conditions at the standard field temperature.

[0080] Preferably, the comprehensive temperature prediction model for the XYZ axes of the processing equipment is obtained through machine learning based on the temperature change model of the XYZ axes of the processing equipment and the radiation temperature change model of the heat treatment equipment.

[0081] In this step, the preferred specific steps of this embodiment are as follows:

[0082] By superimposing the temperature change model of the processing equipment itself with the external temperature change model, a comprehensive temperature prediction of the processing equipment can be obtained.

[0083] Specifically, a model of the impact of temperature on machining equipment is established. This involves combining the temperature variation model of the machining equipment's XYZ axes under machining conditions with the radiation temperature variation model of the heat treatment equipment under machining conditions. Through machine learning training, the temperature variation model of the machining equipment itself is obtained, and this model is superimposed with the external temperature variation model to obtain a comprehensive temperature prediction model for the XYZ axes of the machining equipment. This model can predict the temperature of the machining equipment within different ranges when different equipment operates under different conditions.

[0084] Step S4) Obtain the XYZ axis temperature compensation parameters of the machining equipment at different temperatures. The XYZ axis temperature compensation parameters are obtained by measuring the correspondence between the temperature and thermal deformation of the XYZ axis of the machining equipment.

[0085] Preferably, step S4) further includes:

[0086] S41) Determine the standard length of the XYZ axis of the machining equipment at the standard field temperature;

[0087] S42) Obtain the percentage of thermal deformation of the XYZ axis at different temperatures. The percentage of thermal deformation of the XYZ axis at different temperatures is obtained by measuring the length of the XYZ axis of the processing equipment and the standard length of the XYZ axis in the actual working temperature range. The actual working temperature range is obtained based on the lowest and highest temperatures when the processing equipment is working.

[0088] S43) Convert the percentage of thermal deformation of the XYZ axis at different temperatures into XYZ axis temperature compensation parameters.

[0089] Preferably, the actual operating temperature range is 14℃~26℃.

[0090] In this step, the preferred specific steps of this embodiment are as follows:

[0091] Obtain the XYZ axis temperature compensation parameters for different machining equipment:

[0092] By measuring the relationship between temperature and thermal deformation on the XYZ axes of the machining equipment, temperature compensation parameters for the XYZ axes of different machining equipment can be obtained.

[0093] Figure 5This is a schematic diagram illustrating the acquisition of XYZ axis temperature compensation parameters, representing a preferred application example of an embodiment of the present invention. Figure 5 As shown, the processing equipment includes a processing equipment bed 11 and a processing equipment spindle box 12. The workpiece 13 is placed on the processing equipment bed 11. At 20℃, the processing equipment spindle box 12 is moved to a fixed position A by a program. A dial indicator 14 is attached to the bed via a magnetic base, with its side contacting the spindle box and zeroed, thus obtaining the standard length of the XYZ axes. The processing equipment is repeatedly moved to a fixed position A by program control at temperatures of 14℃, 15℃, 16℃, 17℃, 18℃, 19℃, 20℃, 21℃, 22℃, 23℃, 24℃, 25℃, and 26℃. The readings of the dial indicator 14 are recorded at the corresponding temperatures. The thermal expansion and contraction changes of the XYZ axes of the equipment at the corresponding temperatures are obtained through the dial indicator 14. This is because the processing equipment itself also has a gradual rate of thermal conduction and thermal expansion and contraction when the ambient temperature changes. The XYZ axes of the machining equipment will have corresponding thermal expansion and contraction data depending on the direction of heat conduction and the temperature in the surrounding environment. It is worth noting that... Figure 4 The diagram only schematically illustrates the percentage of thermal deformation of the X-axis of the machining equipment at different temperatures. The testing methods for the Y-axis and Z-axis can be obtained by referring to the X-axis method. After obtaining the percentage data of thermal deformation of the XYZ axes at different temperatures, the data is converted into XYZ axis temperature compensation parameters that can be read by the machining equipment through methods such as computer language conversion. In this embodiment, a program for converting the data into XYZ axis temperature compensation parameters that can be read by a PLC is preferred.

[0094] Optionally, the compensation accuracy of the machining equipment can be tested after setting the XYZ axis temperature compensation parameters. The steps are as follows:

[0095] The repeatability of the XYZ axes of the processing equipment was tested at the end position of the processed product 13 using dial indicator 14 under different temperature changes and with temperature compensation parameters superimposed.

[0096] Specifically, at 20℃, the spindle box 12 of the machining equipment is programmed to a fixed position A. The magnetic base of the dial indicator 14 is then attached to the machine bed, with the dial indicator 14's side contacting the spindle box 12 and zeroed. The machining equipment is then repeatedly programmed to a fixed position A at temperatures of 14℃, 15℃, 16℃, 17℃, 18℃, 19℃, 20℃, 21℃, 22℃, 23℃, 24℃, 25℃, and 26℃, with XYZ axis temperature compensation parameters superimposed. The dial indicator reading is recorded to determine if it remains zero. If not, the XYZ axis temperature compensation parameters need to be re-acquired.

[0097] Step S5) Use the XYZ axis comprehensive temperature prediction model and XYZ axis temperature compensation parameters of the machining equipment to compensate for the XYZ axis accuracy during machining.

[0098] Preferably, step S5) further includes:

[0099] S51) Obtain the output signals of different states of the processing equipment under different working conditions;

[0100] S52) Determine the status of the processing equipment based on the output of different status signals of the processing equipment, and use the comprehensive temperature prediction model of the XYZ axes of the processing equipment to obtain the XYZ axis temperature of the processing equipment.

[0101] S53) Based on the XYZ axis temperature of the machining equipment, the accuracy of the XYZ axis during machining is compensated using the XYZ axis temperature compensation parameters.

[0102] In this step, the preferred specific steps of this embodiment are as follows:

[0103] Generate XYZ axis temperature compensation parameters for the machining equipment within different ranges based on the equipment status signals:

[0104] During production, operating status signals from processing equipment and heat treatment equipment are collected. Based on the XYZ axis comprehensive temperature prediction model for the processing equipment, the comprehensive temperature of the processing equipment within different ranges is predicted. The predicted comprehensive temperature is then used to obtain the corresponding XYZ axis temperature compensation parameters for the processing equipment.

[0105] Specifically, the system operates during normal production. It monitors the real-time operating status of all equipment. When equipment status changes (e.g., shutdown), it outputs corresponding status signals and predicts the overall temperature of the processing equipment within different ranges based on the XYZ axis comprehensive temperature prediction model. It then sends the corresponding XYZ axis temperature compensation parameters to the equipment for compensation. This compensates for the impact of equipment status changes on the workshop ambient temperature, ensuring that product manufacturing accuracy is not affected by the thermal expansion and contraction of the equipment caused by ambient temperature fluctuations. This avoids repeated physical temperature adjustments by air conditioning, thus saving a significant amount of energy.

[0106] The preferred method for intelligent electrical control in engine production workshops, as described in this invention, effectively solves the following problem:

[0107] 1. Due to the large number of equipment in the workshop, increasing the number of temperature sensors would lead to increased costs;

[0108] 2. Due to the physical laws of heat conduction, the process of detecting the ambient temperature through a temperature sensor, sending temperature control commands to the air conditioning control system, and then cooling or heating the workshop environment through air conditioning inevitably leads to a certain lag in temperature control.

[0109] The method for intelligent control of electrical energy in engine production workshops using the present invention enables the monitoring and prediction of electrical energy and temperature data in engine plants, and makes the electrical energy management and control process in engine plants more scientific and effective.

[0110] Those skilled in the art will understand that information, signals, and data can be represented using any of a variety of different techniques and arts. For example, the data, instructions, commands, information, signals, bits, symbols, and chips described throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0111] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this hardware-software interchangeability, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this application.

[0112] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0113] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0114] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0115] The above embodiments are provided for those skilled in the art to implement or use this application. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the spirit of this application. Therefore, the scope of protection of this application is not limited to the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. A method for intelligent control of electrical energy in an engine production workshop, characterized in that, Including the following steps: S1) Obtain the temperature variation model of the XYZ axis machining state of the machining equipment under different working conditions; S2) Obtain the heat treatment equipment's radiation temperature variation model under different working conditions; S3) Based on the machining state temperature change model of the XYZ axis of the machining equipment and the radiation temperature change model of the machining state of the heat treatment equipment, a comprehensive temperature prediction model of the XYZ axis of the machining equipment is established. The comprehensive temperature prediction model of the XYZ axis of the machining equipment is used to obtain the XYZ axis temperature of the machining equipment under different working conditions at the standard field temperature. S4) Obtain the XYZ axis temperature compensation parameters of the processing equipment at different temperatures. The XYZ axis temperature compensation parameters are obtained by measuring the correspondence between the temperature and thermal deformation of the XYZ axis of the processing equipment. S5) Use the comprehensive temperature prediction model of the XYZ axis of the processing equipment and the temperature compensation parameters of the XYZ axis for XYZ axis accuracy compensation during processing. Wherein, step S1) further includes: S11) Acquire the processing equipment's different status signals output under different working conditions; S12) Measure the temperature change process of the XYZ axis of the processing equipment when the processing equipment outputs different state signals and establish a temperature change model of the XYZ axis of the processing equipment in the processing state. Step S2) further includes: S21) Obtain the output signals of different states of the heat treatment equipment under different working conditions; S22) Measure the radiant temperature change of the heat treatment equipment under different working conditions based on the output signals of the heat treatment equipment under different state conditions, and establish a model of the radiant temperature change of the heat treatment equipment under processing conditions. Step S5) further includes: S51) Acquire the different status signals of the processing equipment under different working conditions; S52) Determine the state of the processing equipment based on the output of different state signals of the processing equipment, and obtain the XYZ axis temperature of the processing equipment using the comprehensive temperature prediction model of the XYZ axis of the processing equipment. S53) Based on the XYZ axis temperature of the processing equipment, the accuracy of the XYZ axis of the processing equipment is compensated during processing using the XYZ axis temperature compensation parameters; The area around the heat treatment equipment is divided into different ranges according to gradients, and target points are established in different ranges. The time of the change in the operating state of the heat treatment equipment is used as the horizontal axis, the temperature is used as the vertical axis, and the state change signal is used as the origin of the coordinate system to obtain a curve model of the influence of the change in the state signal of the heat treatment equipment on the external temperature of the processing equipment within the range of the target point. The temperature change of the processing equipment itself, as represented by the XYZ axis processing state temperature change model, is superimposed with the external temperature change, as represented by the heat treatment equipment processing state radiation temperature change model, to obtain a comprehensive temperature prediction of the processing equipment. During production, the system collects operating status signals from the processing equipment and the heat treatment equipment. Based on the XYZ axis comprehensive temperature prediction model of the processing equipment, it predicts the comprehensive temperature of the processing equipment within different ranges and obtains the corresponding XYZ axis temperature compensation parameters for the processing equipment based on the predicted comprehensive temperature. During normal production, the system operates and monitors the working status of the processing equipment and the heat treatment equipment in real time. When the working status of the processing equipment or the heat treatment equipment changes, it outputs a status signal corresponding to the changed working status. Based on the XYZ axis comprehensive temperature prediction model of the processing equipment, it predicts the comprehensive temperature of the processing equipment within different ranges, obtains the corresponding XYZ axis temperature compensation parameters for the processing equipment based on the predicted comprehensive temperature, and sends the XYZ axis temperature compensation parameters to the corresponding processing equipment for compensation.

2. The method for intelligent electrical energy control in an engine production workshop according to claim 1, characterized in that, The comprehensive temperature prediction model for the XYZ axes of the processing equipment is obtained through machine learning based on the temperature change model of the XYZ axes of the processing equipment and the radiation temperature change model of the heat treatment equipment.

3. The method for intelligent electrical energy control in an engine production workshop according to claim 1, characterized in that, The different operating states of the heat treatment equipment include start-up, quenching, tempering, standby, and shutdown.

4. The method for intelligent electrical energy control in an engine production workshop according to claim 1, characterized in that, The different working states of the processing equipment include start-up, normal operation, heavy-load roughing, light-load finishing, and stop.

5. The method for intelligent electrical energy control in an engine production workshop according to claim 1, characterized in that, Step S4) further includes: S41) Determine the standard length of the XYZ axis of the machining equipment at the standard field temperature; S42) Obtain the percentage of thermal deformation of the XYZ axis at different temperatures. The percentage of thermal deformation of the XYZ axis at different temperatures is obtained by measuring the length of the XYZ axis of the processing equipment and the standard length of the XYZ axis in the actual working temperature range. The actual working temperature range is obtained based on the lowest and highest temperatures when the processing equipment is working. S43) Convert the percentage of thermal deformation of the XYZ axis at different temperatures into XYZ axis temperature compensation parameters.

6. The method for intelligent electrical energy control in an engine production workshop according to claim 5, characterized in that, The actual operating temperature range is 14℃~26℃.

7. The method for intelligent control of electrical energy in an engine production workshop according to claim 1, characterized in that, The standard on-site temperature is 20℃.

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