CN117970889A - Intelligent control method for electric energy of engine production workshop - Google Patents
Intelligent control method for electric energy of engine production workshop Download PDFInfo
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Abstract
The application discloses a method for intelligently controlling electric energy of an engine production workshop, which comprises the following steps: acquiring a temperature change model of the processing state of the XYZ shaft of the processing equipment under different working states; acquiring a radiation temperature change model of the heat treatment equipment in the processing state of the heat treatment equipment under different working states; establishing an XYZ-axis comprehensive temperature prediction model of the processing equipment according to the XYZ-axis processing state temperature change model of the processing equipment and the radiation temperature change model of the processing state of the heat treatment equipment, wherein the XYZ-axis comprehensive temperature prediction model of the processing equipment is used for acquiring the XYZ-axis temperatures of the processing equipment in different working states at standard site temperatures; acquiring temperature compensation parameters of XYZ axes of the processing equipment at different temperatures, wherein the temperature compensation parameters of the XYZ axes are obtained according to the corresponding relation between the temperature and the thermal deformation of the XYZ axes of the processing equipment; and the XYZ-axis comprehensive temperature prediction model of the processing equipment and the XYZ-axis temperature compensation parameters are used for XYZ-axis precision compensation during processing of the processing equipment.
Description
Technical Field
The invention relates to the field of automobile manufacturing, in particular to an intelligent control method for electric energy of an engine production workshop.
Background
The processing of the parts of the automobile power assembly is required to be completed in a processing workshop. With the improvement of the quality of the automobile, the machining precision of parts of the automobile power assembly reaches the micron level and has extremely high requirements on temperature. FIG. 1 is a schematic diagram of a prior art arrangement of equipment in an automotive powertrain process plant. As shown in fig. 1, a power train processing plant of an automobile mainly comprises processing equipment 1, heat treatment equipment 2, a temperature sensor 3 and a logistics door 4. The machining equipment preferably uses machining equipment controlled by a PLC program to perform machining modes such as milling, boring and drilling on the machined part. The cutting heat, grinding heat, hydraulic stations, motor operation of the machining device 1 all emit a large amount of heat. The heat treatment apparatus 2 mainly includes a quenching and tempering apparatus for parts such as a crankshaft, and generates a large amount of heat when the apparatus is operated. Opening and closing the shop floor door 4 also allows a lot of outdoor air to enter the shop resulting in a change of the ambient temperature in the shop floor. The factors that influence the processing temperature of the parts are mainly the temperature generated by the processing equipment 1 and the heat treatment equipment 2 and the workshop environment temperature. The temperature of the processing equipment 1 itself comprises the cutting heat of a cutter, the friction heat of a friction pair such as a main shaft, a guide rail, a screw rod and the like. The ambient temperature of the plant is the effect of the ambient temperature of the equipment on the processing equipment. The thermal expansion and contraction of the apparatus caused by the temperature causes deformation of the XYZ-direction movement axis at the time of processing of the processing apparatus 1 and affects the processing accuracy of the product. Therefore, in the related art, the temperature sensor 3 and the air conditioner are used to monitor and adjust the temperature of the plant. However, if the temperature of the workshop needs to be accurately monitored and adjusted, the temperature sensors 3 corresponding to the processing equipment one by one need to be arranged, and there are problems of low control accuracy and delayed temperature response. Because of the high environmental temperature requirements of process plants, air conditioning requires continuous operation and consumes a significant amount of electrical energy to maintain the ambient temperature within the plant.
Disclosure of Invention
In order to solve the above problems, an object of the present application is to provide a method for intelligent control of electric energy in an engine production plant, which can save electric energy and increase machining accuracy
The embodiment of the application provides a method for intelligently controlling electric energy of an engine production workshop, which is characterized by comprising the following steps:
S1) acquiring a temperature change model of the processing state of the XYZ axes of the processing equipment under different working states;
s2) acquiring a radiation temperature change model of the heat treatment equipment in the processing state of the heat treatment equipment under different working states;
S3) establishing an XYZ-axis comprehensive temperature prediction model of the processing equipment according to the XYZ-axis processing state temperature change model of the processing equipment and the radiation temperature change model of the processing state of the heat treatment equipment, wherein the XYZ-axis comprehensive temperature prediction model of the processing equipment is used for acquiring the XYZ-axis temperatures of the processing equipment in different working states at standard site temperatures;
S4) acquiring temperature compensation parameters of XYZ axes of the processing equipment at different temperatures, wherein the temperature compensation parameters of the XYZ axes are obtained according to the corresponding relation between the temperature and the thermal deformation of the XYZ axes of the processing equipment;
s5) using the XYZ-axis comprehensive temperature prediction model of the processing equipment and the XYZ-axis temperature compensation parameters to compensate the XYZ-axis precision during processing of the processing equipment.
Further, the method for intelligently controlling the electric energy of the engine production workshop, wherein the step S1) further comprises the following steps:
s11) acquiring signals of different states of the processing equipment under different working states of the processing equipment and outputting the signals;
s12) measuring a temperature change process of XYZ axes of the processing device at the time of outputting signals of different states of the processing device and establishing a processing state temperature change model of XYZ axes of the processing device.
Further, the method for intelligently controlling the electric energy of the engine production workshop, wherein the step S2) further comprises the following steps:
s21) acquiring signals of different states of the heat treatment equipment under different working states of the heat treatment equipment and outputting the signals;
S22) measuring the radiation temperature change of the heat treatment equipment in different working states according to the signal output of the different states of the heat treatment equipment and establishing a radiation temperature change model of the processing state of the heat treatment equipment.
Further, according to the method for intelligently controlling the electric energy of the engine production workshop, the comprehensive temperature prediction model of the XYZ axes of the processing equipment is obtained through machine learning according to the temperature change model of the processing state of the XYZ axes of the processing equipment and the radiation temperature change model of the processing state of the heat treatment equipment.
Further, according to the intelligent control method for the electric energy of the engine production workshop, different working states of the heat treatment equipment comprise starting, quenching, tempering, standby and stopping.
Further, according to the method for intelligently controlling the electric energy of the engine production workshop, different working states of the processing equipment comprise a starting state, a normal operation state, a heavy-load rough processing state, a light-load finish processing state and a stop state.
Further, the method for intelligently controlling the electric energy of the engine production workshop, wherein the step S4) further comprises the following steps:
S41) measuring XYZ-axis standard lengths of XYZ-axes of the processing apparatus at the standard site temperature;
S42) obtaining thermal deformation percentages of XYZ axes with different temperatures, wherein the thermal deformation percentages of the XYZ axes with different temperatures are obtained through measurement of the length of XYZ axes of a processing device in an actual working temperature range of the processing device and the standard length of the XYZ axes, and the actual working temperature range is obtained according to the minimum temperature and the maximum temperature of the processing device during working;
S43) converting the different temperature XYZ-axis thermal deformation percentages into XYZ-axis temperature compensation parameters.
Further, according to the method for intelligently controlling the electric energy of the engine production workshop, the actual working temperature range is 14-26 ℃.
Further, according to the method for intelligently controlling the electric energy of the engine production workshop, the standard site temperature is 20 ℃.
Further, the method for intelligently controlling the electric energy of the engine production workshop, wherein the step S5) further comprises the following steps:
s51) acquiring signals of different states of the processing equipment under different working states of the processing equipment and outputting the signals;
S52) judging the state of the processing equipment according to the signal output of the different states of the processing equipment and acquiring the temperature of the XYZ axes of the processing equipment by using the XYZ axis comprehensive temperature prediction model of the processing equipment;
s53) compensating the precision of the XYZ axes of the processing apparatus at the time of processing by the XYZ axis temperature compensation parameter according to the XYZ axis temperature of the processing apparatus.
The technical scheme provided by the embodiment of the application has the following advantages:
1. the XYZ-axis comprehensive temperature prediction model of the processing equipment is established, so that the temperature of the processing equipment can be effectively predicted;
2. the temperature compensation parameters of the XYZ axes are adopted, so that the processing precision error caused by the temperature influence during the processing of equipment is effectively realized, the precision of the XYZ axes during the processing is increased, the repeated physical temperature adjustment of an air conditioner is avoided, and a large amount of electric energy is saved;
3. the arrangement of the sensors is effectively reduced, and the cost is saved.
Drawings
FIG. 1 is a schematic diagram of a prior art arrangement of equipment in an automotive powertrain process plant;
FIG. 2 is a flow chart of a method for intelligent control of electrical energy in an engine production plant, which is preferred in an embodiment of the present invention;
FIGS. 3a and 3b are schematic diagrams of curve models of the influence of the change of the status signal of the processing equipment on the temperature of the processing equipment according to the preferred embodiment of the invention;
FIG. 4 is a schematic diagram showing the gradient division of the temperature of the heat treatment equipment according to different ranges according to the preferred embodiment of the invention;
fig. 5 is a schematic diagram of XYZ-axis temperature compensation parameter acquisition according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application become more apparent, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. 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, but not all, embodiments of the application. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Furthermore, unless specifically stated and limited otherwise, the terms "mounted," "connected," and the like in the description of the present application are used in a broad sense, and for example, the connection may be a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can also be communicated with the inside of two elements, and the specific meaning of the two elements can be understood by a person skilled in the art according to specific situations.
A schematic diagram of the equipment layout of a car power assembly processing workshop in the prior art is shown in fig. 1. The power assembly processing workshop of the automobile mainly comprises processing equipment 1, heat treatment equipment 2, a temperature sensor 3 and a logistics door 4.
FIG. 2 is a flow chart of a method for intelligent control of electric energy in an engine production plant, which is preferred in an embodiment of the invention. As shown in fig. 2, the method for intelligently controlling the electric energy of the engine production workshop comprises the following steps:
S1) acquiring a temperature change model of the processing state of the XYZ axes of the processing equipment under different working states;
s2) acquiring a radiation temperature change model of the heat treatment equipment in the processing state of the heat treatment equipment under different working states;
S3) establishing an XYZ-axis comprehensive temperature prediction model of the processing equipment according to the XYZ-axis processing state temperature change model of the processing equipment and the radiation temperature change model of the processing state of the heat treatment equipment, wherein the XYZ-axis comprehensive temperature prediction model of the processing equipment is used for acquiring the XYZ-axis temperatures of the processing equipment in different working states at standard site temperatures;
S4) acquiring temperature compensation parameters of XYZ axes of the processing equipment at different temperatures, wherein the temperature compensation parameters of the XYZ axes are obtained according to the corresponding relation between the temperature and the thermal deformation of the XYZ axes of the processing equipment;
S5) using the XYZ-axis comprehensive temperature prediction model of the processing equipment and the XYZ-axis temperature compensation parameters to compensate the XYZ-axis precision during processing of the processing equipment.
The preferred method for intelligently controlling the electric energy of the engine production plant according to the invention is further described in detail below with reference to the accompanying drawings and examples.
Step S1) obtaining a temperature change model of the working state of the XYZ axes of the working equipment under different working states.
Preferably, step S1) further comprises:
s11) acquiring signals of different states of the processing equipment under different working states of the processing equipment and outputting the signals;
S12) measuring a temperature change process of XYZ axes of the processing apparatus at the time of outputting signals of different states of the processing apparatus and establishing a processing state temperature change model of XYZ axes of the processing apparatus.
Preferably, the different operating conditions of the processing apparatus include start-up, normal operation, heavy duty rough machining, light duty finish machining and stop conditions.
In this embodiment, the different status signals of the processing apparatus are preferably output as PLC output status signals.
In this step, the preferred specific steps in this embodiment are as follows:
Defining a processing equipment state variable:
Corresponding PLC output state signals are set in the starting-up, normal operation, heavy-load rough machining, light-load finish machining and stopping states of the processing equipment, and the signals are acquired to obtain the perception of different states of the processing equipment.
Specifically, the operating state variable signals of the device are set and defined in the PLC program of the device. For example, the processing equipment is divided into a heavy-load rough processing corresponding equipment PLC output port M100.0 which is set to 0 if not, a light-load finish processing corresponding equipment PLC output port M100.1 which is set to 1 if not, and a shutdown state corresponding equipment PLC output port M100.2 which is set to 1 if not. And outputting corresponding state signals when the equipment performs different process states according to the program. Therefore, the operation state of the processing equipment can be judged according to the collected output state signals.
Obtaining the relation of the influence of different running states of the processing equipment on the temperature of the processing equipment:
the relation of different process states corresponding to temperature change processes is obtained through starting up, normal operation, heavy-duty rough machining, light-duty finish machining, process state acquisition in a stop state and measurement of temperature change processes of XYZ axes of the processing equipment.
The preferred standard in-situ temperature for this example is 20 ℃.
Specifically, under the standard condition that the standard site temperature is 20 ℃, a certain processing device is independently operated, in the embodiment, a handheld infrared temperature test gun is preferably adopted, or temperature sensors are arranged at corresponding positions of the processing device to measure and record the temperature change processes of the XYZ axes in the starting state, the normal operation state, the heavy-load rough machining state, the light-load finish machining state and the stop state, so that the temperature change relation is obtained.
Establishing a model of the influence of the change of the state signal of the processing equipment on the temperature of the processing equipment:
Fig. 3a and 3b are schematic diagrams of curve models of the influence of the change of the status signal of the processing equipment on the temperature of the processing equipment according to the preferred embodiment of the invention. As shown in fig. 3a and 3b, the horizontal axis represents the state change time of the processing equipment and the vertical axis represents the temperature. The state change signal can be obtained as a coordinate origin, and the state change of the processing equipment has a curve model of the influence of the state change signal of the processing equipment on the temperature of the XYZ axes of the processing equipment. When the machining apparatus is in a low power state signal output of 1 (for example, on, normal operation, and stop state) or when the high power state signal output of 1 (for example, heavy duty rough machining and light duty finish machining), there are a temperature rise phase and a temperature balance phase in which the temperature of the XYZ axes of the machining apparatus changes with time, and when the machining apparatus is in a low power state signal output of 0 or when the high power state signal output of 0, the temperature of the XYZ axes of the machining apparatus changes with time, the temperature drops. Data of temperature rise and fall of XYZ axes of the processing apparatus are recorded, thereby establishing a processing state temperature change model of XYZ axes of the processing apparatus.
Step S2) obtaining a radiation temperature change model of the heat treatment equipment in the processing state of the heat treatment equipment under different working states.
Preferably, step S2) further comprises:
s21) acquiring signals of different states of the heat treatment equipment under different working states of the heat treatment equipment and outputting the signals;
S22) measuring the radiation temperature change of the heat treatment equipment in different working states according to the signal output of the different states of the heat treatment equipment and establishing a radiation temperature change model of the processing state of the heat treatment equipment.
Preferably, the different operating states of the heat treatment apparatus include start-up, quench, tempering, standby and shut-down.
In this embodiment, the different status signals of the heat treatment apparatus are preferably output as PLC output status signals.
In this step, the preferred specific steps in this embodiment are as follows:
Defining a heat treatment apparatus state variable:
Corresponding PLC output state signals are set in different process states of starting, quenching, tempering, standby and stopping of the heat treatment equipment, and sensing of different states of the heat treatment equipment is obtained through signal acquisition.
Specifically, the operating state variable signals of the device are set and defined in the PLC program of the device. For example, the heat treatment equipment is divided into a quenching high-load heat heating corresponding equipment PLC output port M101.0 which is set to 1 otherwise, a tempering low-load heat treatment corresponding equipment PLC output port M101.1 which is set to 1 otherwise, a shutdown state PLC output port M101.2 which is set to 1 otherwise, and a shutdown state PLC output port M101.2 which is set to 0 otherwise. And outputting corresponding state signals when the equipment performs different process states according to the program. Therefore, the operation state of the heat treatment equipment can be judged according to the collected output state signals.
Obtaining the relation between different running states of the heat treatment equipment and the external temperature change of the processing equipment:
FIG. 4 is a schematic diagram showing the gradient division of the temperature of the heat treatment apparatus according to the preferred embodiment of the present invention. As shown in fig. 4, the temperature change process of the XYZ axes of the processing equipment is measured under the influence of the surrounding temperature distribution gradient through the process of changing the different process states of starting, quenching, tempering, standby and stopping of the heat treatment equipment, so that the relation of the different operation states of the heat treatment equipment to the external temperature change of the processing equipment is obtained.
Specifically, under the standard condition that the standard site temperature is 20 ℃, the heat treatment equipment is independently operated, and the temperature sensors arranged in workshops are used for measuring the relationship of influences on the temperature gradient distribution of the environment of the surrounding processing equipment under different process states of starting, quenching, tempering, standby and stopping the heat treatment equipment.
Establishing a model of the influence of the state signal change of the heat treatment equipment on the external temperature of the processing equipment:
The heat treatment equipment is divided into different ranges around the periphery according to gradients. Target points are established within different ranges (e.g. temperature is measured over ranges 1,2, 3,4 … … in the figure). The time of change of the operating state of the heat treatment apparatus is taken as the horizontal axis and the temperature is taken as the vertical axis. The state change signal is taken as a coordinate origin to obtain a curve model of the influence of the state signal change of the heat treatment equipment on the external temperature of the processing equipment in the range of the target point. The method for establishing the radiation temperature change model of the processing state of the heat treatment equipment by collecting data is similar to the method for establishing the temperature change model of the processing state of the XYZ axes of the processing equipment by the processing equipment, so that the description is omitted.
Step S3) a machining device XYZ axis comprehensive temperature prediction model is established according to the machining device XYZ axis machining state temperature change model and the heat treatment device machining state radiation temperature change model, and the machining device XYZ axis comprehensive temperature prediction model is used for acquiring the XYZ axis temperatures of the machining device in different working states at standard site temperatures.
Preferably, the machining apparatus XYZ-axis integrated temperature prediction model is obtained by machine learning from a machining apparatus XYZ-axis machining state temperature change model and a heat treatment apparatus machining state radiation temperature change model.
In this step, the preferred specific steps in this embodiment are as follows:
Superposing a temperature change model of the processing equipment and an external temperature change model to obtain comprehensive temperature prediction of the processing equipment:
Specifically, a model of the effect of temperature on the processing equipment is built. And (5) a processing state temperature change model of the XYZ axes of the processing equipment and a radiation temperature change model of the processing state of the heat treatment equipment. The temperature change model of the processing equipment is obtained through machine learning training and is overlapped with an external temperature change model to obtain an XYZ-axis comprehensive temperature prediction model of the processing equipment, and when different running states of different equipment occur, the temperature of the processing equipment in different ranges can be predicted.
Step S4) acquiring temperature compensation parameters of XYZ axes of the processing equipment at different temperatures, wherein the temperature compensation parameters of the XYZ axes are obtained according to the corresponding relation between the temperature and the thermal deformation of the XYZ axes of the processing equipment
Preferably, step S4) further comprises:
s41) measuring XYZ-axis standard lengths of XYZ-axes of the processing apparatus at standard field temperatures;
S42) obtaining the thermal deformation percentages of XYZ axes with different temperatures, wherein the thermal deformation percentages of the XYZ axes with different temperatures are obtained through measurement of the length of XYZ axes of the processing equipment and the standard length of the XYZ axes in an actual working temperature interval of the processing equipment, and the actual working temperature interval is obtained according to the lowest temperature and the highest temperature of the processing equipment during working;
S43) converting the XYZ-axis thermal deformation percentages at different temperatures into XYZ-axis temperature compensation parameters.
Preferably, the actual working temperature range is 14-26 ℃.
In this step, the preferred specific steps in this embodiment are as follows:
obtaining temperature compensation parameters of XYZ axes of different processing equipment:
by measuring the correspondence relationship with the thermal deformation at the temperature of the XYZ axes of the processing apparatus, the temperature compensation parameters corresponding to the XYZ axes of the different processing apparatuses are obtained.
Fig. 5 is a schematic diagram of XYZ-axis temperature compensation parameter acquisition according to a preferred embodiment of the present invention. As shown in fig. 5, the processing apparatus includes a processing apparatus bed 11 and a processing apparatus headstock 12, a processed product 13 is placed on the processing apparatus bed 11, the processing apparatus headstock 12 is operated to a certain fixed position a by a program at 20 ℃, a dial indicator 14 is adsorbed on the bed by a magnetic gauge stand, and a side head of the dial indicator 14 contacts the headstock and is cleared, thereby obtaining XYZ-axis standard length. The process equipment was repeatedly operated at a temperature of 14 ℃, 15 ℃, 16 ℃, 17 ℃, 18 ℃,19 ℃, 20 ℃, 21 ℃, 22 ℃, 23 ℃, 24 ℃, 25 ℃, 26 ℃ to a certain fixed position A by program control, respectively. The readings of the dial 14 at the corresponding temperatures are recorded. And obtaining the corresponding thermal expansion and contraction change condition of the device XYZ axes at the corresponding temperature through the dial indicator 14. Because the processing equipment itself has a gradual heat transfer and gradual expansion and contraction rate as the ambient temperature changes. Corresponding heat expansion and cold contraction data can be obtained on the XYZ axes of the processing equipment in different heat transduction directions and at different temperatures in the surrounding environment of the processing equipment. It should be noted that the percentage of thermal deformation of the X-axis of the processing apparatus at different temperatures is shown only schematically in fig. 4, and the Y-axis and Z-axis test methods can be obtained by referring to the X-axis method. After the data of the thermal deformation percentages of the XYZ axes at different temperatures are acquired, the data are converted into XYZ axis temperature compensation parameters readable by the processing apparatus by conversion such as a computer language. The present embodiment preferably converts to a program of XYZ-axis temperature compensation parameters readable by the PLC.
Optionally, the compensation accuracy of the processing equipment can be detected after the temperature compensation parameters of the XYZ axes, and the steps are as follows:
the machining device XYZ axis repetition accuracy of the superimposed temperature compensation parameters of the machining device at different temperature variations was tested at the end position of the machined product 13 using the dial gauge 14.
Specifically, the machining equipment headstock 12 is operated to a certain fixed position a by a program at 20 ℃, the magnetic gauge stand of the dial indicator 14 is adsorbed on a bed, and the side head of the dial indicator 14 contacts the machining equipment headstock 12 and is cleared. The processing equipment was repeatedly operated to a certain fixed position A by program control at temperatures of 14 ℃, 15 ℃, 16 ℃, 17 ℃, 18 ℃, 19 ℃, 20 ℃, 21 ℃, 22 ℃, 23 ℃, 24 ℃, 25 ℃, 26 ℃ while superimposing the XYZ-axis temperature compensation parameters. Record whether the readings of the dial indicator are any zero. And if the temperature is not zero, the temperature compensation parameters of the XYZ axes need to be acquired again.
And S5) using the XYZ-axis comprehensive temperature prediction model of the processing equipment and the XYZ-axis temperature compensation parameters for XYZ-axis precision compensation during processing of the processing equipment.
Preferably, step S5) further comprises:
s51) acquiring signals of different states of the processing equipment under different working states of the processing equipment and outputting the signals;
s52) judging the state of the processing equipment according to the signal output of the different states of the processing equipment and acquiring the temperature of the XYZ axes of the processing equipment by using an XYZ axis comprehensive temperature prediction model of the processing equipment;
s53) compensating the accuracy of the XYZ axes of the processing apparatus at the time of processing by the XYZ axis temperature compensation parameter according to the XYZ axis temperature of the processing apparatus.
In this step, the preferred specific steps in this embodiment are as follows:
Generating XYZ-axis temperature compensation parameters of the processing equipment in different ranges according to the equipment state signals:
And acquiring operation state signals of the processing equipment and operation state signals of the heat treatment equipment in production, and predicting the comprehensive temperatures of the processing equipment in different ranges according to the XYZ-axis comprehensive temperature prediction model of the processing equipment. And obtaining the temperature compensation parameters of the XYZ axes of the corresponding processing equipment from the predicted comprehensive temperature.
Specifically, the system is operated at normal production. And monitoring the running states of all the equipment in real time, outputting corresponding state signals when the equipment states change (such as shutdown), and predicting the comprehensive temperatures of the processing equipment in different ranges according to the XYZ-axis comprehensive temperature prediction model of the processing equipment. And sending the temperature compensation parameters corresponding to the XYZ axes of the processing equipment in different ranges to the equipment for compensation. The method and the device have the advantages that the influence on the environmental temperature of a workshop is avoided when the state of the device changes, so that the manufacturing accuracy of the product is ensured not to be influenced by expansion caused by heat and contraction caused by fluctuation of the environmental temperature. The repeated physical temperature adjustment of the air conditioner is avoided, so that a large amount of electric energy is saved.
The method for intelligently controlling the electric energy of the engine production workshop effectively solves the problems that:
1. The number of workshop equipment is large, so that the cost is increased due to the fact that the number of temperature sensors is increased;
2. Due to the physical rule of heat conduction, the temperature sensor detects the ambient temperature, then the temperature control instruction is sent to the air conditioner control system, and the temperature control is delayed to a certain extent due to the fact that the air conditioner cools down or heats the workshop environment.
The intelligent control method for the electric energy of the engine production workshop realizes the functions of monitoring and predicting the electric energy and temperature data of the engine factory, and ensures that the electric energy management and control process of the engine factory is more scientific and effective.
Those of skill in the art would understand that information, signals, and data may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The various illustrative logical modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with 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. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic 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 that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a 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 medium. Disk (disk) and disc (disk) as used herein include Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disk) usually reproduce data magnetically, while discs (disk) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The embodiments described above are intended to provide those skilled in the art with a full range of modifications and variations to the embodiments described above without departing from the spirit of the application, and therefore the scope of the application is not limited to the embodiments described above, but is to be accorded the broadest scope consistent with the novel features set forth in the claims.
Claims (10)
1. The intelligent control method for the electric energy of the engine production workshop is characterized by comprising the following steps of:
S1) acquiring a temperature change model of the processing state of the XYZ axes of the processing equipment under different working states;
s2) acquiring a radiation temperature change model of the heat treatment equipment in the processing state of the heat treatment equipment under different working states;
S3) establishing an XYZ-axis comprehensive temperature prediction model of the processing equipment according to the XYZ-axis processing state temperature change model of the processing equipment and the radiation temperature change model of the processing state of the heat treatment equipment, wherein the XYZ-axis comprehensive temperature prediction model of the processing equipment is used for acquiring the XYZ-axis temperatures of the processing equipment in different working states at standard site temperatures;
S4) acquiring temperature compensation parameters of XYZ axes of the processing equipment at different temperatures, wherein the temperature compensation parameters of the XYZ axes are obtained according to the corresponding relation between the temperature and the thermal deformation of the XYZ axes of the processing equipment;
s5) using the XYZ-axis comprehensive temperature prediction model of the processing equipment and the XYZ-axis temperature compensation parameters to compensate the XYZ-axis precision during processing of the processing equipment.
2. The method for intelligent control of electric energy in an engine production plant according to claim 1, wherein said step S1) further comprises:
s11) acquiring signals of different states of the processing equipment under different working states of the processing equipment and outputting the signals;
s12) measuring a temperature change process of XYZ axes of the processing device at the time of outputting signals of different states of the processing device and establishing a processing state temperature change model of XYZ axes of the processing device.
3. The method for intelligent control of electric energy in an engine production plant according to claim 1, wherein said step S2) further comprises:
s21) acquiring signals of different states of the heat treatment equipment under different working states of the heat treatment equipment and outputting the signals;
S22) measuring the radiation temperature change of the heat treatment equipment in different working states according to the signal output of the different states of the heat treatment equipment and establishing a radiation temperature change model of the processing state of the heat treatment equipment.
4. A method for intelligent control of electric energy in an engine production plant according to any one of claims 1 to 3, wherein the machining apparatus XYZ-axis integrated temperature prediction model is obtained by machine learning based on the machining apparatus XYZ-axis machining state temperature change model and the heat treatment apparatus machining state radiation temperature change model.
5. The method for intelligent control of electrical energy in an engine production plant of claim 1, wherein the different operating conditions of the heat treatment apparatus include start-up, quench, tempering, standby and shut-down.
6. The method of claim 1, wherein the different operating conditions of the processing equipment include start-up, normal operation, heavy duty rough machining, light duty finish machining, and stop conditions.
7. The method for intelligent control of electric energy in an engine production plant according to claim 1, wherein said step S4) further comprises:
S41) measuring XYZ-axis standard lengths of XYZ-axes of the processing apparatus at the standard site temperature;
S42) obtaining thermal deformation percentages of XYZ axes with different temperatures, wherein the thermal deformation percentages of the XYZ axes with different temperatures are obtained through measurement of the length of XYZ axes of a processing device in an actual working temperature range of the processing device and the standard length of the XYZ axes, and the actual working temperature range is obtained according to the minimum temperature and the maximum temperature of the processing device during working;
S43) converting the different temperature XYZ-axis thermal deformation percentages into XYZ-axis temperature compensation parameters.
8. The method for intelligently controlling electric energy in an engine production plant according to claim 7, wherein the actual working temperature range is 14-26 ℃.
9. The method for intelligent control of electrical energy in an engine production plant of claim 1, wherein the standard in-situ temperature is 20 ℃.
10. The method for intelligent control of electric energy in an engine production plant according to claim 1, wherein said step S5) further comprises:
s51) acquiring signals of different states of the processing equipment under different working states of the processing equipment and outputting the signals;
S52) judging the state of the processing equipment according to the signal output of the different states of the processing equipment and acquiring the temperature of the XYZ axes of the processing equipment by using the XYZ axis comprehensive temperature prediction model of the processing equipment;
s53) compensating the precision of the XYZ axes of the processing apparatus at the time of processing by the XYZ axis temperature compensation parameter according to the XYZ axis temperature of the processing apparatus.
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