Intelligent mixer control method based on material properties of emulsified asphalt mixture

Abstract

The invention discloses a mixer intelligent control method based on emulsified asphalt mixture material properties, which is characterized in that a hydraulic motor for driving a mixer is provided with a sensor assembly, and the sensor assembly comprises a torque sensor, a rotating speed sensor, a temperature sensor, a particle size sensor and a selective resistance sensor; a sensor collects signals, a convolutional neural network carries out data processing, feature extraction, deep learning generation of an adversarial network, online abnormity monitoring and fault diagnosis and prediction to form global features, the global features are sent to a controller and a human-computer interface for intervention, and the operation parameters of the mixer are adjusted in real time. According to the invention, the problems of inconsistency and easy instability of power response characteristics at different working points are solved, and the power control precision is improved; according to the invention, automatic power adjustment of the mixer is ensured, and the road mixing and paving construction quality is greatly improved; the problem that the emulsified asphalt mixture is easy to block in the mixing process is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control of road paving equipment, and in particular relates to an intelligent control method for a mixer based on the material properties of an emulsified asphalt mixture. Background Art

[0002] The mixer is an important part of the paving equipment. It plays a role in stirring the emulsified asphalt mixture, directly affecting the mixing quality of the emulsified asphalt mixture, affecting the work quality and productivity during construction. Because the emulsified asphalt mixture is a combination of organic binder and hydraulic material, the use of ordinary mixers to produce emulsified asphalt mixtures is prone to demulsification, segregation due to excessive temperature, uneven mixing quality, high viscosity mixer clogging, resulting in construction quality that does not meet the standards. The cleaning of emulsified asphalt by the mixer is difficult, labor-intensive and time-consuming, and other problems need to be solved urgently.

[0003] By analyzing the material properties of emulsified asphalt mixture, optimizing the design of the mixer that meets the characteristics of emulsified asphalt mixture, and monitoring the working status of the mixer, the real-time torque data and speed data of the mixer at time N-1 are collected, and the power of the mixer at time N is predicted using the established neural network, the dynamic parameters of the mixer are obtained in real time, and the mixer feeding working parameters are adjusted. Through the mixer power prediction, the abnormal signal of the mixer power is obtained in advance, and the mixer operating parameters are intervened and controlled in advance to improve the mixing efficiency and mixing uniformity, and the mixing, scraping, and transmission functions of the emulsified asphalt mixture are realized, and the whole mixing process is not blocked. Summary of the invention

[0004] The technical problem solved by the present invention is to provide an intelligent control method for a mixer based on the material properties of an emulsified asphalt mixture. The method innovatively designs a control system based on an emulsified asphalt mixture mixer to effectively overcome the problems of uneven mixing of the mixer and easy clogging of the mixer.

[0005] The present invention adopts the following technical scheme to solve the above technical problems: an intelligent control method for a mixer based on the material properties of emulsified asphalt mixture, wherein the mixer comprises a mixing device body, a feeding device arranged above the mixing device body, a power device arranged on the side of the mixing device body and a discharge port arranged below the mixing device body, wherein a rotating device and a mixing chamber are arranged on the mixing shaft with mixing blades arranged in a double helix cross pattern, the power device comprises a mixing motor and a mixing reduction motor, the active end of the mixing shaft is coaxially connected with the end of the power shaft of the mixing motor or the mixing reduction motor, and the rotating support of the mixing active shaft and the driven shaft is arranged at the center of the mixing chamber of the double mixing drum, characterized in that: a sensor assembly is arranged on the mixing motor, and the sensor assembly comprises a torque sensor and a speed sensor; the sensor collects signals, a convolutional neural network performs data processing, feature extraction, deep learning generates adversarial networks, online monitoring of abnormalities, fault diagnosis and prediction, forming global features, sending them to the controller, and the human-machine interface intervenes to adjust the mixer operation parameters in real time.

[0006] It is further defined that during the mixing construction of emulsified asphalt mixture, data information such as mixer motor torque and mixer motor speed are automatically collected through torque sensors and pressure sensors, and convolutional neural networks are used to perform data processing, feature extraction, deep learning generative adversarial networks, online monitoring of anomalies, fault diagnosis and prediction, and the formation of global features sent to the controller. The controller processes the information and establishes a statistical, analysis, processing, storage, archiving, and query information system for the data.

[0007] It is further defined that the convolutional neural network comprises an input layer, a hidden layer and an output layer, and the torque sensor collects T t Data and speed sensor collected by N t The corresponding power data P obtained from the data will be used as the input layer of the prediction neural network. After the prediction calculation of the prediction neural network, the power at time t+1 is obtained. According to the predicted power value Analyze the mixer failure events corresponding to the abnormal power values, adjust the mixer working status in advance, and control the feeding parameters of the emulsified asphalt mixture through timely feedback from the alarm module and display module to achieve uniform mixing of the emulsified asphalt mixture and no abnormal conditions in the mixer.

[0008] It is further defined that the prediction neural network structure includes a data memory unit, a gate unit and a data output unit. Before the power data enters the prediction neural network, it needs to be converted into dimensionless data and normalized. Normalization converts the power data into data between 0 and 1. The conversion formula is as follows:

[0009]

[0010] Where: Pt is the normalized power value, P is the actual power value at time t, Pa and Pi are the maximum and minimum values ​​of the predicted power;

[0011] The data memory unit collects the normalized power data P at time t-1 t-1 , and the output long-term memory unit information is recorded as c t-1 and short-term memory unit information is implicitly encoded as d t-1 , c t-1 and d t-1 It has the function of transmitting data information and transferring long and short-term memory information to the gate unit, which contains the input gate i t 、Forget gate t , control door c tr and output gate o t , use their respective functions to the current input data P t-1 , the implicit encoding of the input data at the last moment d t-1 And the internal memory unit output c t-1 After information processing, the long short-term memory unit information dt and ct of the next moment are output. Among them, the three gates use the nonlinear activation function sigmoid to control the information. The gate plays a control role, and the value range is compressed to between 0 and 1. When the value range of a certain gate is 0, the gate is in a fully closed state and cannot transmit any information. When the value range is 1, it is opposite to the 0 state, and the control gate g t The tanh function is used to correct information when it is compounded;

[0012] Input Gate i t 、Forget gate t , control door c tr and output gate o t The information output formula is as follows:

[0013] i t =sigmoid(γ ai ×P t +γ di ×d t-1 +b i )

[0014] f t =sigmoid(γ af ×P t +γ df ×d t-1 +b f )

[0015] c tr =tanh(γ ag ×P t +γ dg ×dt-1 +b g )

[0016] o t =sigmoid(γ ao ×P t +γ do ×d t-1 +b o )

[0017] Where: γ ai , γ di , γ af , γ df , γ ag , γ dg , γ ao , γ do are weight matrices; b i , b f , b g , b o are the bias items of the corresponding gate units respectively;

[0018] Internal memory unit c t-1 , after the gate module, updated to c t.1 ;

[0019] The internal memory unit ct information output formula is:

[0020] c t =c t-1 ×f t +i t ×g t

[0021] The output formula of the implicit encoding dr of the input data is:

[0022] d t =o t ×tanh(c t )

[0023] The data output unit is connected via the input c t and d t Parameter calculation and prediction of normalized power at time t+1

[0024] The predicted normalized power Denormalize to get the predicted power The conversion formula is as follows

[0025]

[0026] Where: P max is the maximum value of the test power; P minis the maximum value of the test power; is the normalized predicted power; Denormalized predicted power.

[0027] The intelligent control method of a mixer based on the material properties of an emulsified asphalt mixture according to the present invention is characterized in that the specific steps are:

[0028] Step S1, using the parameter sharing feature of convolutional neural network, the convolutional model predicts the power at time t+1 based on the power P at time t The mixed material power is predicted one moment in advance, and the controller will predict the mixed material power The best output power P of the mixer best Compare, if you set the threshold The mixer operates normally; if the threshold is set The controller can be used to adjust parameters, send signals to control the motor speed, control the feed amount, and control the mixer operation status in real time;

[0029] Step S2, the controller transmits the predicted mixed material power to the alarm module through the communication line, that is, after the controller transmits the predicted mixed material power, it sends a signal to the indicator light, and controls the flashing of the indicator light differently in different situations, so as to achieve the purpose of intelligent control;

[0030] In step S3, the controller transmits the predicted mixed material power to the display screen through the communication line. The display screen displays the key parameters in the mixing process and monitors the operating status of the mixer in real time.

[0031] Compared with the prior art, the present invention has the following advantages and beneficial effects: the present invention solves the problem that the power response characteristics are inconsistent and easy to become unstable at different working points, and improves the power control accuracy; the present invention ensures the automatic power adjustment of the mixer, greatly improving the quality of road mixing and paving construction; and solves the problem of easy clogging during the mixing process of emulsified asphalt mixture. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a structural schematic diagram of an intelligent mixer based on emulsified asphalt mixture in the present invention;

[0033] Figure 2 It is a control hardware installation diagram of an intelligent control method based on emulsified asphalt mixture in the present invention;

[0034] Figure 3 It is a power feedback control block diagram of the intelligent mixer in the present invention;

[0035] Figure 4 It is a network block diagram of the power prediction of the intelligent mixer in the present invention.

[0036] In the figure: 1 motor, 2 reducer, 3 mixing blades, 4 bypass detection device, 5 mixing shaft, 6 double-drum mixing drum, 7 emulsified asphalt inlet, 8 water inlet, 9 stone inlet, 10 emulsified asphalt mixture outlet, 11 mixer motor torque speed sensor, 12 mixer motor torque sensor. DETAILED DESCRIPTION

[0037] The above contents of the present invention are further described in detail below through examples, but this should not be understood as the scope of the above subject matter of the present invention being limited to the following examples, and all technologies implemented based on the above contents of the present invention belong to the scope of the present invention.

[0038] The present invention is designed to use a double-screw mixer with stirring blades arranged in a straight line, which plays the role of stirring, scraping and conveying. Figure 1 As shown in the figure: the hydraulic mixing pump in the power compartment of the paving vehicle drives the mixer hydraulic motor 1 to rotate after proportional control, and the output of the motor 1 is connected to the mixing shaft 5 through the reducer 2. The two mixing shafts 5 are gear-driven and rotate at the same speed but in opposite directions, driving the mixing blades 3 spirally arranged on the mixing shaft 5. The mixing blades 3 rotate in the double-tube mixing drum 6. The feeding of stone, emulsified asphalt and water according to the set value is turned on through the display screen. The emulsified asphalt, water and stone are fed from the emulsified asphalt inlet 7, the water inlet 8 and the stone inlet 9 respectively, and enter the sealed cavity of the double-tube mixing drum 6. After being driven by power, the double-helix mixing shaft 4 rotates, and the mixing blades 3 arranged on the mixing shaft 5 continuously move in the double-tube mixing drum 6 to stir and convey the mixture, thereby realizing the stirring, tumbling and shearing of the emulsified asphalt mixture. The 4 bypass detection device automatically detects and identifies any potential abnormal data, transforms the data, builds a model, adjusts the feeding parameters, learns useful features according to the original signal, enhances the fault learning ability, and implements real-time fault-tolerant operation. After the mixture is evenly mixed, it moves to the discharge port, moves to the emulsified asphalt mixture outlet 10 of the mixer, and enters the paving trough for road paving. The intelligent mixer based on emulsified asphalt mixture realizes the mixing, scraping and conveying functions of the emulsified asphalt mixture, meeting production needs.

[0039] Design of intelligent control method based on emulsified asphalt mixture. Provide a power control system, including Figure 2 The mixer motor torque speed sensor 10, the mixer motor torque sensor 11, Figure 3 The controller, communication line, display screen, and alarm indicator light in the Figure 3The intelligent mixer power feedback control block diagram shows that the pump transmits power to the mixer motor after being adjusted by the proportional valve. After the motor rotates, the mixer motor torque speed sensor 10 and the mixer motor torque sensor 11 automatically collect data information such as the mixer motor torque and the mixer motor speed. The communication cable sends the collected information to the controller in real time through the communication transmission technology. The controller program performs statistics, analysis, processing, storage, archiving, query and other real-time online monitoring and management information systems on the information. The controller uses the collected mixer motor torque data and mixer motor speed data through the established convolutional neural network to predict the mixer mixing power, analyze the mixing events corresponding to the abnormal power, control the mixer working state in advance, and intervene in the feeding parameters of the emulsified asphalt mixture through the timely feedback of the alarm module and the display module, so as to achieve the function of uniform mixing of the emulsified asphalt mixture and non-blocking of the mixer.

[0040] The convolutional neural network of the present invention comprises an input layer, a hidden layer and an output layer. t Data and speed sensor collected by N t The corresponding power data P obtained from the data will be used as the input layer of the prediction neural network. After the prediction calculation of the prediction neural network, the power at time t+1 is obtained. According to the predicted power value Analyze the mixer failure events corresponding to the abnormal power values, adjust the mixer working status in advance, and control the feeding parameters of the emulsified asphalt mixture through timely feedback from the alarm module and display module to achieve uniform mixing of the emulsified asphalt mixture and no abnormal conditions in the mixer.

[0041] The prediction neural network structure of the present invention comprises a data memory unit, a gate unit and a data output unit. Before the power data enters the prediction neural network, it needs to be converted into dimensionless data and normalized. Normalization converts the power data into data between 0 and 1. The conversion formula is as follows:

[0042]

[0043] Where: Pt is the normalized power value, P is the actual power value at time t, Pa and Pi are the maximum and minimum values ​​of the predicted power;

[0044] The data memory unit collects the normalized power data P at time t-1 t-1 , and the output long-term memory unit information is recorded as c t-1 and short-term memory unit information is implicitly encoded as d t-1 , c t-1 and d t-1 It has the function of transmitting data information, transferring long-term and short-term memory information to the gate unit, which contains the input gate it 、Forget gate t , control door c tr and output gate o t , use their respective functions to the current input data P t-1 , the implicit encoding of the input data at the last moment d t-1 And the internal memory unit output c t-1 After information processing, the long short-term memory unit information dt and ct of the next moment are output. Among them, the three gates use the nonlinear activation function sigmoid to control the information. The gate plays a control role, and the value range is compressed to between 0 and 1. When the value range of a certain gate is 0, the gate is in a fully closed state and cannot transmit any information. When the value range is 1, it is opposite to the 0 state, and the control gate g t The tanh function is used to correct information when it is compounded;

[0045] Input Gate i t 、Forget gate t , control door c tr and output gate o t The information output formula is as follows:

[0046] i t =sigmoid(γ ai ×P t +γ di ×d t-1 +b i )

[0047] f t =sigmoid(γ af ×P t +γ df ×d t-1 +b f )

[0048] c tr =tanh(γ ag ×P t +γ dg ×d t-1 +b g )

[0049] o t =sigmoid(γ ao ×P t +γ do ×d t-1 +b o )

[0050] Where: γ ai , γ di , γ af , γ df, γ ag , γ dg , γ ao , γ do are all weight matrices; b i , b f , b g , b o are the bias items of the corresponding gate units respectively;

[0051] Internal memory unit c t-1 , after the gate module, updated to c t.1 ;

[0052] The internal memory unit ct information output formula is:

[0053] c t =c t-1 ×f t +i t ×g t

[0054] The output formula of the implicit encoding dr of the input data is:

[0055] d t =o t ×tanh(c t )

[0056] The data output unit is connected via the input c t and d t Parameter calculation and prediction of normalized power at time t+1

[0057] The predicted normalized power Denormalize to get the predicted power The conversion formula is as follows

[0058]

[0059] Where: P max is the maximum value of the test power; P min is the maximum value of the test power; is the normalized predicted power; Denormalized predicted power.

[0060] The intelligent control method for a mixer based on emulsified asphalt mixture of the present invention comprises the following specific steps:

[0061] Step S1, using the parameter sharing feature of convolutional neural network, the convolutional model predicts the power at time t+1 based on the power P at time t The mixed material power is predicted one moment in advance, and the controller will predict the mixed material power The best output power P of the mixer best Compare, if you set the threshold The mixer operates normally; if the threshold is set The controller can be used to adjust parameters, send signals to control the motor speed, control the feed amount, and control the mixer operation status in real time;

[0062] Step S2, the controller transmits the predicted mixed material power to the alarm module through the communication line, that is, after the controller transmits the predicted mixed material power, it sends a signal to the indicator light, and controls the flashing of the indicator light differently in different situations, so as to achieve the purpose of intelligent control;

[0063] In step S3, the controller transmits the predicted mixed material power to the display screen through the communication line. The display screen displays the key parameters in the mixing process and monitors the operating status of the mixer in real time.

[0064] The above embodiments describe the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for illustrating the principles of the present invention. Without departing from the scope of the principles of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for intelligent control of a mixer based on the material properties of emulsified asphalt mixture, wherein the mixer comprises a mixer body, a feeding device arranged above the mixer body, a power device arranged on the side of the mixer body; and a discharging device arranged below the mixer body, a raw material bypass detection device arranged on the mixer and a mixer intelligent control system, emulsified asphalt, water are respectively transported to the feeding end of the mixer through pipelines, and rap material or new material is respectively transported to the feeding end of the mixer through belt conveyors, the power device is started by a hydraulic motor, drives the power shaft of the reduction motor, transmits to the driving shaft, drives the driven shaft to move relative to each other, the mixing blades are arranged on the mixing shaft in a straight line, the raw materials are stirred, and at the same time play the role of scraping the wall and conveying, the sensor collects signals, the convolutional neural network performs data processing, feature extraction, deep learning generates adversarial networks, online monitoring of abnormalities, fault diagnosis and prediction, the raw material bypass detection device analyzes the raw material characteristics, forms global features, and sends them to the controller, the human-machine interface intervenes, and the mixer operation parameters are adjusted in real time, the mixed material is stirred and conveyed to the discharging port, and the mixer can work continuously without blocking.

2. The intelligent control method for a mixer based on the material properties of emulsified asphalt mixture according to claim 1 is characterized in that: The feeding device can freely switch between rap material and new material and can automatically adjust the amount of emulsified asphalt and water. After the stirring motor of the power device is started, the stirring shaft rotates, and the stirring blades stir and convey the mixture. Through torque sensors, pressure sensors, temperature sensors, particle size sensors, rotary resistance sensors, etc., it automatically collects data information such as mixer motor torque, mixer motor speed, mixing temperature, aggregate particle size, emulsified asphalt aggregate ratio, etc., and uses convolutional neural networks to process data, extract features, generate adversarial networks through deep learning, monitor anomalies online, diagnose and predict faults, and form global features to send to the controller. The controller processes the information and establishes a statistical, analytical, processing, storage, archiving, and query information system for the data.

3. The intelligent control method for a mixer based on the material properties of emulsified asphalt mixture according to claim 1 is characterized in that: The mixer includes a mixing device body and a raw material bypass detection device arranged on the mixer device. The sensor collects signals, detects and analyzes the shape, position and temperature of the emulsified asphalt. The blocking phenomenon and characteristics in different areas are collective and correlated. Further hierarchical analysis is performed, and the abnormal data in the detection area is combined to analyze the cause of the blocking, and regional troubleshooting measures are proposed. Combined with the mixing test, the optimal asphalt dosage and the optimal mixing temperature of the gradation are determined, and the asphalt temperature and the gradation of the mixture are adjusted by the controller.

4. The intelligent control method for a mixer based on the material properties of emulsified asphalt mixture according to claim 1 is characterized in that: During the mixing construction of emulsified asphalt mixture, the function of uniform mixing of emulsified asphalt mixture and no abnormal condition of the mixer is realized. The prediction neural network structure includes a data memory unit, a gate unit and a data output unit. Before the power data enters the prediction neural network, it needs to be converted into dimensionless data and normalized. Normalization converts the power data into data between 0 and 1. The conversion formula is as follows: Where: Pt is the normalized power value, P is the actual power value at time t, Pa and Pi are the maximum and minimum values ​​of the predicted power; The data memory unit collects the normalized power data P at time t-1 t-1 , and the output long-term memory unit information is recorded as c t-1 and short-term memory unit information is implicitly encoded as d t-1 , c t-1 and d t-1 It has the function of transmitting data information and transferring long and short-term memory information to the gate unit, which contains the input gate i t 、Forget gate t , control door c tr and output gate o t , use their respective functions to the current input data P t-1 , the implicit encoding of the input data at the last moment d t-1 And the internal memory unit output c t-1 After information processing, the long short-term memory unit information dt and ct of the next moment are output. Among them, the three gates use the nonlinear activation function sigmoid to control the information. The gate plays a control role, and the value range is compressed to between 0 and 1. When the value range of a certain gate is 0, the gate is in a fully closed state and cannot transmit any information. When the value range is 1, it is opposite to the 0 state, and the control gate g t The tanh function is used to correct information when it is compounded; Input Gate i t 、Forget gate t , control door c tr and output gate o t The information output formula is as follows: I t =sigmoid(γ ai ×P t +g di ×d t-1 +b i ) f t =sigmoid(γ af ×P t +g df ×d t-1 +b f ) c tr =tanh(γ ag ×P t +γ dg ×d t-1 +b g ) the t =sigmoid(γ ao ×P t +g do ×d t-1 +b o ) Where: γ ai , γ di , γ af , γ df , γ ag , γ dg , γ ao , γ do are weight matrices; b i , b f , b g , b o are the bias items of the corresponding gate units respectively; Internal memory unit c t-1 , after the gate module, updated to c t.1 ; The internal memory unit ct information output formula is: c t =c t-1 ×f t +i t ×g t The output formula of the implicit encoding dr of the input data is: d t =o t ×tanh(c t ) The data output unit is connected via the input c t and d t Parameter calculation and prediction of normalized power at time t+1 The predicted normalized power Denormalize to get the predicted power The conversion formula is as follows Where: P max is the maximum value of the test power; P min is the maximum value of the test power; is the normalized predicted power; Denormalized predicted power; according to Power data, get power change rate data and power acceleration rate data The abnormal power value is analyzed by data such as temperature, particle size and mix ratio. At the same time, the abnormal value is analyzed by collecting temperature, particle size and mix ratio information to obtain the corresponding mixer fault event, adjust the mixer working state in advance, and control the feed parameters such as mineral aggregate gradation, mixing temperature, and oil-stone ratio of the emulsified asphalt mixture through timely feedback from the alarm module and the display module to achieve continuous operation of the mixer. If the data detection and analysis is abnormal, the bypass detection device is turned on to analyze the characteristics of the raw materials, namely emulsified asphalt, water, and aggregate, and adjust the feed parameters, including emulsified asphalt temperature and material door switch amount, and water volume, to ensure that the mineral aggregate gradation, mixing temperature, and oil-stone ratio are in the best state to ensure continuous mixing; if the data detection and analysis is still abnormal, switch the aggregate input mode. Due to the secondary aging of RAP materials and the insufficient performance of hot recycled asphalt mixtures, the rap silo is automatically switched to the new silo, and the angular new materials are transported to the main body of the mixing device together with water and emulsified asphalt to improve the mixing and scraping effects, and are evenly mixed and then transported to the discharge port.

5. The intelligent control method for a mixer based on the material properties of emulsified asphalt mixture according to claim 1, characterized in that The specific steps are: Step S1, ① Using the parameter sharing feature of convolutional neural network, the convolutional model predicts the power at time t+1 based on the power P at time t The mixed material power is predicted one moment in advance, and the controller will predict the mixed material power The best output power P of the mixer best Compare, if you set the threshold The mixer operates normally; if the threshold is set The controller adjusts the parameters, sends signals to control the motor speed, controls the feed rate, and controls the mixer operation status in real time; ② The convolution model predicts the power at time t+1 based on the power change rate △P at time t The power change rate of the mixture is predicted one moment in advance, and the controller will predict the power change rate of the mixture Compared with the optimal output power change rate of the mixer △Pbest, if the threshold is set The mixer operates normally; if the threshold is set The controller adjusts the parameters, sends signals to control the motor speed, controls the feed rate, and controls the power change rate of the mixer in real time; ③ The convolution model predicts the power acceleration at time t+1 based on the power acceleration △△P at time t The power acceleration of the mixture is predicted one moment in advance, and the controller uses the predicted power acceleration of the mixture Compared with the optimal output power of the mixer △△Pbest, if the threshold is set The mixer operates normally; if the threshold is set The controller adjusts the parameters, sends signals to control the motor speed, controls the feed rate, and controls the operating state of the mixer acceleration rate in real time; At the same time, the temperature, particle size and rotation resistance sensor information are collected to analyze abnormal values ​​and corresponding mixer failure events, and the mixer working status is adjusted in advance. Through the timely feedback of the alarm module and the display module, the mineral grading, mixing temperature, oil-stone ratio and other feeding parameters of the emulsified asphalt mixture are controlled to achieve continuous operation of the mixer; Step S2, the controller transmits the predicted mixed material power to the alarm module through the communication line, that is, after the controller transmits the predicted mixed material power, it sends a signal to the indicator light, and controls the flashing of the indicator light differently in different situations, so as to achieve the purpose of intelligent control; In step S3, the controller transmits the predicted mixed material power to the display screen through the communication line. The display screen displays the key parameters in the mixing process and monitors the operating status of the mixer in real time.

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