Homogenizing equipment for rubber asphalt processing

Abstract

The invention discloses homogenization equipment for rubber asphalt processing, and relates to the technical field of rubber asphalt processing equipment, the homogenization equipment comprises an intelligent control system, the intelligent control system comprises a data analysis module, a homogenization analysis module, a compensation analysis module and an execution judgment module; the data analysis module is used for receiving and processing the sensing data sent by the sensor group to obtain a statistical index of any parameter in the sensing data; performing environment analysis on the data preprocessed by the sensor group in combination with a statistical index to obtain a homogeneous analysis result; and the homogenization analysis module is used for evaluating the mixing quality in the stirring process based on the homogenization analysis result. By arranging the multi-dimensional sensor group in the mixing box and constructing the three-dimensional space model, real-time three-dimensional monitoring and intelligent analysis of temperature, viscosity and concentration parameters in the rubber asphalt mixing process are realized, parameter abnormal points and trend changes can be identified, and the problems that traditional equipment is lagged in monitoring and cannot capture spatial distribution characteristics are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of rubber asphalt processing equipment, and in particular to a homogenizing equipment for rubber asphalt processing. Background Art

[0002] In road construction, rubber asphalt is becoming a popular material due to its excellent performance. Rubber asphalt is made by adding scrap tire rubber powder to a base asphalt and then undergoing a series of processing steps. It not only effectively utilizes scrap tires, achieving resource recycling and alleviating environmental pollution, but also significantly improves the performance of asphalt pavements. For example, pavements paved with rubber asphalt exhibit excellent skid resistance, wear resistance, aging resistance, and noise reduction, extending road life and reducing maintenance costs.

[0003] However, in the processing of rubber asphalt, homogenizing equipment is required. However, when using existing homogenizing equipment, due to the differences in density, viscosity and particle size between rubber powder and matrix asphalt, it is easy to cause uneven mixing, and there is a lack of multi-parameter real-time monitoring and intelligent adjustment mechanism. There are problems such as large local concentration deviation, low stirring efficiency and high energy consumption, which affect construction quality and production benefits. Summary of the Invention

[0004] In order to solve the problems in the background technology, the present invention proposes a homogenizing device for processing rubber asphalt.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A homogenizing device for processing rubber asphalt, comprising a mixing box, a stirring mechanism disposed within the mixing box, and a sensor group for monitoring the state of the material within the mixing box, and an intelligent control system electrically connected to the sensor group and the stirring mechanism; the intelligent control system comprises a data analysis module, a homogenizing analysis module, a compensation analysis module, and an execution judgment module; The data analysis module is used to receive and process the sensor data sent by the sensor group to obtain statistical indicators of any parameter in the sensor data; the data pre-processed by the sensor group is combined with the statistical indicators to perform environmental analysis to obtain homogeneous analysis results; wherein the statistical indicators include the mean, the range of the maximum and minimum values, and the coefficient of variation; the homogeneous analysis results include the calculation indicators of each parameter in the sensor data, the trend influencing factors and the comprehensive evaluation value of the parameters, and the abnormal conditions of the parameters; The homogenization analysis module is used to evaluate the mixing quality during the stirring process based on the homogenization analysis results; The compensation analysis module is used to generate a compensation coefficient for adjusting the operating state of the equipment based on the homogeneity analysis results and the mixing quality; The execution judgment module is used to generate and execute corresponding control strategies to adjust the operation of the stirring mechanism based on the homogeneity analysis results and the mixing quality.

[0006] Preferably, a bracket is provided on the outside of the mixing box, a barrel is provided on the top of the bracket through a support member, and the mixing box and the barrel are connected by a connecting member; a mounting ring is symmetrically provided on the top surface of the mixing box, and the stirring mechanism for stirring the rubber asphalt in the mixing box is provided on the mounting ring.

[0007] Preferably, the stirring mechanism includes a stirring shaft rotatably arranged on the mixing box, a driving motor is provided on the top of the stirring shaft, and a plurality of stirring blades are obliquely provided on the bottom outer wall; a motor frame is provided on the outer wall of the driving motor, and the motor frame is installed on the top surface of the mounting ring.

[0008] Preferably, the connecting member comprises a tee pipe arranged at the discharge end of the barrel, a connecting pipe is provided at the bottom of the tee pipe, and the connecting pipe passes through each mounting ring and enters the mixing box.

[0009] Preferably, the sensor data sent by the sensor group is received and processed, specifically: Acquire sensor data collected by a preset sensor group and perform preprocessing, including outlier removal, noise filtering, data normalization, time synchronization, and missing value filling; The space inside the mixing box is used as the mixing space, and a three-dimensional space model of the mixing box is generated using a space recognition algorithm; Set the installation position of the preset sensor group and mark the installation position of the sensor in the three-dimensional spatial model of the mixing box; the sensor group includes temperature, humidity, pressure, viscosity, concentration and vibration sensors distributed at different heights and directions of the mixing box; Acquire sensor data collected by the sensor group after preprocessing, including temperature, humidity, vibration amplitude, pressure, viscosity, and concentration of rubber asphalt collected by sensors at different locations during the mixing process; The numerical value of any parameter in the sensor data is mapped to a three-dimensional space to form a parameter point. The parameter points at the same height are analyzed and their statistical indicators are calculated. The statistical indicators include the mean, the range between the maximum and minimum values, and the coefficient of variation.

[0010] Preferably, the data pre-processed by the sensor group is combined with statistical indicators to perform environmental analysis, specifically: Analyze the parameters in the sensor data and the preset standard values ​​to obtain the parameter abnormality evaluation value corresponding to the sensor position; Perform time zone analysis on the standard deviation of the parameters in the sensor data at a specified time to obtain the trend influencing factors corresponding to the parameters; The trend impact factor corresponding to the parameter and the corresponding parameter abnormal evaluation value are multiplied to obtain the comprehensive evaluation value of the parameter; The comprehensive evaluation value of the parameter is compared with its preset threshold to determine whether the parameter at each position is normal. If it is abnormal, it is recorded as the abnormal point corresponding to the parameter in the sensor data, and the position and number of the abnormal point are recorded as the parameter abnormality; the parameter abnormality evaluation value, trend influencing factor, comprehensive evaluation value of the parameter and parameter abnormality are used as the analysis results corresponding to the parameter; the analysis results of all parameters in the sensor data are summarized and recorded as the homogeneous analysis results.

[0011] Preferably, the homogenization analysis module is used to evaluate the mixing quality during the stirring process based on the homogenization analysis results, and performs the following evaluations: Obtain statistical indicators and homogeneity analysis results corresponding to concentration and viscosity in sensor data; To quantify the mixing uniformity of rubber asphalt, the L2 norm of the standard deviation of the concentration is calculated and the uniformity index is defined; Obtain the uniformity index of all positions, calculate the mean and standard deviation to obtain the average index mean and average index fluctuation value, then calculate the difference between the maximum and minimum uniformity index in all positions to obtain the average index range value, and perform weighted calculation on the average index mean, average index fluctuation value, average index range value and their preset weights to obtain the mixed uniformity index; Then, the viscosity and concentration are coupled and analyzed to obtain the coefficient of variation corresponding to the concentration and viscosity, and to construct a coupling index; The coupling index of all positions is obtained, and the mean and standard deviation are calculated to obtain the coupling index mean and coupling index fluctuation value. Then, the difference between the maximum and minimum coupling indexes in all positions is calculated to obtain the coupling index range value. The coupling index mean, coupling index fluctuation value, coupling index range value and their preset weights are weighted to obtain the coupling comprehensive index.

[0012] Preferably, the compensation analysis module generates a compensation coefficient for adjusting the operating state of the equipment based on the homogeneity analysis result and the mixing quality, specifically generating: Temperature compensation coefficient: Get the number of temperature anomalies in the mixing box. When the number is greater than the preset temperature anomaly threshold, generate a temperature compensation coefficient. The logic for obtaining the temperature compensation coefficient is as follows: Obtain the trend influencing factor and its standard deviation of the temperature in the mixing box, and calculate the temperature compensation coefficient by combining the trend influencing factor and its standard deviation, the number of temperature anomaly points, and the total number of temperature monitoring points; Stirring intensity compensation coefficient: Obtain the mixing uniformity index, set the target uniform mixing index, and use the preset formula to obtain the stirring intensity compensation coefficient; the stirring intensity compensation coefficient is used to adjust the speed of the drive motor.

[0013] Preferably, the parameters in the sensor data are analyzed with respect to the preset standard values ​​to obtain the parameter abnormality evaluation value corresponding to the sensor position, and the specific process is as follows: According to the preset standard value of the corresponding parameter set in the sensor data, the parameter in the sensor data is subtracted from its corresponding preset standard value and the absolute value is processed to obtain the standard deviation value corresponding to the parameter; the standard deviation value of the parameter and its statistical index are normalized and weighted to obtain the parameter abnormality assessment value corresponding to the sensor position.

[0014] Preferably, the standard deviation of the parameter in the sensor data is analyzed in the time zone of the equipment homogenization processing to obtain the trend influencing factor corresponding to the parameter. The specific analysis is as follows: The startup time of the device is taken as the first time, the current time is taken as the second time, and the time zone between the first time and the second time is taken as the device homogenization processing time zone; The calculation indicators of the standard deviation value of the parameter at any collection moment in the homogeneous processing time zone of the calculation equipment and its parameter anomaly evaluation value, including the mean, trend value, and range value, are calculated; the calculation indicators corresponding to the standard deviation value of the parameter and its parameter anomaly evaluation value are weightedly calculated to obtain the impact factor corresponding to the standard deviation value and the impact factor corresponding to the parameter anomaly evaluation value; the standard deviation value and the impact factor corresponding to the parameter anomaly evaluation value are weighted to obtain the trend impact factor corresponding to the parameter.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. By installing a multi-dimensional sensor group in the mixing box and constructing a three-dimensional spatial model, the present invention achieves real-time stereoscopic monitoring and intelligent analysis of temperature, viscosity, and concentration parameters during the rubber asphalt mixing process. It can identify parameter anomalies and trend changes, solving the problems of traditional equipment monitoring lag and inability to capture spatial distribution characteristics, and significantly improving the controllability and stability of the mixing process.

[0016] 2. The present invention uses an intelligent control system to automatically generate compensation coefficients such as stirring intensity and temperature based on real-time monitoring data, and drives the stirring mechanism and temperature control equipment to coordinate adaptive adjustment, effectively avoiding over-stirring or under-stirring, ensuring uniform dispersion of the various components of rubber asphalt, and improving the mixing quality of rubber asphalt. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It shows a schematic diagram of the overall structure provided by the present invention; Figure 2 It shows a schematic structural diagram of a homogenizing device provided according to the present invention; Figure 3 shows a cross-sectional view of a mixing box provided according to the present invention; Figure 4The figure shows a principle block diagram of an intelligent control system provided according to the present invention.

[0018] Legend: 1. Bracket; 2. Mixing box; 3. Barrel; 4. Fixed frame; 5. Tee pipe; 6. Connecting pipe; 7. Drive motor; 8. Motor frame; 9. Mounting ring; 10. Stirring shaft; 11. Stirring blade. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] See also Figures 1-4 , the present invention provides a technical solution: A homogenizing device for processing rubber asphalt includes a mixing box 2, a stirring mechanism disposed within the mixing box 2, and a sensor group for monitoring the state of the material within the mixing box 2. The intelligent control system is electrically connected to the sensor group and the stirring mechanism; the intelligent control system includes a data analysis module, a homogenizing analysis module, a compensation analysis module, and an execution judgment module. The data analysis module receives and processes sensor data from the sensor group to obtain statistical indicators for any parameter in the sensor data. It then performs environmental analysis on the sensor group's pre-processed data in combination with statistical indicators to obtain homogeneous analysis results. Statistical indicators include the mean, the range between the maximum and minimum values, and the coefficient of variation. The homogeneous analysis results include the calculated indicators of each parameter in the sensor data, trend influencing factors, comprehensive parameter evaluation values, and parameter anomalies. The homogenization analysis module is used to evaluate the mixing quality during the stirring process based on the homogenization analysis results; The compensation analysis module is used to generate a compensation coefficient for adjusting the operating status of the equipment based on the homogeneity analysis results and the mixing quality; The execution judgment module is used to generate and execute corresponding control strategies to adjust the operation of the stirring mechanism based on the homogeneity analysis results and the mixing quality.

[0021] In the present application, a bracket 1 is provided on the outside of the mixing box 2, and a barrel 3 is provided on the top of the bracket 1 through a support member, and the mixing box 2 and the barrel 3 are connected by a connecting member; a mounting ring 9 is symmetrically provided on the top surface of the mixing box 2, and a stirring mechanism for stirring the rubber asphalt in the mixing box 2 is provided on the mounting ring 9.

[0022] In the present application, the stirring mechanism includes a stirring shaft 10 rotatably arranged on the mixing box 2, a driving motor 7 is provided on the top of the stirring shaft 10, and a plurality of stirring blades 11 are obliquely provided on the bottom outer wall; a motor frame 8 is provided on the outer wall of the driving motor 7, and the motor frame 8 is installed on the top surface of the mounting ring 9.

[0023] In the present application, the connecting piece includes a tee pipe 5 provided at the discharge end of the barrel 3 , a connecting pipe 6 is provided at the bottom of the tee pipe 5 , and the connecting pipe 6 passes through each mounting ring 9 and enters the mixing box 2 .

[0024] In this application, the sensor data sent by the sensor group is received and processed, specifically: Acquire sensor data collected by a preset sensor group and perform preprocessing. Preprocessing includes outlier removal, noise filtering, data normalization, time synchronization, and missing value filling. Preprocessing is an existing routine operation and will not be described in detail. The space inside the mixing box 2 is used as the mixing space, and a space recognition algorithm is used to generate a three-dimensional space model of the mixing box 2; Set the installation position of the preset sensor group and mark it in the three-dimensional spatial model of the mixing box 2 according to the installation position of the sensor; the sensor group includes temperature, humidity, pressure, viscosity, concentration sensors and vibration sensors distributed at different heights and directions of the mixing box 2; Acquire sensor data collected by the sensor group after preprocessing, including temperature, humidity, vibration amplitude, pressure, viscosity, and concentration of rubber asphalt collected by sensors at different locations during the mixing process; The value of any parameter in the sensor data Corresponding to the three-dimensional space to form parameter points, analyze the same height parameter points, and calculate their statistical indicators, including the mean , maximum value With minimum value The range of , coefficient of variation CV, the coefficient of variation formula is , Indicates the standard deviation of the same height parameter point.

[0025] In this application, the sensor group pre-processed data is combined with statistical indicators to perform environmental analysis, specifically: Analyze the parameters in the sensor data and the preset standard values ​​to obtain the parameter abnormality evaluation value E corresponding to the sensor position; Perform time zone analysis on the standard deviation of the parameters in the sensor data at a specified time to obtain the trend influence factor g corresponding to the parameter; The parameter comprehensive evaluation value ZH is obtained by multiplying the trend impact factor corresponding to the parameter and the corresponding parameter abnormal evaluation value; The comprehensive evaluation value of the parameter is compared with its preset threshold to determine whether the parameter at each position is normal. If it is abnormal, it is recorded as the abnormal point corresponding to the parameter in the sensor data, and the position and number of the abnormal point are recorded as the parameter abnormality; the parameter abnormality evaluation value, trend influencing factor, comprehensive evaluation value of the parameter and parameter abnormality are used as the analysis results corresponding to the parameter; the analysis results of all parameters in the sensor data are summarized and recorded as the homogeneous analysis results.

[0026] In this application, the parameters in the sensor data are analyzed with respect to the preset standard values ​​to obtain the parameter abnormality evaluation value E corresponding to the sensor position. The specific process is as follows: According to the preset standard value of the corresponding parameter in the sensor data, the parameter in the sensor data is subtracted from its corresponding preset standard value and the absolute value is processed to obtain the standard deviation value CS1 corresponding to the parameter; the standard deviation value of the parameter is normalized and weighted with its statistical index, and the formula is: , get the parameter abnormality evaluation value E corresponding to the sensor position; where, They represent the weights corresponding to the standard deviation, mean, range and coefficient of variation of the parameters, and are set according to actual conditions.

[0027] The standard deviation of the parameters in the sensor data is analyzed in the time zone of the equipment homogenization processing to obtain the trend influence factor g corresponding to the parameter. The specific analysis is as follows: The startup time of the device is taken as the first time, the current time is taken as the second time, and the time zone between the first time and the second time is taken as the time zone for homogenizing the device; The calculation indicators of the standard deviation value of the parameter at any acquisition moment in the homogeneous processing time zone of the calculation equipment and its parameter anomaly evaluation value, including the mean, trend value, and range value, are calculated; the calculation indicators corresponding to the standard deviation value of the parameter and its parameter anomaly evaluation value are weightedly calculated to obtain the impact factor corresponding to the standard deviation value and the impact factor corresponding to the parameter anomaly evaluation value; the standard deviation value and the impact factor corresponding to the parameter anomaly evaluation value are weighted to obtain the trend impact factor g corresponding to the parameter.

[0028] In this application, the homogenization module evaluates the mixing quality results during the stirring process and performs the following evaluations: Obtain statistical indicators and homogeneity analysis results corresponding to concentration and viscosity in sensor data;

[0029] To quantify the mixing uniformity of rubber asphalt, the L2 norm of the standard deviation of the concentration is calculated, and the formula is: , and then define the uniformity index ,formula: ,in, is the norm of the standard deviation of the concentration at the initial mixing moment, Indicates improved mixing uniformity; Get the uniformity index for all locations , calculate the mean and standard deviation to obtain the average index mean and average index fluctuation value, then calculate the difference between the maximum and minimum uniformity index in all positions to obtain the average index range value, and perform weighted calculation on the average index mean, average index fluctuation value, average index range value and their preset weights to obtain the mixed uniformity index U; Then, the viscosity and concentration were coupled and analyzed, and the coefficients of variation corresponding to the concentration and viscosity were obtained and recorded as 、 , construct the coupling index K, the formula is: ,in 、 are the weights corresponding to the coefficient of variation of concentration and viscosity, respectively, which are used to evaluate the effect of fluidity on mixing effect. The smaller the K value, the better the coupling performance. The coupling index of all positions is obtained, and the mean and standard deviation are calculated to obtain the coupling index mean and coupling index fluctuation value. Then, the difference between the maximum and minimum coupling indexes in all positions is calculated to obtain the coupling index range value. The coupling index mean, coupling index fluctuation value, coupling index range value and their preset weights are weighted to obtain the coupling comprehensive index.

[0030] In this application, the compensation analysis module generates a compensation coefficient for adjusting the operating state of the equipment based on the homogeneity analysis results and the mixing quality. Specifically, it generates: Temperature compensation coefficient: Get the number of temperature anomalies Nt in mixing box 2. When the number is greater than the preset temperature anomaly threshold Nn, generate a temperature compensation coefficient. The logic for obtaining the temperature compensation coefficient is as follows: Obtain the trend influencing factor g and its standard deviation CS1 of the temperature in the mixing box 2, calculate the trend influencing factor g and its standard deviation CS1, the number of temperature anomaly points and the total number of temperature monitoring points, and the formula is expressed as follows: , get the temperature compensation coefficient ; Among them, g is the temperature trend influencing factor, which is obtained through the time series analysis of temperature data and characterizes the direction and intensity of the impact of temperature change trend on mixing quality. It represents the trend influence weight coefficient, which is in the range of 0-1. It can be set according to the influence of temperature change trend on mixing quality. Indicates the absolute value of the temperature standard deviation, reflecting the degree to which the temperature deviates from the preset standard value. Indicates the number of temperature monitoring points; It should be noted that the temperature anomaly points are concentrated and the deviation degree is high, such as When it is larger, Significantly increase, strengthen the heating / cooling power; if the temperature continues to deteriorate (g>0), The project amplifies the compensation strength and realizes early intervention of trend anomalies; Stirring intensity compensation coefficient: Get the mixing uniformity index U, set the target uniform mixing index Un, and use the preset formula: , and obtain the stirring intensity compensation coefficient d; the stirring intensity compensation coefficient is used to adjust the speed h of the drive motor 7, h=h'×d, h' represents the current speed of the drive motor 7; When the uniformity is insufficient (U<Un), the stirring intensity compensation coefficient d increases to increase the speed and strengthen the stirring; when it approaches the target uniformity, the stirring intensity compensation coefficient d automatically decays to reduce energy consumption, thereby achieving dynamic matching of stirring intensity and mixing quality.

[0031] In this application, the execution judgment module generates an execution strategy based on the following rules: When the temperature, humidity, and vibration amplitude are greater than their preset evaluation thresholds, corresponding execution strategies are generated, including temperature adjustment strategy, humidity adjustment strategy, and amplitude inspection strategy; when the coupling comprehensive index is greater than its preset viscosity-concentration threshold, a viscosity-concentration coordinated adjustment strategy is generated; When the temperature regulation strategy is generated, the cooling device or heating device in the mixing box 2 is started; When generating a humidity adjustment strategy, starting a dehumidification device or a humidification device; When generating the amplitude inspection strategy, the vibration source (such as agitator shaft eccentricity, agitator blade wear, or material accumulation) is determined based on the location of the vibration anomaly points marked by the data analysis module and combined with the 2D spatial model of the mixing box. A vibration graded response group is set, including mild vibration anomaly and severe vibration anomaly. The comprehensive parameter evaluation value corresponding to the vibration amplitude is matched with the value range of its vibration graded response group: If the vibration is slightly abnormal, an early warning signal is generated to prompt the operator to check the connection parts of the stirring mechanism (such as the motor frame 8 bolts and the stirring shaft bearings); if the vibration is seriously abnormal, the stirring drive motor 7 is automatically paused, the safety shutdown program is triggered, and a fault code is generated and stored in the storage module for traceability; When generating a viscosity-concentration coordinated regulation strategy, different regulation paths are triggered according to the dominant relationship between the viscosity and concentration coefficient of variation: if the viscosity is too high, the speed and temperature are adjusted; if the concentration is uneven, the stirring is locally strengthened and the time is extended; when the two parameters are strongly correlated, a multivariable PID linkage is used to control the speed of the drive motor 7, the heating power and the inclination angle of the stirring blade, the effect is verified in real time, and the regulation model is optimized through historical data.

[0032] It should be noted that, in response to abnormal environmental parameters such as temperature and humidity, cooling / heating, dehumidification / humidification and other adjustment strategies are automatically triggered to maintain the stability of the process environment in the mixing box, avoid the degradation of rubber asphalt performance due to temperature and humidity fluctuations, and realize closed-loop control of environmental parameters. Based on the viscosity-concentration coupling index, intelligent matching adjustment paths (such as speed-temperature linkage, local stirring enhancement, multivariable PID control) are used to dynamically solve the problems of excessive viscosity or uneven concentration during the mixing process, realize adaptive optimization of the mixing process, and ensure the uniformity and performance consistency of rubber asphalt. In summary, the execution strategy generation rules and corresponding adjustment and control methods described in this section are relatively conventional and existing in the relevant technical fields.

[0033] This equipment realizes real-time monitoring and adaptive adjustment of the rubber asphalt mixing process through the coordinated work of a multi-dimensional sensor group and an intelligent control system. The specific working principle is as follows: Data collection and preprocessing stage: Temperature, viscosity, and concentration sensors located at different heights and locations in the mixing tank continuously collect parameter data during the rubber asphalt mixing process. For example, as the rubber asphalt raw material enters the mixing tank from the barrel through the tee and connecting pipe, temperature sensors monitor the material temperature at each location in real time to prevent local overheating or overcooling that may affect mixing quality.

[0034] The data analysis module pre-processes the collected sensor data by removing outliers and filtering out noise. For example, if the temperature sensor data at a certain moment suddenly rises by 100°C, clearly exceeding the normal range, the system automatically removes the outlier and fills in the gaps with data from nearby moments to ensure the accuracy of subsequent analysis.

[0035] Three-dimensional spatial modeling and statistical analysis: A spatial recognition algorithm is used to generate a 3D model of the mixing box, marking the sensor installation locations. When the mixing mechanism is activated, the drive motor rotates the mixing shaft and impellers. The 3D model visually displays the material state changes at each sensor location.

[0036] For example, calculate the mean, range, and coefficient of variation for temperature points at the same altitude. For example, if the mean temperature at a certain altitude is 180°C, the maximum is 195°C, and the minimum is 165°C, then the range is 30°C. The coefficient of variation formula is used to calculate the CV value, which reflects the degree of dispersion of the temperature distribution at that altitude.

[0037] Environmental Analysis and Anomaly Assessment: Taking the temperature parameter as an example, the preset standard value is 180°C, and the actual measured value at a certain sensor location is 190°C. The calculated standard deviation value CS1 = |190-180| = 10°C. Combined with the mean, range, coefficient of variation, and corresponding weights of the temperature parameter, the parameter anomaly assessment value E is obtained through normalized weighted calculation to determine whether the temperature at that location is abnormal.

[0038] Using the equipment startup moment as the first moment and the current moment as the second moment, calculate the temperature standard deviation, the mean of the anomaly assessment value, and the trend value indicator within the homogenization treatment time zone. If the temperature continues to rise, the trend value is positive, and a weighted calculation is used to determine the trend impact factor g, which reflects the impact of the temperature change trend on the mixing quality.

[0039] Hybrid quality assessment and compensation adjustment: Calculate the L2 norm of the concentration standard deviation and define the uniformity index U1. For example, if the concentration norm at the initial mixing moment is 10 and the norm at the current moment is 5, then U1 = 1-5 / 10 = 0.5. Then, calculate the mixing uniformity index U by using the mean and standard deviation to evaluate the current mixing quality.

[0040] Temperature compensation: If the number of temperature anomaly points Nt is greater than the preset threshold Nn, such as Nt=5 and Nn=3, calculate the temperature compensation coefficient βT=1+kT×(5 / NJ×|CS1|+λ×g), increase the heating or cooling power, and adjust the temperature in the mixing box.

[0041] Stirring intensity compensation: When the mixing uniformity index U is less than the target value Un, such as U=0.6, Un=0.8, calculate the stirring intensity compensation coefficient d=exp((0.8-0.6) / 0.8)≈1.284, adjust the drive motor speed to h'×1.284, and strengthen the stirring.

[0042] Enforcement policies and security controls: If the comprehensive evaluation value of the vibration amplitude parameter shows a severe abnormality, the execution judgment module automatically pauses the drive motor, triggers the safety shutdown program, and generates a fault code for storage, such as the prompt "The stirring shaft is eccentric, and the bearing needs to be checked."

[0043] When the coupled comprehensive indicators show abnormal viscosity and concentration, such as excessively high viscosity and uneven concentration, the system triggers multivariable PID linkage control, and simultaneously adjusts the drive motor speed, heating power, and stirring blade inclination angle to verify the adjustment effect in real time and optimize the model.

[0044] Through the coordinated work of the above modules, the equipment realizes intelligent monitoring and adaptive adjustment of the rubber asphalt mixing process, ensuring the mixing quality and improving production efficiency.

[0045] The above description of the embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A homogenizing device for processing rubber asphalt, comprising a mixing box (2), a stirring mechanism arranged in the mixing box (2), and a sensor group for monitoring the state of the material in the mixing box (2), characterized in that: It also includes an intelligent control system, which is electrically connected to the sensor group and the stirring mechanism; the intelligent control system includes a data analysis module, a homogenization analysis module, a compensation analysis module and an execution judgment module; The data analysis module is used to receive and process the sensor data sent by the sensor group to obtain statistical indicators of any parameter in the sensor data; the data pre-processed by the sensor group is combined with the statistical indicators to perform environmental analysis to obtain homogeneous analysis results; wherein the statistical indicators include the mean, the range of the maximum and minimum values, and the coefficient of variation; the homogeneous analysis results include the calculation indicators of each parameter in the sensor data, the trend influencing factors and the comprehensive evaluation value of the parameters, and the abnormal conditions of the parameters; The homogenization analysis module is used to evaluate the mixing quality during the stirring process based on the homogenization analysis results; The compensation analysis module is used to generate a compensation coefficient for adjusting the operating state of the equipment based on the homogeneity analysis results and the mixing quality; The execution judgment module is used to generate and execute corresponding control strategies to adjust the operation of the stirring mechanism based on the homogeneity analysis results and the mixing quality.

2. A homogenizing device for processing rubber asphalt according to claim 1, characterized in that: A bracket (1) is provided on the outside of the mixing box (2), a barrel (3) is provided on the top of the bracket (1) via a support member, and the mixing box (2) and the barrel (3) are connected via a connecting member; a mounting ring (9) is symmetrically provided on the top surface of the mixing box (2), and a stirring mechanism for stirring the rubber asphalt in the mixing box (2) is provided on the mounting ring (9).

3. A homogenizing device for processing rubber asphalt according to claim 2, characterized in that: The stirring mechanism comprises a stirring shaft (10) rotatably arranged on a mixing box (2); a driving motor (7) is provided on the top of the stirring shaft (10); and a plurality of stirring blades (11) are obliquely provided on the outer wall of the bottom; a motor frame (8) is provided on the outer wall of the driving motor (7), and the motor frame (8) is mounted on the top surface of a mounting ring (9).

4. A homogenizing device for processing rubber asphalt according to claim 2, characterized in that: The connecting piece comprises a three-way pipe (5) arranged at the discharge end of the barrel (3), a connecting pipe (6) is provided at the bottom of the three-way pipe (5), and the connecting pipe (6) passes through each mounting ring (9) and enters the mixing box (2).

5. The homogenizing equipment for processing rubber asphalt according to claim 1, characterized in that: Receive sensor data sent by the sensor group and process it. The specific processing is as follows: Acquire sensor data collected by a preset sensor group and perform preprocessing, including outlier removal, noise filtering, data normalization, time synchronization, and missing value filling; The space in the mixing box (2) is used as the mixing space, and a three-dimensional space model of the mixing box (2) is generated by using a space recognition algorithm; Setting the installation position of the preset sensor group, and marking the installation position of the sensor in the three-dimensional space model of the mixing box (2); wherein the sensor group includes temperature, humidity, pressure, viscosity, concentration sensors and vibration sensors distributed at different heights and orientations of the mixing box (2); Acquire sensor data collected by the sensor group after preprocessing, including temperature, humidity, vibration amplitude, pressure, viscosity, and concentration of rubber asphalt collected by sensors at different locations during the mixing process; The value of any parameter in the sensor data Corresponding to the three-dimensional space to form parameter points, analyze the same height parameter points, and calculate their statistical indicators, including the mean , maximum value With minimum value The range of , coefficient of variation CV, the coefficient of variation formula is , Indicates the standard deviation of the same height parameter point.

6. The homogenizing equipment for processing rubber asphalt according to claim 1, characterized in that: The sensor group pre-processed data is combined with statistical indicators to perform environmental analysis, specifically: Analyze the parameters in the sensor data and the preset standard values ​​to obtain the parameter abnormality evaluation value E corresponding to the sensor position; Perform time zone analysis on the standard deviation of the parameters in the sensor data at a specified time to obtain the trend influence factor g corresponding to the parameter; The parameter comprehensive evaluation value ZH is obtained by multiplying the trend impact factor corresponding to the parameter and the corresponding parameter abnormal evaluation value; Compare the comprehensive evaluation value of the parameter with its preset threshold to determine whether the parameter at each position is normal. If it is abnormal, it is recorded as an abnormal point corresponding to the parameter in the sensor data, and the location and number of the abnormal point are recorded as the parameter abnormality; The parameter anomaly evaluation value, trend influencing factor, parameter comprehensive evaluation value and parameter anomaly situation are taken as the analysis results corresponding to the parameters; the analysis results of all parameters in the sensor data are summarized and recorded as the homogeneous analysis results.

7. The homogenizing equipment for processing rubber asphalt according to claim 1, characterized in that: The homogenization analysis module is used to evaluate the mixing quality during the stirring process based on the homogenization analysis results, and performs the following evaluations: Obtain statistical indicators and homogeneity analysis results corresponding to concentration and viscosity in sensor data; To quantify the mixing uniformity of rubber asphalt, the L2 norm of the standard deviation of the concentration is calculated, and the formula is: , and then define the uniformity index ,formula: ,in, is the norm of the standard deviation of the concentration at the initial mixing moment, Indicates improved mixing uniformity; Get the uniformity index for all locations 1. Calculate the mean and standard deviation to obtain the average index mean and average index fluctuation value, then calculate the difference between the maximum and minimum uniformity index in all positions to obtain the average index range value, and perform weighted calculation on the average index mean, average index fluctuation value, average index range value and their preset weights to obtain the mixed uniformity index U; Then, the viscosity and concentration were coupled and analyzed, and the coefficients of variation corresponding to the concentration and viscosity were obtained and recorded as 、 , construct the coupling index K, the formula is: ,in 、 are the weights corresponding to the coefficient of variation of concentration and viscosity, respectively; The coupling index of all positions is obtained, and the mean and standard deviation are calculated to obtain the coupling index mean and coupling index fluctuation value. Then, the difference between the maximum and minimum coupling indexes in all positions is calculated to obtain the coupling index range value. The coupling index mean, coupling index fluctuation value, coupling index range value and their preset weights are weighted to obtain the coupling comprehensive index.

8. The homogenizing equipment for processing rubber asphalt according to claim 6, characterized in that: The compensation analysis module generates a compensation coefficient for adjusting the operating state of the equipment based on the homogeneity analysis results and the mixing quality, specifically generating: Temperature compensation coefficient: Get the number of temperature anomalies Nt in the mixing box (2). When the number is greater than the preset temperature anomaly threshold Nn, generate a temperature compensation coefficient. The logic for obtaining the temperature compensation coefficient is: Obtain the trend influencing factor g and its standard deviation CS1 of the temperature in the mixing box (2), calculate the trend influencing factor g and its standard deviation CS1, the number of temperature anomaly points and the total number of temperature monitoring points, and the formula is expressed as follows: , get the temperature compensation coefficient ; where g is the temperature trend influencing factor, represents the trend impact weight coefficient, Indicates the absolute value of the temperature standard deviation, Indicates the number of temperature monitoring points; Stirring intensity compensation coefficient: Get the mixing uniformity index U, set the target uniform mixing index Un, and use the preset formula: , and obtain the stirring intensity compensation coefficient d; the stirring intensity compensation coefficient is used to adjust the speed h of the drive motor (7), h=h'×d, h' represents the current speed of the drive motor (7).

9. The homogenizing equipment for processing rubber asphalt according to claim 6, characterized in that: The parameters in the sensor data are analyzed with the preset standard values ​​to obtain the parameter abnormality evaluation value E corresponding to the sensor position. The specific process is as follows: According to the preset standard value of the corresponding parameter in the sensor data, the parameter in the sensor data is subtracted from its corresponding preset standard value and the absolute value is processed to obtain the standard deviation value CS1 corresponding to the parameter; the standard deviation value of the parameter is normalized and weighted with its statistical index, and the formula is: , get the parameter abnormality evaluation value E corresponding to the sensor position; where, They represent the weights corresponding to the standard deviation, mean, range and coefficient of variation of the parameters respectively.

10. The homogenizing equipment for processing rubber asphalt according to claim 6, characterized in that: The standard deviation of the parameters in the sensor data is analyzed in the time zone of the equipment homogenization processing to obtain the trend influence factor g corresponding to the parameter. The specific analysis is as follows: The startup time of the device is taken as the first time, the current time is taken as the second time, and the time zone between the first time and the second time is taken as the time zone for homogenizing the device; Calculate the standard deviation of the parameters at any acquisition time within the homogeneous processing time zone of the computing device and the calculation indicators of the parameter anomaly assessment value, including mean, trend value, and range value; The standard deviation value of the parameter and the calculation index corresponding to its parameter abnormality evaluation value are weightedly calculated to obtain the impact factor corresponding to the standard deviation value and the impact factor corresponding to the parameter abnormality evaluation value. The standard deviation value and the impact factor corresponding to the parameter abnormality evaluation value are weighted to obtain the trend impact factor g corresponding to the parameter.

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