Concrete mixing control system and method based on Internet of Things

Abstract

The invention belongs to the technical field of the Internet of Things and automatic control, and provides a concrete mixing control system and method based on the Internet of Things, and the system comprises a data collection unit which is used for obtaining key state data in the mixing process; the process parameter library is used for storing index parameter information according to a concrete mark-working condition parameter-process parameter three-level index structure; the intelligent control unit is used for receiving stirring parameter setting information input by a user, querying corresponding index parameter information from a process parameter library according to the stirring parameter setting information, and correcting the index parameter information based on the key state data to obtain to-be-executed parameters; and the execution unit is used for generating a first action execution signal to the corresponding target execution member according to the to-be-executed parameter, so that the target execution member executes a feeding and / or stirring action based on the first action execution signal. The scheme is enabled based on the Internet of Things, and the control precision, the control efficiency and the intelligent degree of the control link are improved.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things (IoT) and automatic control technology, and in particular to an IoT-based concrete mixing control system and method. Background Technology

[0002] In the concrete production industry, the mixing process is the core step that determines concrete quality and production efficiency. Traditional concrete mixing control systems largely rely on manual operation to complete material proportioning, speed adjustment, and process monitoring. For example, operators need to manually calculate the amount of raw materials such as sand, stone, and cement to be added based on the concrete grade, and adjust the mixing motor speed by observing the material status on-site. This manual control scheme not only consumes manpower and resources, but is also prone to insufficient accuracy in material proportioning due to human calculation errors and operational delays, leading to fluctuations in concrete performance.

[0003] While some existing mixing control systems are equipped with basic parameter storage functions, their parameter recall and adaptability to operating conditions are weak, and they cannot achieve dynamic correction. Traditional systems typically store fixed process parameters only based on the concrete grade, without considering the impact of relevant operating conditions on the mixing process. For example, in high-temperature and high-humidity environments, using the same water-cement ratio parameters as in normal-temperature environments can easily lead to concrete segregation; while in low-temperature environments, failing to extend the mixing time will result in uneven material mixing. These problems significantly reduce the concrete quality pass rate of traditional control systems under complex operating conditions, making it difficult to meet the construction requirements of different engineering scenarios.

[0004] In addition, the existing control system's sensor configuration is scattered, and it can only collect a small amount of data. Moreover, the data is not interconnected through the network and is mostly limited to local storage and simple display. It cannot provide support for parameter optimization, and thus it is difficult to reduce energy consumption by dynamically adjusting parameters. As a result, the stirring motor is often in an overloaded or inefficient operating state.

[0005] It is evident that existing control systems suffer from low control efficiency and accuracy, as well as insufficient intelligence. Summary of the Invention

[0006] This invention provides a concrete mixing control system and method based on the Internet of Things, which solves the defects of low control efficiency and accuracy and insufficient intelligence of existing control systems.

[0007] On one hand, the present invention provides an Internet of Things-based concrete mixing control system, comprising: The data acquisition unit is used to acquire key status data during the mixing process; The process parameter library is used to store index parameter information according to a three-level index structure of concrete grade, working condition parameters, and process parameters. The intelligent control unit is used to receive the stirring parameter setting information input by the user, query the corresponding index parameter information from the process parameter library according to the stirring parameter setting information, and correct the index parameter information based on the key status data to obtain the parameters to be executed. An execution unit is configured to generate a first action execution signal to a corresponding target actuator based on the parameters to be executed, so that the target actuator performs feeding and / or stirring actions based on the first action execution signal.

[0008] According to the IoT-based concrete mixing control system provided by the present invention, the intelligent control unit corrects the index parameter information based on the key status data to obtain the parameters to be executed, including: Set the parameter correction amount corresponding to different index parameter information under each key state data, and establish a state data-parameter correction amount lookup table; Based on the state data-parameter correction amount lookup table, determine the target parameter correction amount corresponding to the current key state data; The index parameter information is corrected according to the target parameter correction amount to obtain the parameters to be executed.

[0009] According to the Internet of Things-based concrete mixing control system provided by the present invention, the target actuators include a feeding conveyor and a mixing motor; The execution unit generates a first action execution signal to the corresponding target execution component based on the parameters to be executed, including: Determine the speed-flow rate calibration curve corresponding to the feeding conveyor; based on the target weight of each material in the parameters to be executed, calculate the required conveying speed and running time for each material according to the speed-flow rate calibration curve; and generate a first action execution signal to the feeding conveyor based on the conveying speed and running time; and / or, The actual rotational speed of the stirring motor is obtained, and the actual rotational speed is compared with the target rotational speed in the parameters to be executed to obtain the rotational speed deviation. If the rotational speed deviation exceeds a preset deviation threshold, the output frequency adjustment amount is determined by the PID algorithm, and a first action execution signal is generated to the stirring motor based on the output frequency adjustment amount.

[0010] The Internet of Things-based concrete mixing control system provided by the present invention further includes: a collaborative optimization unit; The collaborative optimization unit is used to receive the key state data and input the key state data into a pre-built data prediction model to obtain the uniformity prediction value and energy consumption prediction value for at least one future control period output by the data prediction model. Based on the uniformity prediction value and energy consumption prediction value, the unit performs optimization and solves to obtain the optimal control variable sequence for at least one future control period. The execution unit is also used to generate a second action execution signal to the corresponding target execution device based on the optimal control variable sequence.

[0011] According to the Internet of Things-based concrete mixing control system provided by the present invention, the data prediction model includes: A stirring mechanism model is used to characterize the internal mechanism relationship between the key state data during the stirring process and to obtain intermediate output quantities. A data-driven model is used to predict uniformity and energy consumption for at least one future control cycle based on the intermediate output.

[0012] According to the Internet of Things-based concrete mixing control system provided by the present invention, the data-driven model is obtained by training a long short-term memory network based on pre-built intermediate output samples, uniformity samples and energy consumption samples.

[0013] The Internet of Things-based concrete mixing control system provided by the present invention further includes: a fault diagnosis unit; The fault diagnosis unit is used to extract features from the key state data to obtain state feature parameters, and input the state feature parameters into a pre-established fault diagnosis model to obtain the fault diagnosis result output by the fault diagnosis model.

[0014] According to the Internet of Things-based concrete mixing control system provided by the present invention, the key status data includes: current data, temperature data, and vibration data of key components; The fault diagnosis unit is used to extract features from the key state data to obtain state feature parameters, including: The trend analysis of the current data and the temperature data is performed respectively to obtain the current trend parameters and the temperature trend parameters; Waveform analysis is performed on the vibration data to obtain waveform characteristic parameters; The current trend parameter, the temperature trend parameter, and the waveform characteristic parameter are used as state characteristic parameters.

[0015] The Internet of Things-based concrete mixing control system provided by the present invention further includes: a visualization unit; The visualization unit is used to generate graphical information based on the key status data and display the graphical information on the visualization interface.

[0016] On the other hand, the present invention also provides an Internet of Things (IoT) based concrete mixing control method, wherein the method comprises: Key status data during the stirring process are acquired through the data acquisition unit; The index parameter information is stored in the process parameter library according to a three-level index structure of concrete grade - working condition parameter - process parameter; The intelligent control unit receives the stirring parameter setting information input by the user, queries the corresponding index parameter information from the process parameter library based on the stirring parameter setting information, and corrects the index parameter information based on the key status data to obtain the parameters to be executed. The execution unit generates a first action execution signal to the corresponding target execution component based on the parameters to be executed, so that the target execution component performs feeding and / or stirring actions based on the first action execution signal.

[0017] The present invention provides an IoT-based concrete mixing control system and method, which acquires key status data during the mixing process through a data acquisition unit; a process parameter library stores index parameter information according to a three-level index structure of concrete grade-working condition parameters-process parameters; an intelligent control unit receives mixing parameter setting information input by the user, queries the corresponding index parameter information from the process parameter library based on the mixing parameter setting information, and corrects the index parameter information based on the key status data to obtain the parameters to be executed; and an execution unit generates a first action execution signal to the corresponding target actuator based on the parameters to be executed, so that the target actuator performs feeding and / or mixing actions based on the first action execution signal. This solution leverages the Internet of Things (IoT) to achieve efficient collection and transmission of critical status data, breaking the limitations of fragmented and isolated data in traditional systems. By utilizing a process parameter library built with a three-level index structure of concrete grade, operating condition parameters, and process parameters, combined with the intelligent control unit's dynamic correction of index parameter information based on critical status data, it effectively solves the problem of traditional systems storing fixed parameters in a single dimension and being unable to adapt to complex operating conditions, significantly improving the matching degree between process parameters and actual production scenarios. Furthermore, the execution unit precisely drives the target actuators to complete the feeding and mixing actions, forming an IoT closed-loop control process of data collection, parameter correction, and action execution. This not only reduces reliance on manual operation and lowers concrete quality fluctuations caused by human error, but also optimizes energy consumption in the mixing process based on IoT-enabled data analysis and dynamic control capabilities, improving the control precision, efficiency, and intelligence of the control process. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the structure of the Internet of Things-based concrete mixing control system provided in an embodiment of the present invention; Figure 2 This is a schematic flowchart of the Internet of Things-based concrete mixing control method provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] The following is combined with Figure 1 and Figure 2 This invention describes the detailed scheme of the Internet of Things-based concrete mixing control system and method provided in the embodiments of the present invention.

[0022] like Figure 1 As shown, the IoT-based concrete mixing control system provided in this embodiment of the invention mainly includes: The data acquisition unit 110 is used to acquire key status data during the stirring process.

[0023] The process parameter library 120 is used to store index parameter information according to a three-level index structure of concrete grade, working condition parameter, and process parameter.

[0024] The intelligent control unit 130 is used to receive the stirring parameter setting information input by the user, query the corresponding index parameter information from the process parameter library based on the stirring parameter setting information, and correct the index parameter information based on the key status data to obtain the parameters to be executed.

[0025] The execution unit 140 is used to generate a first action execution signal to the corresponding target execution component based on the parameters to be executed, so that the target execution component performs feeding and / or stirring actions based on the first action execution signal.

[0026] In this embodiment, the data acquisition unit 110 specifically includes: A current sensor is used to collect real-time current data during the operation of the mixing motor, monitor load changes, and determine whether the mixing motor is in an abnormal operating state such as overload or underload. In practical applications, a Hall current sensor can be used. Based on the Hall effect, when a current signal passes through the primary coil of the sensor, a magnetic field is generated in the iron core. Under the action of the magnetic field, the Hall element outputs a Hall current proportional to the primary current. After signal conditioning circuitry (filtering, amplification, and linearization), the analog signal is converted into a digital signal and transmitted to the intelligent control unit 130.

[0027] Temperature sensors are used to monitor the temperature data of key components of the mixing motor, such as the stator windings, bearings, and gearbox lubricating oil, in real time. This prevents malfunctions caused by excessive temperature, such as motor insulation damage, accelerated bearing wear, and poor gear meshing in the gearbox, ensuring stable equipment operation. In practical applications, a PT100 platinum resistance temperature sensor can be selected. Its resistance value changes linearly with temperature; the higher the temperature, the greater the resistance value. The temperature sensor is connected to a temperature acquisition module, which provides a constant current to the PT100 through a constant current source circuit, measures the voltage drop across the PT100, calculates the corresponding temperature value based on the relationship between the voltage and resistance values, and then transmits the temperature data to the intelligent control unit 130.

[0028] Vibration sensors are used to collect vibration data from key components such as the mixing motor, gearbox, and mixing drum. By analyzing parameters such as vibration frequency and amplitude, abnormalities such as imbalance, misalignment, bearing failure, and gear wear can be identified, providing timely warnings of potential faults. Piezoelectric vibration sensors can be used, with a piezoelectric crystal at their core. When the sensor is subjected to vibration, the piezoelectric crystal deforms mechanically, generating an electric charge. This charge is converted into a voltage signal by a charge amplifier, then filtered and amplified by a signal conditioning circuit, converting the vibration signal into a digital signal and transmitting it to the intelligent control unit 130.

[0029] In some embodiments, the data acquisition unit 110 may further include a material status sensor, specifically an ultrasonic sensor and a weight sensor. The ultrasonic sensor is used to detect the height of the material in the mixing drum to determine whether the material is sufficient or overflowing. The weight sensor is used to measure the weight of the material in the mixing drum to accurately control the material ratio, ensure that the concrete quality meets the requirements, and avoid overloading the motor or affecting the mixing efficiency due to too much material or too little material.

[0030] Specifically, the ultrasonic sensor consists of a transmitter and a receiver. The transmitter emits ultrasonic signals to the surface of the material inside the mixing drum. The ultrasonic waves are reflected after encountering the material surface, and the receiver receives the reflected signals. Based on the time difference between the emission and reception of the ultrasonic waves, combined with the speed of ultrasonic wave propagation in the air, the distance between the sensor and the material surface is calculated, and thus the height of the material is obtained.

[0031] The weight sensor is a resistance strain gauge type weight sensor, which is installed on the support structure of the mixing drum. When the material enters the mixing drum, the weight sensor is subjected to pressure and undergoes mechanical deformation. The strain gauge attached to the elastic body of the sensor deforms accordingly, causing the resistance value of the strain gauge to change. The resistance change is converted into a voltage change through a Wheatstone bridge. After signal amplification and filtering, the weight signal is transmitted to the intelligent control unit 130.

[0032] In this embodiment, the intelligent control unit 130 is specifically implemented by multiple edge computing nodes and at least one management server. The multiple edge computing nodes can be deployed close to the location of each sensor in the data acquisition unit 110 and connected to the data acquisition unit 110 via Ethernet. The multiple edge computing nodes can be connected to the management server through remote communication, specifically using a hybrid communication method of 4G / 5G and NB-IoT.

[0033] In this embodiment, the process parameter library 120 can store data using a three-level index structure of concrete grade, working condition parameters, and process parameters. The first level is the concrete grade, such as C25, C30, and C40, corresponding to different engineering strength requirements; the second level is the working condition parameters, such as normal temperature drying, high temperature and high humidity, and low temperature construction, adapting to the influence of different environments on mixing; the third level is the specific process parameters, including material proportioning parameters, mixing process parameters, and feeding sequence parameters.

[0034] The material proportioning parameters include the mass ratio of cement, sand, stone, water, and admixtures, which can be accurate to 0.01. The mixing process parameters include the mixing speed range, total mixing time, and staged speed switching nodes, such as low speed of 120 r / min for the first 30 seconds, high speed of 180 r / min for the middle 60 seconds, and medium speed of 150 r / min for the last 30 seconds. The feeding sequence parameters include the feeding interval time and the feeding time of each material, such as adding 50% sand (10 s) → adding cement (8 s) → adding the remaining 50% sand (10 s) → adding stone (15 s) → adding water (12 s) → adding admixtures (5 s).

[0035] Users input mixing parameter settings into the intelligent control unit. For example, they can select the target concrete grade and current working conditions through the human-machine interface. The intelligent control unit then uses an SQL query to match the corresponding parameter group, i.e., the indexed parameter information. Simultaneously, the intelligent control unit will call key status data to correct the indexed parameter information and obtain the parameters to be executed.

[0036] In one embodiment, the intelligent control unit corrects the index parameter information based on key state data to obtain the parameters to be executed, specifically including: First, set the parameter correction amount corresponding to different index parameter information under each key state data, and establish a state data-parameter correction amount lookup table.

[0037] In this embodiment, the parameter correction amount is mainly based on the temperature data in the key state data. For example, under high temperature conditions, the parameter correction amount can be set as follows: water-cement ratio reduced by 0.02, stirring time extended by 15s.

[0038] Then, based on the state data-parameter correction amount lookup table, determine the target parameter correction amount corresponding to the current key state data.

[0039] Finally, the index parameter information is corrected according to the target parameter correction amount to obtain the parameters to be executed.

[0040] In practical applications, the parameters to be executed can be sent to the execution unit in JSON format via the MQTT protocol.

[0041] In one embodiment, the target actuators include a feeding conveyor and a stirring motor.

[0042] The execution unit generates a first action execution signal to the corresponding target executor based on the parameters to be executed, including: Determine the speed-flow rate calibration curve corresponding to the feeding conveyor; based on the target weight of each material in the parameters to be executed, calculate the required conveying speed and running time for each material according to the speed-flow rate calibration curve; and generate a first action execution signal to the feeding conveyor based on the conveying speed and running time; and / or, The actual speed of the stirring motor is obtained, and the actual speed is compared with the target speed in the parameters to be executed to obtain the speed deviation. If the speed deviation exceeds the preset deviation threshold, the output frequency adjustment is determined by the PID algorithm, and the first action execution signal is generated to the stirring motor based on the output frequency adjustment.

[0043] On one hand, the feeding conveyor refers to the screw conveyor or belt conveyor of the automatic feeding device. These conveyors are equipped with servo motors, and their speed-flow calibration curve can be obtained in advance through experiments. For example, the speed of the sand feeding conveyor is 100 r / min, corresponding to a flow rate of 20 kg / s. The PLC in the execution unit can calculate the required conveying speed and running time for each material based on the target weight of each material in the parameters to be executed and the speed-flow calibration curve of the feeding conveyor. For example, if the target sand quantity is 500 kg and the sand conveying flow rate is 20 kg / s, the running time is 25 seconds. Subsequently, the PLC controls the servo motor to run at 100 r / min for 25 seconds. Simultaneously, the weight sensor below the mixing drum can collect the material weight in real time. When the actual weight reaches 95% of the target weight, the PLC controls the feeding conveyor speed to decrease to 50% (i.e., 50 r / min), entering the fine feeding stage to avoid overfeeding due to inertia. When the actual weight reaches ±0.5% of the target weight, the solenoid valve is immediately closed, stopping the feeding.

[0044] On the other hand, the inverter of the mixing motor provides real-time feedback on the current speed. The PLC compares the actual speed with the target speed in the parameters to be executed. If the deviation exceeds a preset deviation threshold, such as ±2 r / min, the output frequency of the inverter is adjusted through a PID algorithm. For example, in the PID parameters, the proportional coefficient Kp = 2.5, the integral coefficient Ki = 0.1, and the derivative coefficient Kd = 0.05. The formula for calculating the adjustment amount of the inverter's output frequency is: Δf = Kp×(Vm -Vi) + Ki×∫(Vm -Vi) dt + Kd×d (Vm -Vi) / dt (1) Where Δf represents the output frequency adjustment, Vm represents the target speed, and Vi represents the actual speed.

[0045] For example, if the actual speed is 3 r / min lower than the target speed and the deviation lasts for 2 seconds, the integral term accumulates to 0.6 and the derivative term is 0. Then, Δf = 2.5 × 3 + 0.1 × 0.6 + 0.05 × 0 = 7.56 Hz. The inverter's output frequency increases from the original 50 Hz to 57.56 Hz, causing the motor speed to return to the target value.

[0046] In one embodiment, the above-mentioned IoT-based concrete mixing control system may further include: a collaborative optimization unit.

[0047] The collaborative optimization unit is used to receive key state data and input the key state data into a pre-built data prediction model to obtain the uniformity prediction value and energy consumption prediction value for at least one future control period output by the data prediction model. Based on the uniformity prediction value and energy consumption prediction value, the unit performs optimization to obtain the optimal control variable sequence for at least one future control period.

[0048] The execution unit is also used to generate a second action execution signal to the corresponding target execution unit based on the optimal control variable sequence.

[0049] In a specific implementation, the data prediction model includes: The stirring mechanism model is used to characterize the internal mechanism relationship between key state data during the stirring process and obtain intermediate output quantities.

[0050] A data-driven model is used to predict uniformity and energy consumption for at least one future control cycle based on intermediate output values.

[0051] In this embodiment, the data prediction model is constructed by fusing a stirring mechanism model and a data-driven model. First, a system of mechanistic equations for the stirring process can be established based on fluid mechanics and thermodynamics theories. Taking the impact of material viscosity changes on motor load as an example, the following mechanistic equations can be constructed: I = a × μ × n + b (2) Where I represents the motor load, μ represents the material viscosity, and a and b are both mechanism coefficients, which are strongly correlated with the equipment structural parameters and process characteristics.

[0052] In the process of constructing the mechanism equation, it is necessary to comprehensively consider key factors such as the geometry of the stirring blade, the intensity of fluid turbulence, and the heat transfer efficiency, so as to form a stirring mechanism model that includes the heat transfer equation, the momentum equation, and the mass conservation equation, which are coupled with multiple physical fields.

[0053] Then, systematic data collection was carried out, covering different material grades (e.g., grades A, B, and C correspond to significantly different material characteristics), complex operating conditions (high temperature and high pressure, low temperature and low pressure, normal operating conditions, etc.), and equipment health status (new equipment, normally operating equipment, and potentially faulty equipment), accumulating multiple sets of historical operating data. Through data preprocessing, outlier removal, missing value imputation, and normalization were performed to ensure data quality.

[0054] In this embodiment, the data-driven model is obtained by training the Long Short-Term Memory network based on pre-built intermediate output samples, uniformity samples, and energy consumption samples.

[0055] In the data-driven model construction phase, this embodiment uses a Long Short-Term Memory (LSTM) network as the core architecture. An input layer with multiple neurons is set up, corresponding to material temperature, pressure, flow rate, viscosity, motor speed, and equipment vibration values, respectively. Three hidden layers, each containing 128 neurons, are set up, and the stacked structure enhances the model's feature extraction capability from time-series data. An output layer with two neurons is set up to predict uniformity and energy consumption values, respectively. In practical applications, the output of the stirring mechanism model can be used as the initial input of the data-driven model. Iterative training is performed using the Adam optimizer with a learning rate of 0.001. A dynamic learning rate adjustment strategy is used; when the validation set error decreases by less than 0.1% for five consecutive epochs, the learning rate is reduced to 0.9 times its original value. After multiple rounds of iterative training, the data-driven model is obtained.

[0056] In the optimization solution process, a weighted objective function needs to be constructed with the dual objectives of maximizing concrete uniformity and minimizing energy consumption, as follows: J =ω1×(100 - U_pred) +ω2×E_pred (3) Wherein, ω1 and ω2 are weighting coefficients that can be dynamically adjusted according to project needs. For example, when key projects have high quality requirements, ω1=0.7 and ω2=0.3; when ordinary projects pursue energy conservation, ω1=0.4 and ω2=0.6; U_pred is the predicted value of uniformity and E_pred is the predicted value of energy consumption. In the constraint setting stage, hard constraints and soft constraints can be set. Hard constraints include: motor temperature T≤120℃ (exceeding this will cause insulation damage), gearbox oil temperature T_oil≤90℃ (exceeding this will cause lubricating oil failure), motor current I≤1.2×rated current (180A, to avoid overload), and stirring speed n∈[100,200] r / min (below 100r / min will result in uneven stirring, and above 200r / min will result in excessive equipment vibration); soft constraints include: material viscosity μ∈[500,2000]Pa・s (outside this range, the feeding ratio needs to be adjusted), and uniformity U≥90% (below this range, the stirring time needs to be extended). In the rolling optimization execution phase, the optimization solution process can be executed according to the following steps: Data acquisition: Real-time values ​​of multiple state variables are acquired during each control cycle.

[0057] Predictive calculation: Input the real-time values ​​of the state variables into the data prediction model, and output the uniformity prediction value U_pred and the energy consumption prediction value E_pred for at least one future period.

[0058] Optimization solution: The interior point method is used to solve for the minimum value of the constrained objective function J, and the optimal sequence of control variables for future control cycles is obtained, including the stirring speed n1-n5 and the fine-tuning value of the feed amount Δm1-Δm5.

[0059] Command issuance: Only the control variables (n1, Δm1) of the first cycle are issued to the PLC to control the action of the target actuator, such as adjusting the speed of the frequency converter to n1, and fine-tuning the feeding amount Δm1 of the next batch of materials.

[0060] Rolling update: Enter the next control cycle and repeat the above steps. Based on the new state variables, predict and solve again to realize the rolling cycle of prediction-optimization-execution-feedback, ensuring that the system is always in the optimal state.

[0061] For example, when the motor temperature T=115℃ is detected, which is close to the hard constraint of 120℃, under the premise of ensuring U≥90%, the speed of the next 5 control cycles can be reduced to 170r / min, 165r / min, 160r / min, 155r / min and 150r / min respectively. At the same time, the feeding amount is reduced by 5kg to reduce the motor load and allow the temperature to gradually drop back to the safe range.

[0062] In one embodiment, the above-mentioned IoT-based concrete mixing control system may further include a fault diagnosis unit.

[0063] The fault diagnosis unit is used to extract features from key state data, obtain state feature parameters, and input the state feature parameters into a pre-established fault diagnosis model to obtain the fault diagnosis results output by the fault diagnosis model.

[0064] In this embodiment, a lightweight fault diagnosis model can be set up, such as a CNN-based fault diagnosis model, to analyze the real-time extracted state feature parameters. If an abnormality is found in the equipment, an alarm message is immediately generated and the fault information is quickly fed back to the PLC to drive the actuator to take emergency measures, such as reducing the motor speed or stopping the machine.

[0065] In practical applications, alarm information including fault type, fault time, fault location, and fault parameter values ​​can be sent to users via pop-ups, SMS, emails, etc. At the same time, all operating data and fault information are stored in a historical database. Users can query historical data by time range, equipment number, fault type, etc., and can also generate data reports such as daily, weekly, and monthly reports to facilitate the analysis of equipment operating patterns and fault causes.

[0066] In one embodiment, the key status data includes: current data, temperature data, and vibration data of key components.

[0067] The fault diagnosis unit is used to extract features from key state data to obtain state feature parameters, specifically including: On the one hand, trend analysis was performed on the current data and temperature data respectively to obtain current trend parameters and temperature trend parameters.

[0068] For time-series data such as temperature and current, trend analysis can be performed using moving average and exponential smoothing methods. By setting dynamic thresholds and comparing the trends of real-time and historical data, an early warning mechanism is immediately triggered if the temperature curve shows a continuous sharp increase or the current value frequently exceeds the normal fluctuation range. Combined with the equipment operating status, the system determines whether the data trend is normal, providing a reliable basis for equipment fault diagnosis.

[0069] On the other hand, waveform analysis is performed on the vibration data to obtain waveform characteristic parameters.

[0070] In this embodiment, vibration data can be processed in depth using Fast Fourier Transform and wavelet analysis. By converting the time-domain signal into a frequency-domain signal, the distribution of vibration energy at different frequencies can be visually presented, accurately capturing the characteristic frequencies of abnormal vibrations in the equipment. Wavelet analysis utilizes multi-resolution characteristics to perform localized analysis of the signal in the time-frequency domain, making it suitable for extracting transient features of non-stationary vibration signals. After processing, waveform characteristic parameters such as vibration frequency, amplitude, and harmonic components can be extracted.

[0071] Finally, the current trend parameter, temperature trend parameter, and waveform characteristic parameter are used as state characteristic parameters.

[0072] In one embodiment, the above-mentioned IoT-based concrete mixing control system may further include a visualization unit.

[0073] The visualization unit is used to generate graphical information based on key status data and display the graphical information on the visualization interface.

[0074] In practical applications, data can be graphically displayed using the ECharts chart library, such as current trend curves, real-time temperature dashboards, and vibration spectrum diagrams, making it easier for users to intuitively understand the concrete mixing status.

[0075] Based on the same general inventive concept, this invention also protects an Internet of Things (IoT) based concrete mixing control method. The IoT-based concrete mixing control method provided by this invention will be described below. The IoT-based concrete mixing control method described below can be referred to in correspondence with the IoT-based concrete mixing control system described above.

[0076] like Figure 2As shown, the IoT-based concrete mixing control method provided in this embodiment of the invention is implemented based on the IoT-based concrete mixing control system provided in the above embodiments. The method specifically includes the following steps: Step 210: Acquire key status data during the stirring process through the data acquisition unit.

[0077] Step 220: Store index parameter information in the process parameter library according to the three-level index structure of concrete grade - working condition parameter - process parameter.

[0078] Step 230: Receive the stirring parameter setting information input by the user through the intelligent control unit, query the corresponding index parameter information from the process parameter library based on the stirring parameter setting information, and correct the index parameter information based on the key status data to obtain the parameters to be executed.

[0079] Step 240: The execution unit generates a first action execution signal to the corresponding target execution component based on the parameters to be executed, so that the target execution component performs feeding and / or stirring actions based on the first action execution signal.

[0080] The specific implementation of each step in the methods described in the above embodiments has been described in detail in the embodiments of the relevant systems, and will not be elaborated further here.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A concrete mixing control system based on the Internet of Things, characterized in that, include: The data acquisition unit is used to acquire key status data during the mixing process; The process parameter library is used to store index parameter information according to a three-level index structure of concrete grade, working condition parameters, and process parameters. The intelligent control unit is used to receive the stirring parameter setting information input by the user, query the corresponding index parameter information from the process parameter library according to the stirring parameter setting information, and correct the index parameter information based on the key status data to obtain the parameters to be executed. An execution unit is configured to generate a first action execution signal to a corresponding target actuator based on the parameters to be executed, so that the target actuator performs feeding and / or stirring actions based on the first action execution signal.

2. The IoT-based concrete mixing control system according to claim 1, characterized in that, The intelligent control unit corrects the index parameter information based on the key status data to obtain the parameters to be executed, including: Set the parameter correction amount corresponding to different index parameter information under each key state data, and establish a state data-parameter correction amount lookup table; Based on the state data-parameter correction amount lookup table, determine the target parameter correction amount corresponding to the current key state data; The index parameter information is corrected according to the target parameter correction amount to obtain the parameters to be executed.

3. The IoT-based concrete mixing control system according to claim 1, characterized in that, The target actuators include a feeding conveyor and a stirring motor; The execution unit generates a first action execution signal to the corresponding target execution component based on the parameters to be executed, including: Determine the speed-flow rate calibration curve corresponding to the feeding conveyor; based on the target weight of each material in the parameters to be executed, calculate the required conveying speed and running time for each material according to the speed-flow rate calibration curve; and generate a first action execution signal to the feeding conveyor based on the conveying speed and running time; and / or, The actual rotational speed of the stirring motor is obtained, and the actual rotational speed is compared with the target rotational speed in the parameters to be executed to obtain the rotational speed deviation. If the rotational speed deviation exceeds a preset deviation threshold, the output frequency adjustment amount is determined by the PID algorithm, and a first action execution signal is generated to the stirring motor based on the output frequency adjustment amount.

4. The IoT-based concrete mixing control system according to claim 1, characterized in that, The system also includes: a collaborative optimization unit; The collaborative optimization unit is used to receive the key state data and input the key state data into a pre-built data prediction model to obtain the uniformity prediction value and energy consumption prediction value for at least one future control period output by the data prediction model. Based on the uniformity prediction value and energy consumption prediction value, the unit performs optimization and solves to obtain the optimal control variable sequence for at least one future control period. The execution unit is also used to generate a second action execution signal to the corresponding target execution device based on the optimal control variable sequence.

5. The IoT-based concrete mixing control system according to claim 4, characterized in that, The data prediction model includes: A stirring mechanism model is used to characterize the internal mechanism relationship between the key state data during the stirring process and to obtain intermediate output quantities. A data-driven model is used to predict uniformity and energy consumption for at least one future control cycle based on the intermediate output.

6. The IoT-based concrete mixing control system according to claim 5, characterized in that, The data-driven model is obtained by training the Long Short-Term Memory network based on pre-built intermediate output samples, uniformity samples, and energy consumption samples.

7. The IoT-based concrete mixing control system according to claim 1, characterized in that, The system also includes: a fault diagnosis unit; The fault diagnosis unit is used to extract features from the key state data to obtain state feature parameters, and input the state feature parameters into a pre-established fault diagnosis model to obtain the fault diagnosis result output by the fault diagnosis model.

8. The IoT-based concrete mixing control system according to claim 7, characterized in that, The key status data includes: current data, temperature data, and vibration data of key components; The fault diagnosis unit is used to extract features from the key state data to obtain state feature parameters, including: The trend analysis of the current data and the temperature data is performed respectively to obtain the current trend parameters and the temperature trend parameters; Waveform analysis is performed on the vibration data to obtain waveform characteristic parameters; The current trend parameter, the temperature trend parameter, and the waveform characteristic parameter are used as state characteristic parameters.

9. The IoT-based concrete mixing control system according to claim 1, characterized in that, The system also includes: a visualization unit; The visualization unit is used to generate graphical information based on the key status data and display the graphical information on the visualization interface.

10. A method for controlling concrete mixing based on the Internet of Things, characterized in that, Based on the IoT-based concrete mixing control system as described in any one of claims 1 to 9, the method includes: Key status data during the stirring process are acquired through the data acquisition unit; The index parameter information is stored in the process parameter library according to a three-level index structure of concrete grade - working condition parameter - process parameter; The intelligent control unit receives the stirring parameter setting information input by the user, queries the corresponding index parameter information from the process parameter library based on the stirring parameter setting information, and corrects the index parameter information based on the key status data to obtain the parameters to be executed. The execution unit generates a first action execution signal to the corresponding target execution component based on the parameters to be executed, so that the target execution component performs feeding and / or stirring actions based on the first action execution signal.

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